Technical deep-dives on AI verification, security audits, and trust infrastructure.
Article-by-article mapping: how multi-model verification satisfies Art. 9, 13, 14, and 43 — with code examples and an honest assessment of limitations.
Discovery → Orchestration → Verification → Trust. Most teams stop at Layer 0. The real risk lives in Layer 2.
How we turned 20+ real security findings into automated detection rules — and what the false positive journey teaches about AI security tooling.
How to systematically audit Model Context Protocol servers for prompt injection, authority bypass, and trust boundary violations.
Claim → Perspectives → Synthesis → Hash. Everything else builds on this: receipts, trust scores, cross-agent verification.
When models disagree, most systems pick the majority. We preserve the dissent — because the minority opinion is often right.
Perplexity, LangChain, and CrewAI solve orchestration (Layer 1). ThoughtProof solves verification (Layer 2). Here's why the distinction matters.
We asked Grok to verify its own output, then ran the same claim through the full pipeline. The results are instructive.
Spoiler: No. But the failure mode is fascinating and reveals exactly why multi-model verification exists.
When AI agents call other agents, how do you verify the chain? Epistemic blocks create a verifiable provenance trail.
Parallel execution, smart routing, and when to skip verification entirely. Latency numbers from production.
The first npm package for epistemic verification. What's in it, how to use it, and what's coming next.