It

It's a Feature, Not a Bug, a gamified bug dismissal logger for QA

Hacker News

It's a Feature, Not a Bug, a gamified bug dismissal logger for QA

I built https://no-bug.app as a small love letter to frustrated testers. It’s a web app where you log every time a bug you reported gets dismissed as “not a bug”, “it is a feature”, or “works on my machine”. Instead of just venting in chat, you turn those moments into data, badges, and leaderboards. Each log is one little “are you kidding me” captured for science. You record what was said, who said it, how annoyed you were, and then watch patterns appear, like realizing your team’s favorite rejection line is basically a catchphrase. There are 28 achievements for things no one should be proud of, like logging your first dismissed bug, surviving a week of “by design”, or discovering that your frustration level graph looks like a crypto chart. There is also a “Blame Game” leaderboard where roles compete for the unofficial title of “most likely to gaslight a bug”, plus a “Frustration Olympics” for the most zen and the most cooked users. You can keep everything private, or let an anonymous username represent you on the global boards, so it feels more like an arcade cabinet than a work report. There are shareable stat cards too, in case you have ever wanted to post “look how many times my bugs were ignored” as content. Underneath the jokes, it is basically a tiny analytics layer on top of everyday QA pain, wrapped in a retro arcade skin.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, user, tiny · Missing: agents, macos, agent
92%92% 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
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: plus, users · Missing: platform, intuitive, reviews
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, 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
28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, crypto · Missing: web3, cryptocurrency, make money
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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