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 figure out how I would do that with htmx in FastHTML, so I built a small project to experiment with various approaches.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 (e.g. ```json).
While JSON isn’t as good an example as XML, which ends less ambiguously, here’s a quick script showing the concept:
import anthropic message = anthropic.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 to run model inference on a Mac with an M-series chip using llama-cpp and a gguf file built from safetensors files on Huggingface.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 use it how you might use streamlit.
I ran into an issue where I was struggling to submit a form with multiple images.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 configured WezTerm to provide the following experience. Getting this working probably took me 30 minutes spread across a few sessions as I noticed things I was missing.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.
Let’s take a simple example Protobuf service:
syntax = "proto3"; package simple; message HelloRequest { string name = 1; } message HelloResponse { string message = 1; } service Greeter { rpc SayHello (HelloRequest) returns (HelloResponse); } Next, we run some variant of python -m grpc_tools.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.
Let’s say we have the following activity and workflow (imports omitted for brevity)
Activity code
@dataclass class MyGoodActivityArgs: arg1: str arg2: str @dataclass class MyGoodActivityResult: arg1: str arg2: str random_val: float @activity.defn async def my_good_activity(args: MyGoodActivityArgs) -> MyGoodActivityResult: activity.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.
I didn’t find explicit documentation in the README on how to pass images to the model but the type hints in the code made it pretty easy to figure out and there are several examples around Github.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.
The html2text script is good at extracting content from html. When combined with a few other CLIs, we can prompt the language model to create a summary for the cleaned HTML page.You can download a Youtube video transcript with yt-dlp.
yt-dlp --write-auto-sub --skip-download --sub-format vtt --output transcript "<video_url>" This will output a file called transcript.en.vtt. That file can be cleaned like this, to remove all formatting and metadata except the transcript text.
cat transcript.en.vtt | grep : -v | awk '!seen[$0]++' This approach is useful for simple way to pipe the contents of a Youtube video into an LLM, my motivation for finding a way to accomplish this task.