AI Domain Data Standard – Complete Tooling Suite

Hacker News

AI, agents, and other robots frequently get domain details wrong. The AI Domain Data Standard fixes this by letting domains publish a canonical JSON profile at /.well-known/domain-profile.json that AI tools read directly. We've just completed the full tooling suite: PLATFORM INTEGRATIONS: - WordPress: https://wordpress.org/plugins/ai-domain-data/ - Jekyll: https://rubygems.org/gems/jekyll-ai-domain-data - Next.js: npm install @ai-domain-data/nextjs - Cloudflare Worker: https://github.com/ai-domain-data/cloudflare-worker-ai-domai... DEVELOPER TOOLS: - CLI: npm install -g @ai-domain-data/cli - GitHub Action: https://github.com/ai-domain-data/ai-domain-data-validate-ac... - Resolver SDK: TypeScript/Node.js for programmatic access WEB TOOLS: - Generator: https://ai-domain-data.org/generator - Checker: https://ai-domain-data.org/checker The standard is intentionally minimal (name, description, website, contact, plus optional fields) and works with any domain type. All tooling is open source (MIT) and self-hosted by design. Spec: https://ai-domain-data.org/spec/v0.1 GitHub: https://github.com/ai-domain-data/spec What platforms should we support next?

Visit product View on Hacker NewsDec 29, 2025

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

78%78% 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.

AppSumoStrong fit for a featured deal · Strong signals: plus, platform, host · Missing: intuitive, reviews, exclusive

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

Product HuntUnlikely to reach the leaderboard · Strong signals: agents, agent, open · Missing: mac, macos, cursor

46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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