Open-source Spotify Wrapped for arbitrary data

Hacker News

Get a "Year-In-Review" for arbitrary data. Yirgachefe was borne out of 3 ideas: 1) Spotify Wrapped is delightful 2) In many countries, services are required by law to provide your exported usage data in a timely manner. 3) Many services don't (or can't) provide years-in-review. I also love the idea of summarizing data people may _not_ want, a la the recent SNL "UberEats Wrapped" sketch. Now you can "wrap" anything if you have the data - traditional sources like video & music streaming, or more creative ones like terminal history, amazon purchases, git contributions, etc. If you bring-your-own API key, all processing exclusively happens in your browser + the Anthropic API (minus some anonymized usage data sent to Umami). Outputs aren't perfect (it's AI after all), but it's also a demo of what you can do with the latest LLM coding tools and a weekend.

Visit product View on Hacker NewsDec 29, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: coding, open · Missing: mac, agents, macos

95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

82%82% predicted probability of success on Indie Hackers, 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

61%61% 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: exclusive · Missing: plus, platform, intuitive

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

28%28% 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

15%15% 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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