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Match – A pattern matching language that replaces regex

Hacker News

Match – A pattern matching language that replaces regex

I built Match, a pattern matching language designed to replace regex. Instead of cryptic escape sequences, you describe patterns in plain English. Example — matching an email address: email: username then "@" then domain username: one or more of (letter, digit, ".", "_", "-") domain: one or more of (letter, digit, "-") then "." then between 2 and 6 letters Key differences from regex: - Human-readable grammar syntax - Full parse trees with named extractions - No backtracking, no ReDoS by design - Composable grammar modules (use "module" (rule1, rule2)) - Zero dependencies, ~15KB Available on npm (@hollowsolve/match). Docs, playground, and examples on the site. GitHub: https://github.com/hollowsolve/Match

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, email, plain · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
44%44% 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
17%17% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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