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Compose-regexp.js Build readable and maintainable RegExps. ReDOS begone

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Compose-regexp.js Build readable and maintainable RegExps. ReDOS begone

This is an old lib I just rejuvenated, adding support for Unicode RegExps, properly handling back-references, and taking advantage of these to create helpers that turn things that are usually too impractical to use into usable techniques. It lets you write anything you'd write with normal RegExps, but with a more readable syntax, and it lets you compose sub-expressions into larger patterns. It also provides helpers like: - `atomic()` that prevents the engine from backtracking into what you passed as argument. No more ReDOS. - `bound()` works like `\b` but for arbitrary character sets. E.g. `bound(/[_\p{L}\p{Number}]/u)` is a Unicode-aware `\b` - Set operations on character classes: `charSet.intersection(/\p{Lowercase}/u, /\p{Script=Greek}/u)` will only match lower case greek letters I hope you'll find it useful!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
45%45% 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, io · Missing: https docs, excited, just released
36%36% 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
26%26% 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
16%16% 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.

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