Model Context Protocol servers have become a popular way to expose software services for LLMs. Prior to this post, I'd not spent nearly enough time with them to have a strong opinion of their pros and cons, so I set out to change that.
It started because I was using the OpenAI completion API to try several different models while building Tomo.
I was working with Amazon Bedrock to run LLM inference. AWS has its fair share of complexity -- VPCs, subnets, security groups, etc.
Lots of language model providers implement the OpenAI API spec. These look similar in shape but often behave differently in subtle ways. Anthropic's prefill sequences are one such example.
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.
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.
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.
Today, Anthropic entered the LLM code tools party with Claude Code.
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.
I built an Astro component called CodeToggle.astro for my experimental site. The idea was to create a simple wrapper around a React (or other interactive component) in an MDX file so that the source of that rendered component could be nicely displayed as a highlighted code block on the click of a...