They flag real students as cheaters, mislabel original writing as “likely AI,” and rely on statistical guesswork that just isn’t reliable. Even OpenAI shut down their own detection tool, citing low accuracy.
So I built EncypherAI: an open-source tool that embeds verifiable cryptographic metadata into AI-generated text at the moment of creation.
Think of it like a digital fingerprint: invisible, tamper-proof, and verifiable in milliseconds.
- No changes to how the text looks or reads
- Works with OpenAI, Anthropic, local models, or custom pipelines
- Lightweight Python package with CLI
It uses invisible Unicode variation selectors to embed the metadata without altering the visible text.
Metadata can include model ID, timestamp, purpose, and even user or session IDs, all verifiable offline using HMACs.
We're hoping this becomes a baseline standard for AI content attribution, something platforms and LLM providers can adopt to prove when something was generated, instead of guessing. This is already sparking conversations with leading LLM providers building toward responsible AI infrastructure.
Absolutely love this. Feels like a real step forward instead of another patch on a broken system. Invisible, secure, and actually useful—huge props to the team behind this. Hope LLM providers start paying attention.
Really appreciate that, this was exactly the goal. Detection always felt like a guessing game, so we wanted to flip the model and build something verifiable from the start. We’re already starting conversations with folks close to the major LLM providers, so fingers crossed this helps move things in the right direction. Thanks again for the kind words, means a lot!
AI detectors suck.
They flag real students as cheaters, mislabel original writing as “likely AI,” and rely on statistical guesswork that just isn’t reliable. Even OpenAI shut down their own detection tool, citing low accuracy.
So I built EncypherAI: an open-source tool that embeds verifiable cryptographic metadata into AI-generated text at the moment of creation. Think of it like a digital fingerprint: invisible, tamper-proof, and verifiable in milliseconds.
- No changes to how the text looks or reads
- Works with OpenAI, Anthropic, local models, or custom pipelines
- Lightweight Python package with CLI
It uses invisible Unicode variation selectors to embed the metadata without altering the visible text.
Metadata can include model ID, timestamp, purpose, and even user or session IDs, all verifiable offline using HMACs.
We're hoping this becomes a baseline standard for AI content attribution, something platforms and LLM providers can adopt to prove when something was generated, instead of guessing. This is already sparking conversations with leading LLM providers building toward responsible AI infrastructure.
Here's the GitHub: https://github.com/encypherai/encypher-ai
Website: https://encypherai.com?utm_source=hn&utm_medium=post&utm_cam...
Article + 1-min explainer: https://encypherai.com/blog/what-if-ai-content-came-with-bui...
Would love your thoughts. Is this the kind of system we need to make AI content more trustworthy?
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Absolutely love this. Feels like a real step forward instead of another patch on a broken system. Invisible, secure, and actually useful—huge props to the team behind this. Hope LLM providers start paying attention.
Really appreciate that, this was exactly the goal. Detection always felt like a guessing game, so we wanted to flip the model and build something verifiable from the start. We’re already starting conversations with folks close to the major LLM providers, so fingers crossed this helps move things in the right direction. Thanks again for the kind words, means a lot!