Today, I ran into an issue where I wanted to use repomix to pack a large codebase into a single file to pass to an LLM, but I couldn’t paste the output into any of the UIs I typically use. The React apps all became sluggish as I waited for ~500,000 tokens to paste.

Enter llm.

I solved this problem with a bash one-liner:

llm -m gemini-2.5-pro-preview-05-06 "The provided context is all the code of an old codebase. Analyze this code and come up with high impact, meaningful improvements to make the codebase easier to work with." < repomix.output

The API and subsequent inference took about 85 seconds, and I had my response.

These days I use agents that write code often. When I am trying to build a new feature, I first write a markdown spec, then point the agent at it and send it on its way.

There are a lot of tools and choices today in the agent space. I regularly use 3-4 different ones, and I expect that number to continue to vary.

When you send an agent off to write code, you need to wait. If you have more work to do, especially work that is unrelated to the current changes the agent is making, it would be nice to unblock that work as well.

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.

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.

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.

Today, I set out to add an llms.txt to this site. I’ve made a few similar additions in the past with raw post markdown files and a search index. Every time I try and change something with outputFormats in Hugo, I forget one of the steps, so in writing this up, finally I’ll have it for next time.

Steps

First, I added a new output format in my config.toml file:

[outputFormats.TXT]
mediaType = "text/plain"
baseName = "llms"
isPlainText = true

Then, I added this format to my home outputs:

Today, Anthropic entered the LLM code tools party with Claude Code.

Coding with LLMs is one of my favorite activities these days, so I’m excited to give it a shot. As a CLI tool, it seems most similar to aider and goose, at least of the projects I am familiar with.

Be forewarned, agentic coding tools like Claude Code use a lot of tokens which are not free. Monitor your usage carefully as you use it or know you may spend more than you expect.

An LLM stop sequence is a sequence of tokens that tells the LLM to stop generating text. I previously wrote about stop sequences and prefilling responses with Claude.

As a reference, here’s how to use a stop sequence with the OpenAI API in Python

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
  model="gpt-4o",
  messages=[{"role": "user", "content": "What is the capital of France?"}],
  stop=["Paris"],
)
print(response.choices[0].message.content)

which outputs something like

'The capital of France is '

Notice the LLM never outputs the word “Paris”. This is due to the stop sequence.