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Continuity-auth – Respect-weighted rate limits for the open web

Hacker News

Continuity-auth – Respect-weighted rate limits for the open web

Identity is a missing piece for managing security in cyberspace where agents co-exist with humans. Traditional methods of managing open-access like captcha or anubis punish real humans while either being rapidly outclassed by computer-use agents or scaling poorly as the value of the site rises. continuity-auth is my attempt to fix this from first principles by using device-continuity proof as a trust signal and time (enforced via rate-limiting) as the core resource to provide a graceful, zero-trust, login-less method to prevent abuse, supporting both browsers and CLI as first-class clients. Built with Clojure/Script, babashka, and Datalevin. Work in progress. Happy to discuss. Source: https://github.com/danieltanfh95/continuity-auth

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, computer · Missing: mac, macos, cursor
87%87% 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
58%58% 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: exist, ide, io · Missing: https docs, excited, just released
46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
27%27% 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
18%18% 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.

Correct prediction on native model

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