Llm

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. ...

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. ...

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. ...