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Online equivalence checker for JavaScript RegExp

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Online equivalence checker for JavaScript RegExp

I built a simple web page that checks whether two JavaScript regex are equivalent (i.e. match exactly the same strings). If the two regex are not equivalent, the tool shows example strings that match one regex but not the other. The most common JavaScript regex syntax features are supported. Some more exotic features like back references are not supported. For anchors (^/$) and lookahead assertions, support is a bit wonky. The UI is vibe coded but the algorithmic machinery underneath is completely hand written.

Visit productView on Hacker NewsAug 20, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, code · Missing: agents, macos, agent
54%54% 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
53%53% 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: io · Missing: https docs, excited, just released
46%46% 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 · Missing: mobile apps, ios, personal
43%43% 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
12%12% 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
5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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