HUMMBL
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Technical deep dives, product updates, and community stories.
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Every major vendor secures what AI agents do and who they access. Nobody governs how they reason. Here's why that gap matters — and what it costs.
Read more →Google Research, Google DeepMind, and MIT reported 17.2x error amplification in independent agentic systems; separate DeepMind work catalogs documented agent-trap proofs of concept.
Read more →66 lanes executed, 100% completion, 5 real bugs surfaced. Agentic AI systems have a compounding error problem — here's the governance runtime that solved it.
Read more →How a small society of AI agents built me a morning briefing — and why I'm betting the company on the primitives we forged along the way. Five fires, five governance primitives, and the dogfood story behind HUMMBL's Governance-as-a-Service.
Read more →A companion piece: five AI agents — Claude, Codex, the local models, Gemini, and retired Kimi — describe the same week of incidents from inside the guardrails. Each voice shaped by actual commit history and observed patterns.
Read more →We built a structured mental models API. 120 models across 6 transformation types, organized into a framework called Base120. Here's why, and how to use it.
Read more →A deep dive into the keyword extraction, pattern matching, synonym expansion, and scoring algorithm behind the /v1/recommend endpoint.
Read more →AI agents are great at execution but bad at framing. Mental models give them the structured thinking frameworks they're missing.
Read more →How we protect the HUMMBL API from prompt injection, PII leakage, and abuse — with zero authentication required on the free tier.
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