The README Is the Payload
When an agent discovers a tool by reading its README, the README becomes the attack. Inside AgentBaiting, the FakeGit campaign, and the gate built to hold the line.
Agentic AISecurity & Compliance
Studies · Field Notes · Agentic AI · Guardrails
Systems that keep themselves honest, secure, and legal.
Research, architecture, and working notes on agentic AI — local-first, auditable, and built where the rules are real. Written from inside the build, where the failure modes actually live.
Regulated & security-first
Built where the rules are real
Security & compliance →Local-first by default — MAX3, REEF, and FeedHacker keep data on your own hardware, so there's no third-party egress of PII/PHI, the smallest possible breach surface, and a clean data-residency story. Automation stays approval-gated, reversible, and audited.
Track record
Delivery in high-stakes systems
$3.1B
multi-brand health plan modernized
~50%
operational efficiency gain from system unification
200%
platform performance lift (~$240K/yr saved)
10×
scale delivered at a Series A–C EHR/RCM startup
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Latest
From the field notes
All research →Inside MAXs Inside the Loop
Eleven days after the first field guide went to press, most of its “limits today” have shipped. How a local-first mind transplanted its own spine withou…
Agentic AI
Reading Over the Machine's Shoulder
What Python is, where it came from, and why you still have to be able to read it.
Agentic AI
Build log
Selected projects
All projects →MAX3
A local-first, voice-first AI resident built as a single Python process — a DuckDB graph spine for memory, a Forward-Forward learner that predicts, proves and adjusts, and a voice loop to talk to him. The collective's flagship.
PythonVoice-firstFlagship
REEF
A local-first network-visibility and gateway-defense tool — a named fleet of sensors fused into one high-confidence card that sees it, explains it in plain language, and acts only on your approval.
PythonOPNsenseSelf-Defending
The Agentic AI Builder's Playbook
A 25-part published series on production-grade agentic AI architecture: the nine-layer agent stack, MCP and A2A protocols, RAG variants, agent memory, and evaluation & observability.
Writing25 Parts
Founder
About the founder
Full story →Jason Newell is the founder and principal architect of MAX Research Collective. He brings 25+ years of engineering leadership at the intersection of production agentic AI and regulated healthcare, and is the author of The Agentic AI Builder's Playbook, a 25-part series on production-grade agentic AI architecture. His current research centers on MAX3, a local-first, voice-first AI resident, and the theory behind it.