Lab notes, not press releases.
We publish what we build and what we learn — architecture, method, results. Including the negative ones.
LAB NOTE // 001
Architecture of a local ACI: what we learned running an autonomous cognitive system on four GPUs at home
For five weeks in 2026, an autonomous cognitive system with persistent memory, gated self-improvement and a five-model deliberation council ran continuously on a home-lab server. The architecture, the numbers, and the honest limitations.
Jul 19, 2026 • 6 min
LAB NOTE // 002
Validation first: twelve hypotheses, zero survivors
We built a validation-first trading harness, tested 12 classic systematic strategies across FX, gold and crypto, and rejected every one with statistical evidence. Here is the method, the numbers, and what failure taught us.
Jul 19, 2026 • 6 min
LAB NOTE // 003
Hydra: composing specialized models instead of scaling one
Our systems aren't one model doing everything. A high-capacity core coordinates models dedicated to specific roles — orchestration, audio, vision — over an internal nervous system we call Tesseract. What we built, and what is still design.
Jul 19, 2026 • 6 min
LAB NOTE // 004
Embedded AI in a real operation: designing an assistant that acts, but only with permission
Atlas PM puts an AI operator inside a field-service platform — reading operational data, flagging what needs attention, and taking actions only a human confirms. What we built, and the honest limit of confirm-by-prompt.
Jul 19, 2026 • 6 min
LAB NOTE // 005
When the win rate lies: killing a prediction-market strategy with our own gate
An 80% win rate that meant nothing. We built a prediction-market research system, pre-registered a validation gate, and watched the gate catch our own false positive before a dollar of judgment rode on it. A rigorously tested negative.
Jul 21, 2026 • 6 min
TECH EVOLUTION // 02
Code Got Cheap. Trust Didn't.
AI scaled code generation ~3x but verification scaled 0x, migrating the bottleneck (and the senior-engineer cost) into review/release — and standard metrics measure the wrong scarcity.
Jul 22, 2026 • 8 min
TECH EVOLUTION // 01
Every Agent Is a Principal Now
Machine identities outnumber humans 109:1 and 79 of them are AI agents; identity is the unglamorous layer that decides whether enterprise agent adoption compounds or stalls.
Jul 19, 2026 • 7 min
Personal notes from the research journey — first entries coming.
HawkClaw
Research in multi-agent orchestration for security validation. Rules of engagement and concept of operations as first-class artifacts, deconfliction between agents, MITRE ATT&CK used as a classification framework, not a target list. Scored 7/8 on the XBOW benchmark (Hard L3) — the benchmark validates the orchestration layer, not an invitation to an offensive service.
Built on the open-source Decepticon agent framework (Apache 2.0, PurpleAILAB); the operator dashboard and the cryptographic governance layer are the lab’s own work.
Hydra & Tesseract
Hydra is our name for composing specialized models into one system — a high-capacity core coordinating models dedicated to specific roles (orchestration, audio, vision). Tesseract is the internal nervous system that connects them: a message bus as synapses, a vector store as long-term memory.
Both are described here as designed and, where measured, as measured — the lab note says which is which.
→ Lab note: Hydra, composing specialized models