Hey r/rss, Like many of you, my RSS feeds are a firehose. Whenever a major tech or world event breaks, I end up scrolling through dozens of articles from different outlets reporting the exact same press release with slightly different headlines. Most existing readers either do basic string matching (which misses paraphrased stories) or lock semantic clustering behind a monthly cloud subscription. I wanted something local, fast, and hackable, so I spent the last few months building RadarRSS: How it handles deduplication: - Ingests feeds in real time and calculates text similarity to cluster related stories into a single thread. - If you provide an optional Gemini API key, it generates a quick multi-source synthesis/bullet-point summary so you don't have to open 5 tabs. If you don't use AI, clustering still works out-of-the-box. The Stack & Footprint: - Frontend: React 19 + Tailwind. - Desktop: Packaged with Tauri 2.0 (Rust backend, ~6.8 MB binary) to keep RAM usage minimal compared to standard web wrappers, with an Electron fallback. - License: MIT, 100% open-source, local-first data storage. Where I need your brutal feedback: 1. Clustering thresholds: Balancing false positives (grouping unrelated stories) vs. false negatives (failing to cluster slightly different angles). 2. What edge cases do you run into with heavy/malformed RSS/Atom feeds that break standard parsers? GitHub: https://github.com/dznass-cmd/RadarRSS I'd love to hear how you currently deal with feed fatigue and what features you'd expect from a clustering engine.   submitted by   /u/Significant-Flan5228   to   r/rss [link]   [comments]