I set out to do a project using my learnings from the first chapter of the fast.ai course. My first idea was to try and train a Ruby/Python classifier. ResNets are not designed to do this, but I was curious how well it would perform.
I've enjoyed using fasthtml to deploy small, easily hosted webpages for little apps I've been building. I'm still getting used to it but it almost no effort at all to deploy. Recently, I built an app that would benefit from having a loading spinner upon submitting a form, but I couldn't quite...
I revisited Eugene's excellent work, "Prompting Fundamentals and How to Apply Them Effectively". From this I learned about the ability to prefill Claude's responses. Using this technique, you can quickly get Claude to output JSON without any negotiation and avoid issues with leading codefences...
One challenge I've continued to have is figuring out how to use the models on Huggingface. There are usually Python snippets to "run" models that often seem to require GPUs and always seem to run into some sort of issues when trying to install the various Python dependencies. Today, I learned how...
I've been experimenting with FastHTML for making quick demo apps, often involving language models. It's a pretty simple but powerful framework, which allows me to deploy a client and server in a single main.py -- something I appreciate a lot for little projects I want to ship quickly. I currently...
I spent a bit of time configuring WezTerm to my liking. This exercise was similar to rebuilding my iTerm setup in Alacritty. I found WezTerm to be more accessible and strongly appreciated the builtin terminal multiplexing because I don't like using tmux.
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...
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...
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.
In Python, the most straightforward path to implementing a gRPC server for a Protobuf service is to use protoc to generate code that can be imported in a server, which then defines the service logic.