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.
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.
Temporal provides helpful primitives called Workflows and Activities for orchestrating processes. A common pattern I've found useful is the ability to run multiple "child workflows" in parallel from a single "parent" workflow.
I spent some time experimenting with multi-modal model (also called vision models on the ollama site) to see how they perform. You try these out with the CLI ollama run <model> but I opted to use the ollama Python client.
Similar to (and perhaps more simply than) analyzing Youtube video transcripts with language models, I wanted to apply a similar approach to webpages like articles, primarily for the purposes of determining the subject content of lengthy pieces and experimenting to see if this is useful at all.
You can download a Youtube video transcript with yt-dlp.