Hey everyone,
I've been working on NewsGator, an open-source project I'd like to share with you.
The problem
You follow 50 RSS feeds in 5 different languages, get 12 newsletters a day, and end up reading nothing because sorting through it all takes too much time. Traditional aggregators give you 3-line teasers, you click through, find 10 articles about the same thing, and you've wasted 20 minutes.
What NewsGator does
- Ingests your RSS/ATOM feeds and keeps everything, even after restart;
- Fetches full article content — not just the teaser, the actual text with images;
- Summarizes and generates an honest title in your language, regardless of the source language;
- Groups articles by event: 10 articles about the same story = 1 "Story" with a summary that evolves as new facts come in;
- Swipe interface on mobile (installable PWA);
- Transparency dashboard: token cost and LLM latency visible in real time.
Stack: FastAPI + SQLite (+ sqlite-vec or Qdrant) + SvelteKit, single docker compose.
> 🔗 Demo & docs: https://tekka90.github.io/NewsGator/
> 🔗 GitHub: https://github.com/Tekka90/NewsGator
⚠️ Warning: this project uses AI
I want to be upfront about this:
- Summaries can be wrong or biased. An LLM is not a source of truth. NewsGator is a triage tool, not an information source. You trust the summary to decide whether it's worth digging into, then you verify at the source.
- I strongly recommend running the LLM locally. NewsGator is provider-agnostic: any OpenAI-compatible LLM works — Ollama, llama.cpp, LM Studio, MLX, or a paid API if you prefer. But ideally, run a model locally. No leaking your reading habits, no recurring costs, no dependency on an external service.
- A small local model is enough for summarizing articles — you don't need a giant model.
- If you do go with a paid API, everything is measured (tokens + latency per call) with a "price playground" to estimate costs.
The project is open source — feel free to ask questions, share feedback, or contribute.
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