A page that hides a sentence for AI and lets you check if it came back

Hacker News

The premise is web pages have two readers, people and the AI reading for people. Web pages can now be written more for the AI and less for people. It’s a companion to an earlier page about browser fingerprinting. ( https://news.ycombinator.com/item?id=48062178 ) On my page, the there’s a planted phrase for AI. As the site gets crawled, you can see if this phrase is ever returned by any AI summarizing the page. So there’s a couple things you can do on my page: 1. Ask any AI to summarize the URL, and check if the planted phrase has been read. 2. Create a short-lived link and paste into any chat. You will see which crawler reads it first, before anyone ever actually opens or clicks on the link. 3. Generate your own planted phrase and leave it for the next crawler. On this page, I also look at one example, composio.dev/hermes which serves a block of instructions specifically for AI agents. It’s not malicious, and it’s good marketing but shows that web pages can now be created and written for two very different audiences.

Visit product View on Hacker NewsMay 29, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, new · Missing: mac, macos, cursor

69%69% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

61%61% 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, 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 · Missing: mobile apps, ios, personal

49%49% 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

25%25% 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

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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