Originally posted on LinkedIn on August 4, 2026. In my last post I introduced Nous Ergon, an applied AI lab. Since then, while building inside my multi-platform agentic workflows, I have been running head-first into a notable pain point: how to keep a fleet of agents doing things the same way? Every agent does things slightly differently. Same task, different direction, different quality. As a solo operator supervising this fleet, I have a strong interest in making sure the agents are delivering a standardized, conforming set of deliverables — so I wrote my expectations down. That corpus is now 35 policy documents and well over 100,000 tokens. ...
Introducing Nous Ergon
Originally posted on LinkedIn on July 22, 2026. In February, Block generously freed up 100% of my time. Since then, I’ve spent almost every waking hour building Nous Ergon, an applied AI lab. I have three products in flight: Crucible — an experiment harness for testing agentic and financial strategies Metron — an agent-powered investment dashboard Vires — a strength-training app with an AI coach I’ve also built mnemon, an open-source memory server for LLMs, which came out of a problem I kept hitting along the way: every LLM platform now ships with its own memory, but each one is a silo. Mnemon gives every LLM I run, across any client, one shared long-term memory. ...
Multi-Agent Research: How 6 LLM Teams Analyze 900 Stocks
In Post 1, I introduced Nous Ergon — an autonomous trading system that splits intelligence across four layers: LLM agents for research judgment, ML for pattern recognition, deterministic rules for execution, and a backtester for system-wide learning. This post goes inside the Research module — the layer where LLMs are found hard at work. What a Weekend Run Looks Like Over the weekend, an AWS Lambda fires. It loads the S&P 500 and S&P 400 — roughly 900 mid-to-large-cap US stocks — along with recent price history, and then distributes them across six sector-specialized teams that run in parallel. Each team screens, analyzes, and debates their sector’s best opportunities. A CIO agent evaluates the top picks across all teams and decides which stocks enter or exit the portfolio. ...
Nous Ergon: Building an Autonomous Alpha Engine with AI
The Thesis Can AI generate sustained market alpha — not through a single model making predictions, but through a system of specialized components, each contributing what it does best? That’s the question behind Nous Ergon: Alpha Engine (νοῦς ἔργον — “intelligence at work”), a fully autonomous trading system I’ve been building that combines AI-driven research, quantitative prediction, and rule-based execution. Quantitative finance — using mathematical models and statistical analysis to make investment decisions — has traditionally been the domain of institutional hedge funds with massive engineering teams. Large language models and modern machine learning tooling are changing that equation. ...