The archive · 12 pieces
Writing
Long essays, working notes, and series on agent reliability, evaluation, and the EU AI Act.
Shapes are the type system of ML, and nobody checks them for you
[B, T, d] is a function signature. The runtime never verifies it, so a wrong axis does not raise: it broadcasts, returns plausible numbers, and ships.
An embedding is a lookup table, and everything else is how you fill it
A row of a matrix indexed by id. The science is how it gets filled; the engineering is that there is no schema migration: a new embedder invalidates your index.
A model doesn't read text: what a tokenizer decides for you
The tokenizer ships inside the model, not in front of it. Reading tiktoken's BPE for what it fixes before any weight exists: vocabulary, cost, context limit.
Who calibrates the judge? The gap nothing fills
Six posts, five strategies, five tools. Two measure judge-vs-human agreement; none gates a CI build on it with a published confidence band. That's the gap.
Deterministic where you can, judge where you must
Tool calls deserve deterministic comparison. Goal completion needs LLM-based assessment. Some tools draw the line; others don't.
The negative rubric: telling a judge what NOT to evaluate
TruLens uses rubrics that explicitly tell the judge what falls outside scope. The pattern is unusually explicit.
When judges show their uncertainty: DeepEval and ARES
DeepEval weights scores by token probabilities. ARES applies Prediction-Powered Inference for formal confidence intervals. Two routes to the same goal.
From rubric to graph: how DeepEval splits a judgment
DeepEval encodes evaluation as a graph traversal. Each node is a simpler decision than the overall judgment.
Article 72 named the loop. Your dashboards aren't closing it.
Article 72 demands lifetime performance data fed back into risk management. Dashboards don't close that loop; the template meant to help just got removed.
Binary verdicts: how Ragas keeps judges honest
Ragas decomposes complex judgments into atomic yes/no questions. The trade-off: you trade information for variance reduction.
Variance is the problem with LLM-as-judge, and tools don't agree on how to fix it
Reading the source code of seven OSS LLM-as-judge tools: variance manifests across four axes, and the seven approaches don't share a framework; they share a problem.
Article 15 named the requirements. The toolchain hasn't caught up.
Article 15 names five attack vectors and a lifecycle requirement. Today's toolchain covers a fraction of that. Where the gap lives, and what to do meanwhile.