Gifhub, bug hunter that shows instead of tells

Hacker News

Like many people, I've started unleashing coding agents on my repos to do parallel, autonomous bug bashing. I had a couple problems: 1. Sometimes the bugs are not actually bugs, just stylistic choices. Fable said it was a bug that my login didn't have a length check error message. I've never seen a length checker on a login. 2. Testing bugs locally takes a long time. 3. Doing testing outside of Github and reviewing code in PRs is a disjointed experience. Gifhub is a simple Claude plugin that solves these problems. It attaches gifs of the reproduced bug and the fix to prs in Github. There's a gif in the README.md to show you what this looks like. Showing is better than telling!

Visit product View on Hacker NewsJun 24, 2026

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2points

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Launch Intel predictions

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

91%91% 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 · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

31%31% 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

24%24% predicted probability of success on AppSumo, 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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