RSS Filter

Hacker News

RSS Filter

Sometimes it's hard to keep up with multiple RSS feeds, and would be great to get some help filtering out the least relevant part of it. RSS filter is a side project that I created for myself to act as an RSS feed articles recommendation system, but self-hosted. It has a Python backend that is able to “proxy" your existing RSS feeds and after learning a bit about your interests, starts recommending similar articles and filtering out the rest. While, of course, always including some random articles for discovery. It uses LLM embeddings and machine learning to recommend similar articles. Embedding models allow for a new era of recommendation systems, where a large user base is not required, since allowing for self-hosting. Feel free to try it on my server or self-host it yourself. All feedback and contributions are welcome.

Visit product View on Hacker NewsMar 31, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, including · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, user · Missing: agents, macos, agent

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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