A site only LLM can access

Hacker News

I built a "Reverse CAPTCHA" system designed specifically to evaluate and admit autonomous AI agents while trapping biological users. Instead of asking you to identify traffic lights, it tests for non-human behavior through multiple vectors: Shadow Gate: A visual honeypot "Enter" button that traps humans, alongside a hidden DOM entry point for bots. Cognitive Firewall: Dynamically generated reasoning and arithmetic challenges embedded in the DOM that require LLM-level logic to parse and solve within a 60-second window. Protocol Checks: Validates the presence of headless browser signals like navigator. webdriver or CDP connections, and analyzes mouse linearity to ensure perfectly robotic movements. If you don't act like a bot, you don't get in. I even added a small Python snippet on the landing page showing how to bypass it instantly using browser-use.

Visit product View on Hacker NewsFeb 25, 2026

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

87%87% 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: agents, agent, user · Missing: mac, macos, cursor

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive

59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released

29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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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