Testing sensor reduction, feature ranking, and generalization to unseen participants

How technical choices can lead to safety failures, and what to do differently

"Always switch" is true for one particular host. Watch a few episodes and you can work out which host you've got.

The statistical paradox that shows how combining data can reverse the story.

Ten quantization guides, and not one threshold-dependent metric between them — what that gap hides, and the two numbers that close it.

How participant-aware evaluation changed performance across three machine-learning models

I rated my confidence 4 out of 5, then couldn’t find a planted bug in 8 minutes. Here is the tool, the run, and its limits.

The tool found the right passage but missed the mismatch, and the audit shows where rule-based checks break down.

Getting your data science code running anywhere

On-premise RAG without a GPU

Partitioning with nearly one assignment per moved value, instead of Hoare's one and a half

A document workspace that explains highlighted jargon in plain English for a 5-year-old, a high schooler or a college grad, built in Antigravity and running on GPT-4o.

ML model adoption doesn't stop with acceptance of accuracy; it starts there.

How technical choices can lead to safety failures, and what to do differently

The statistical paradox that shows how combining data can reverse the story.

Ten quantization guides, and not one threshold-dependent metric between them — what that gap hides, and the two numbers that close it.

How participant-aware evaluation changed performance across three machine-learning models

I rated my confidence 4 out of 5, then couldn’t find a planted bug in 8 minutes. Here is the tool, the run, and its limits.

Beyond basic ReAct loops: six production-oriented architectures for building autonomous, reliable, and scalable LLM agents.

How statistical moments connect the mean, the variance, and higher powers of a distribution

First in a series on probabilistic forecasting for physical signals. Next: what happens when you roll the forecast forward more than one step.

When Codex is the right shape for the problem, when Claude Code is, and how I split 5 specialist agents between them on dense AI capacity work.

Why the skill-inflation panic is aimed at the wrong thing, and what it costs to make agent knowledge a build artifact instead of a file.

A real Weave project that regression-tests three OpenAI models against the exact reply format your app depends on.