No

No more writing shitty regexes to police usernames

Hacker News

No more writing shitty regexes to police usernames

Every product that allows usernames eventually ships the same broken solution. Someone adds a blacklist. Then a regex. Then another regex copied from StackOverflow. It works just long enough to ship, and then `admin`, `support`, city names, brand impersonation, and obvious slurs start leaking through anyway. Everyone knows it’s fragile, but it gets ignored because "it’s just usernames". I’ve had to rebuild this logic across multiple products, and I got tired of pretending it’s a solved problem. So I built *username.dev*, an API that answers a more useful question than "is this taken?" — it tells you what a username actually represents. Instead of returning a boolean, the API classifies usernames into real categories like brands, public figures, places, system-reserved terms, dictionary words, premium handles, and offensive content, and returns structured metadata you can actually make decisions with. That means blocking impersonation without breaking legitimate users, stopping abuse without maintaining massive regex lists, and even monetizing high-demand usernames if that’s part of your product. Under the hood it’s intentionally boring infrastructure: Cloudflare Workers at the edge, KV for fast reads, D1 for usage and analytics, and a simple HTTP endpoint (`GET /check?input=foo`). P95 latency sits around 300ms globally. There’s no ML magic, no black box, and no attempt to be clever — just fast, deterministic classification. Pricing is usage-based and prepaid because subscriptions for infrastructure like this are annoying. There’s a free tier with 1,000 requests and no credit card. Use it, throw it away, or rip the idea off. If you think regex blacklists are "good enough", usernames don’t matter, or this is a trivial problem, you’re probably already shipping bugs — they’re just not loud enough yet. Tell me why this is a bad idea, what edge cases I’m missing, or what you’ve duct-taped together instead. — Sourabh

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Product HuntOn track for Day 1 leaderboard · Strong signals: user · Missing: mac, agents, macos
79%79% 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
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
55%55% 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: users · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: answers, users, way · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · 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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