differential-testing
Portable, content-addressed reliability evidence for LLM systems. Capture how a model behaves under perturbation; preserve, verify, and diff the evidence across model changes.
Prove AI-written code, refute it with the exact failing input, or honestly abstain. Never guesses. Runs locally.
Simplify Ethereum security analysis and testing
Adversarial precision testing for FHE programs
