Evoboard – GitHub style habit heatmaps with automatic activity logging

Hacker News

Hey HN! I built evoboard, a web app which shows your consistency on a year long heatmap. I liked the contribution graphs on GitHub and LeetCode, and wondered if I could make that idea for my everyday habits. I also made it so the habits can be completed automatically when you do them. For example, if you commit to GitHub, play a game of chess, do a LeetCode problem, it can update your habit automatically. You can connect other apps with the use of the API or chrome extension. You can also share your profile with others so you can stay motivated. I’m really proud of this project and I’m going to continue using it myself. I’m happy to answer any questions or feedback!

Visit product View on Hacker NewsAug 31, 2026

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

83%83% 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

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

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

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

47%47% 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: apps · Missing: mobile apps, ios, personal

44%44% 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

20%20% 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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