I made GarWin, a free and user-friendly Garmin Badges database

Hacker News

Hey HN. I am, sadly, ill-equipped for sports and gamification gets me going, so when I discovered that my GF completed almost all related challenges since she got a Garmin smartwatch, I naturally wanted to browse all badges. And the UX was crap. Very little filters and no way to browse the past badges. So I figured-out how to get the data - a little over 1k badges in total so far - and then I manually fine-tuned it, to later add more filtering options, whenever related. It is built with Next.js (on App Router) and Supabase's DB and PostgREST. I thought I'll share: it is all free to use, and I hope those silly challenges help others stay fit, get in better shape or just enjoy sports and accomplishments. Cheers, Vik.

Visit product View on Hacker NewsFeb 29, 2024

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

82%82% 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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: friendly · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

43%43% 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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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