FitVow – a smart contract that fines me if I miss weekly fitness goals

Hacker News

FitVow is my personal commitment-device experiment: I lock funds in a smart contract for 12 weeks, define weekly fitness goals, and if I miss a week a penalty becomes enforceable. Enforcement is permissionless: anyone can call the contract and a portion of the penalty is paid to the caller; the rest goes to a charity (Giveth). Weekly activity data comes from my smartwatch (via Samsung Health / Health Connect), synced by an Android app that publishes the weekly activity on-chain. The site is mostly a viewer for on-chain state. Full write-up / architecture / security notes in the first comment.

Visit product View on Hacker NewsJan 27, 2026

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TrustMRRFits verified-revenue profile · Strong signals: personal, fitness · Missing: mobile apps, ios, entrepreneurs

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

best fitHighest predicted score across all platforms for this description.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

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

41%41% 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

39%39% 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 HuntUnlikely to reach the leaderboard · Strong signals: activity, notes · Missing: mac, agents, macos

38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid, smart · Missing: web3, chat, crypto

28%28% predicted probability of success on BetaList, based on ML models trained on real launch data.

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

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Correct prediction on native model

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