I definitely felt I think writing was a much more angsty process for me in the past. Maybe I’ve just gotten better at it over time or just found my own coping skills. I wouldn’t attribute all this to LLMs, but I think having a partner now, it’s like I actually don’t feel like I’m solitary anymore.
When I’m getting stuck on stuff, I actually have someone I can talk to and whatever I throw at them, I don’t have to give them all the content. I can just throw things and we go back and forth. Just that of not being a solo writer anymore, but having a collaborator is I think a really huge change for a lot of people.
We need to understand that things that were considered easy tasks will no longer exist. And what was considered hard tasks will be the new easy.
And what was considered to be impossible tasks are going to be the new hard.
From The Cognitive Revolution | AI Builders, Researchers, and Live Player Analysis: Fiverr Goes All-In on AI: Empowering Creators, Not Replacing Them, with Micha Kaufman, CEO of Fiverr, Apr 26, 2025
I’ve said this before, but as the feature set of LLM APIs increases, it becomes harder and harder to not just pick a provider and using their SDK for your use case. Between tool calling, thinking, streaming, citations, pdf parsing, and more, there is no consistency in the API design across providers.
This feels like OpenRouter’s moat. They figure out a way to abstract this as much as possible across model APIs and then I just use them. LiteLLM as well though I need experiment with it more.
When building with agents, prompting the agent to
Only do X. Do nothing else.
seems to be required to prevent the agent from being overly eager with its implementation, trying to anticipate what you will want next. However, the agents are often not reliable enough to do all this work in a single tool loop, so forcing them to keep their scope small is still necessary babysitting.
It’s hard to make some simple shit that’s great
Writing software is weird now. Most engineers I know are using LLM-assisted coding tools in some capacity. We can debate the pros and cons of whether this practice is a good idea and the impact it’s going to have on us and the software create, but for now, it is a reality.
The hard parts remain
The parts of software engineering that have gone away are the straightforward parts. The parts where you scaffold a new CRUD endpoint, add a new database table, or write a mapper between an external and internal representation of some data. The LLM is usually done with about as fast and you can point it at the problem. So what does that leave?
It seems OpenAI’s naming scheme is now following Apple’s iPod naming scheme
Today, we’re launching three new models in the API: GPT‑4.1, GPT‑4.1 mini, and GPT‑4.1 nano.
https://openai.com/index/gpt-4-1/
The OpenAI playground has been down for me all day with a Cloudflare error.
I am working on a macOS app, built in Swift and called Hudson. I’ve been inspired by the UX of Chorus and the simplicity of Ollamac. My goal is to build an app that allows me to quickly send prompts to API or local LLMs. I have most of the core functionality working. What’s next is to build something like Chorus’ ambient chat UI.
Having an LLM prompt available via a global hotkey across any model and where my conversation history is stored locally feels like it will address several of my needs. It’s not that a similar app doesn’t exist, it’s that I know I will want to tweak the UX exactly to my liking. With LLMs being so good at generating code, this is now a project that will take a week or two rather than a month or two.
It’s been a little while since I worked at all on Delta but I still have the itch to have access to the source of the tool that becomes my daily LLM driver. I recently setup Chorus which advertises itself as a tool to chat with multiple models at once. This isn’t really a main use case for me but besides not being open source, it’s basically everything I want.
- Clean interface
- Text streaming
- Use my own API keys
- Local model support (ollama, LM Studio)
- Broad model API support
- Mini-mode triggered by a hotkey
- Conversation history
- Image support
I don’t love that their cloud is involved with the service. I understand this is required for them to make money but I can’t see myself using a cloud product for this in the long term, especially for sending images of my system. I feel like there is too much potential for things to go wrong.
OpenAI shipped a new version of 4o that can generate images. I tried generating images in ChatGPT and it popped open a side chat and edit tool I hadn’t seen before. I got rate limited before I could try much.
I read this paper on using “chain of draft”, a more compressed version of chain of thought to achieve similar results to chain of thought reasoning. The main unlock with this approach is a reduction in inference latency and cost due to few tokens needed to achieve similar performance.