| 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? |