Hundreds of papers hit arXiv every day and maybe 3 matter to my research, so I built an open-source tool that finds them [P]

Left: Telegram digest (optional); Right: detailed digest on HTML

Like probably everyone here, my to-read list only grows. Skimming arXiv listings or my feeds takes 30-60 minutes a day, 95% of it is irrelevant to what I actually work on, and newsletters don't really help: they surface what's popular, not what's relevant to my research.

So I built Research Radar, a daily cron job that:

1. Fetches every new paper in your arXiv categories (RSS + API, deduped)
2. Scores every abstract 1-10 against a markdown file describing your research interests (cheap model, batched)
3. Deep-reads the top scorers: downloads the PDF, extracts full text, and a strong model writes a summary, key insights, limitations, and how it relates to your own work
4. Delivers a morning HTML digest + optional Telegram ping with the must-reads

Design decisions:

• Nothing domain-specific in the code. Your interests live in one markdown file. Edit it and the same pipeline works for ML, physics, bio, econ, whatever
• Only the two scoring passes touch a model. Fetching, dedup, PDF extraction, and rendering are deterministic Python
• Model-agnostic backend layer: Claude Code / Codex CLIs run it on the subscription you already pay for (no API key), or any OpenAI-compatible endpoint, including fully local via Ollama / vLLM. Backends mix per pass: cheap model for skimming, strong one for the 5-10 deep reads
• Approximate costs, benchmarked in the repo (tokens, cost, latency, quality grades per model). Rough sizing: a 10-abstract scoring batch is ~18k input tokens with a small JSON out; a deep read sends the whole paper, 40-70k input tokens.

I've been using it daily for about a month and it's been genuinely useful in my field. The repo is a generalization of my personal setup to any domain, and I haven't deeply tested fields other than mine, so feedback and GitHub issues are very welcome.

The model has to say "not relevant, 3/10" a lot without drifting toward score inflation, and the whole scoring is just prompt + markdown context. So I'm curious how others would approach calibrating an LLM judge like this

GitHub: https://github.com/ramazan793/research-radar