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Htmask.js – The input masking library nobody asked for

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Htmask.js – The input masking library nobody asked for

I built htmask.js because I was bored and all my input fields were embarrassingly unmasked. It's a dependency-free JavaScript library that does one thing: masks inputs. No npm bloat, no build step, no tests (yet). Just a script tag and some mask attributes: ```html <input mask="(00) 00000-0000"> <input mask="00/00/0000"> <input mask="AAA-0000"> ``` Where `0` = digit, `A` = letter, and everything else = "let's see what happens". I made this as a weekend boredom project because existing solutions required reading documentation, and I'm fundamentally opposed to that. It works with vanilla JS, plays nicely with htmx (shockingly), and has caused zero production incidents so far (sample size: 1). The code is probably terrible, the edge cases are definitely unhandled, but unformatted input is ugly and this library is… slightly less ugly. GitHub: https://github.com/DaviTostes/htmask Fair warning: Use at your own risk. Or better yet, don't use it at all. But if you do, let me know what breaks.

Visit productView on Hacker NewsOct 22, 2025
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
69%69% 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
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · 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 · Missing: plus, platform, intuitive
36%36% 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
28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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