The Internet's Open Source AI Paywall

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I built DarkForest Protocol after watching sites get hammered by AI search bots. Robots.txt isn't working. Some sites are using Nepenthes/Quixotic to poison models, but that's a lose-lose approach. DarkForest is an open-source middleware (Express/Next.js/Vite) that lets any site establish clear boundaries with AI systems. One line of code redirects crawlers to a message about respecting content - no poisoning required. This is just the start. The long-term vision is to build: (1) A collective intelligence network where sites share crawler patterns, and (2) An internet scale information marketplace for powering AI. Think Google Ads, but for AI systems seeking real-time access to your content. I believe we need infrastructure for fair, automated value exchange at internet scale - not the endless race of litigation and blocking. Would love feedback, especially from: web hosts dealing with AI bot problems, devs who could implement/contribute/critique, and anyone with thoughts on the marketplace vision Source: https://github.com/darkforest-protocol/darkforest-blocker Discord: https://discord.gg/JMBhkhUx

Visit product View on Hacker NewsFeb 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, models · Missing: mac, agents, macos

83%83% 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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released

51%51% 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 · Strong signals: google · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

21%21% 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

13%13% 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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