EncypherAI, Open-source tool for cryptographically verifying AI text

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Hi HN, I'm one of the makers of EncypherAI. We built this tool because current AI detection systems often fall short when it comes to reliably identifying AI-generated content. Instead of relying on guesswork, EncypherAI embeds cryptographically verifiable metadata directly into AI-generated text. If the metadata is present, you can confirm with 100% certainty which model produced the text, when it was generated, and see any custom JSON data provided by the developer without any risk of false positives or negatives. Note: The absence of metadata doesn't automatically indicate human-generated content, as a malicious actor might strip out our markers. That's why our focus is on creating a standard that, when adopted, guarantees verifiable proof whenever it's used. EncypherAI is open-source under the AGPL 3.0 license, and our goal is to set a new industry standard for content authentication. It's free to use, and we're excited to get your feedback, contributions, or ideas for improvement. Ask me anything!

Visit product View on Hacker NewsMar 31, 2025

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

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

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

55%55% 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

50%50% 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

33%33% 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: crypto · Missing: web3, chat, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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