Daniel Roberts
I build and operate autonomous AI systems, and run the programs that ship them.
Most people talk about what AI will do. I operate it. A governed fleet of autonomous agents, live in production, run by one person who spent 25 years learning how large systems succeed and fail. I've led 50 engineers across three countries and I've built alone, and I've learned the future belongs to leaders who can do both. I'm here to build what's next, and to lead the people who ship it.
Dusty Nook · 2018–now
enterprise · telecom & e-commerce
What I’m building now.
Autonomous markets platform
Autonomous crypto-derivatives research & execution: a self-healing service fleet, 3.5 TB proprietary corpus, microsecond decision loops, institutional-grade statistical validation.
Explore →Prediction-market intelligence
22-collector data spine, a nine-layer data model, and self-proving strategy governance: gate → shadow → canary → live.
Explore →AI-orchestrated multiplayer game
A production real-time 3D game, built end to end by a team of four AI models I directed as lead orchestrator, and shipped in two weeks of nights. Live on the internet: my 0-to-1 operating model, proven.
Explore →The earlier acts
Encrypted field tooling, monetized internal platforms, and data-driven ops programs from 25 years in telecom.
Explore →Two live systems, one federated architecture, independent infrastructure sharing data through a strictly read-only seam.
Frictionless, lean, evidence-first.
Measure first
Before I trust a result, I price it against reality: real costs, real constraints, real users, with the pass bar written down before the work begins. Most candidates die here, and I record why.
Prove forward
What survives gets proven forward, judged against a frozen verdict rule written before the outcome is known. The gap between a promise and a production result is where I keep myself honest.
Automate everything
I treat downtime as theft and manual steps as bugs. What I build deploys, monitors, and repairs itself around the clock, and publishes its own health.
CPMAI: the discipline every result runs under.
AI projects fail at ~80% industry-wide (RAND); CPMAI exists to invert that, a vendor-neutral, iterative, data-centric method in six phases.
Business Understanding
Define the business problem and expected value before any technical work begins, so AI addresses a real need rather than technology for its own sake.
Data Understanding
Explore and assess the data landscape to determine feasibility and surface gaps, before they become costly discoveries later in the lifecycle.
Data Preparation
Transform raw data into AI-ready datasets through cleaning, labeling, and feature engineering, the most time-intensive and performance-critical phase.
Model Development
Build and train models that address the defined objectives, with development kept purposefully aligned to business requirements.
Model Evaluation
Assess performance against business requirements, technical metrics, and ethical standards, with bias and fairness checks, before any deployment.
Model Operationalization
Deploy into production with monitoring, governance, data-drift detection, and continuous improvement.
This isn’t framework name-dropping: the same six phases govern Chimera and Prescient today. Watch them run →