Data engineer. I build data platforms on AWS and, outside work hours, small autonomous systems and research tooling — mostly Python, always with a bias for things that can be checked rather than believed.

The rule I build to: if a tool makes a claim, it should be able to prove it. Each repo below ships the tests, an evidence file, and the failure modes it does not hide.

Purged walk-forward cross-validation with embargo, IC decay curves, Benjamini-Hochberg FDR across the whole factor family, deflated Sharpe for the number of trials actually run — and a promotion gate a factor must satisfy before it is allowed into a portfolio.

$ factor-lab list
Name                   Category           Description
mom_20                 momentum           20-day log-return (short-term momentum)
rev_5                  reversal           Negated 5-day log-return (short-term reversal)
vol_20                 volatility         20-day realised volatility, annualised
rsi_14                 oscillator         14-day Relative Strength Index (Wilder smoothing)

107 tests · MIT

Record an agent run once, re-run it forever with zero API keys. Assert tool-call contracts, gate the token cost of a change, and diff behaviour when you swap the model underneath.

$ agenteval replay --run examples/recordings/sample_run.jsonl --mode strict
$ agenteval drift  --baseline base_result.json --current new_result.json

136 tests · MIT

A calibrated cost model and an enforced memory budget in front of a from-scratch NumPy inference engine (KV cache, quantisation). When the model does not fit, it says so loudly instead of OOM-ing silently at token 400.

$ fitsproof probe
Probing machine...
  bandwidth:  6.69 GB/s
  gemm:       314.31 GFLOPS
  RAM:        33.55 GB
  VRAM:       0.00 GB

148 tests · MIT

An MCP server that gives an AI agent read access to a warehouse through a SQL-AST-enforced column policy, row filters, query budgets, redaction and an append-only audit trail. In progress.

Each repo went through the same iteration protocol — research → implement → evaluate → adversarial review → mutation testing — with the evidence committed next to the code (EVIDENCE.md, docs/ADVERSARIAL_REVIEW.md, reports/).

The test counts above are not self-reported. They come from a fresh clone, a clean virtualenv, pip install -e '.[dev]', pytest and ruff — the same commands in each README — and run on CI for Python 3.11 and 3.12.

Where something is unfinished or wrong, the README says so. A repo that hides its gaps is a demo; one that states them is a piece of work.

• AWS data lake engineering — Glue, Athena, S3, orchestration; cost and correctness at scale
• Data tooling: dbt, Dagster, Ibis
• Open-source contributions to strands-agents/harness-sdk