Anagnorisis, local data-management with trainable recommendation engine

Hacker News

I've been working on a project for the past two years called Anagnorisis. It's an self-hosted system that acts like a personal, local Google for all your household's media - music, photos, videos and notes. The core idea is to reclaim our data from cloud services and build a recommendation and search system that is 100% private and adapts to your personal tastes. The whole system is built with Python (Flask, PyTorch, Transformers) and runs locally on your own machine. For me, this started as a personal quest to build a spiritual completely local successor to Grooveshark's fantastic recommendation engine, which I've missed for years. It has since grown into a much broader tool that I use daily. I believe we need more tools that give us the power of modern AI without sacrificing privacy and ownership of our most personal data. I'd love to get your feedback and hear your thoughts.

Visit product View on Hacker NewsJun 25, 2025

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2points

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

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

46%46% 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

16%16% 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.

Incorrect prediction on native model

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