I made Ethos, an open-source tool to visualize the discourse on Hacker News. It extracts entities, tracks sentiment, and groups discussions by concept.

Check it out: https://ethos.devrupt.io

This was a "budget build" experiment. I managed to ship it for under $1 in infra costs. Originally I was using `qwen3-8b` for the LLM and `qwen3-embedding-8b` for the embedding, but I ran into some capacity issues with that model and decided to use `llama-3.1-8b-instruct` to stay within a similar budget while having higher throughput.

What LLM or embedding would you have used within the same price range? It would need to be a model that supports structured output.

How bad do you think it is that `llama-3.1` is being used and then a higher dimension embedding? I originally wanted to keep the LLM and embedding within the same family, but I'm not sure if there is munch point in that.

Repo: https://github.com/devrupt-io/ethos

I'm looking for feedback on which metrics (sentiment vs. concepts) you find most interesting! PRs welcome!