Year in Review – Breakout with your GitHub contributions

Hacker News

Hi HN! it's year-end, so I built a very responsible way to review my GitHub year: turn my contribution calendar into a Breakout level and smash through it. gh-kusa-breaker is a terminal Breakout game where each day on your GitHub contribution graph becomes a brick (more contributions = tougher brick). It uses `gh` auth and GitHub’s GraphQL contributionCalendar. Try it: gh extension install fchimpan/gh-kusa-breaker gh auth login gh kusa-breaker In Japanese we call the contribution graph "kusa"(grass). Now you can literally break it. Repo + demo: ` https://github.com/fchimpan/gh-kusa-breaker `. Feedback welcome.

Visit product View on Hacker NewsDec 27, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

66%66% 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

63%63% predicted probability of success on Indie Hackers, 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

48%48% 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 · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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