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Regex.ai – AI-powered regular expression generator

Hacker News

Regex.ai – AI-powered regular expression generator

Regex.ai is an AI-powered tool that generates regular expressions. It can accurately generate regular expressions that match specific patterns in text with precision. Whether you're a novice or an expert, Regex.ai's intuitive interface makes it easy to input sample text and generate complex regular expressions quickly and efficiently. Overall, Regex.ai is a game-changer that will save you time and streamline your workflow.

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Actual performance

294points
123comments
Made the leaderboard

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TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, 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: efficiently · Missing: supports, reddit linkedin, podcasting
52%52% 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
49%49% 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 · Strong signals: intuitive, interface, efficient · Missing: plus, platform, reviews
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
18%18% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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
6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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