New 10-part series · Read or listen

Inside BitBank: Engineering an Autonomous AI Research Lab

A practical tour of the data, models, GPUs, validation, risk controls, production services, coding agents, and institutional memory behind a modern market-research platform.

1. Building an Autonomous AI Research Loop
   How hypotheses move through causal data, validation, risk gates, deployment, and continuous learning.
2. Market Data as Research Infrastructure
   Timestamp contracts, point-in-time datasets, storage layout, quality controls, and live feature parity.
3. Forecasting Models: From Baselines to Transformers
   Why simple rules, boosted trees, and time-series foundation models must compete on equal evidence.
4. Scaling AI Systems: GPUs, CUDA, Distributed Training, and Autonomous Research
   The compute, storage, compiler, scheduling, and memory systems that expand feasible research.
5. Feature Discovery, Representation Learning, and Synthetic Markets
   Causal feature search, self-supervised market embeddings, transfer, and synthetic stress testing.
6. Honest Backtesting: The Discipline of Trying to Be Wrong
   Chronological folds, purge gaps, execution costs, locked holdouts, and reproducible promotion gates.
7. Risk, Uncertainty, and the Power to Abstain
   Calibration, cash as an action, conservative sizing, portfolio controls, and circuit breakers.
8. Production ML: Serving, Monitoring, Drift, and Rollback
   Immutable artifacts, Go services, model sidecars, shared inference, observability, and safe releases.
9. Coding Agents as Research Collaborators
   Specialized agents, verifiable work, bounded authority, failure memory, and human review.
10. Toward a Self-Improving Market Research Lab
    A roadmap for continuously better evidence, safer decisions, and transparent market intelligence.