Prove that a human wrote it

Hacker News

Hey HN, my name is matt. I built "i typed it" as a platform agnostic proof for human typed text. I've been frustrated recently by posts and comments (on hn and elsewhere) that feel obviously ai generated. I don't mind using AI to brainstorm/edit/revise, but it's a bit painful when it seems like a human didn't even bother reading it before positing. The tool today measures high level signals (typing consistency, paste %, edit rate ,etc.) to give a score. After writing your message you save an attestation link and include it in the post/comment. This basic approach is deeply flawed, but hopefully better than nothing. I'm curious what current state of the art is for _guaranteeing_ that something is human typed. Perhaps some sort of hardware integration? I'm working on a browser extension, and a quick way to integrate attestations with comment sections so users don't need to include the link copy/pasted. Would love any and all feedback. p: https://www.ityped.it/p/RvRegYS453Sy

Visit product View on Hacker NewsFeb 26, 2026

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, using · Missing: mac, agents, macos

75%75% 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

69%69% 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: io · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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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