A house-pricing model with zero training error valued a suburban shack at $2.4 million. This is the geometry behind lasso: centering the data, the L1 diamond, and why landing on a corner zeroes a coefficient.
Studio Code Beta is a CLI coding agent for WordPress, built on Claude Code. It runs WP-CLI, spins up local sites, screenshots its own work, and validates block markup against the editor before inserting it.
Naive before-and-after averages said the London tube strikes lifted bike usage by 5.5%. A two-way fixed effects model with clustered errors put it at 3.95%. This is why the second number is the one to trust.
Academic causal inference stalls in applied work. Decision gravity is the fix: constructive decisions can live with an associative read, final ones cannot, and collider bias is what catches the people who guess wrong.
Vibes-checking AI output is the new bottleneck. What actually moved my Claude Code performance: terminal permissions for the agent, a test requested with every feature, and a no-stop rule until they pass.
Prefill is compute-bound, decode is memory-bound, and running both on one GPU wastes most of it. Splitting them into separate pools, the KV-cache transfer tax that comes with it, and when it pays off.
A synthetic dataset can pass every standard check and still lose the correlations, rare events and privacy margin that your model depends on. Here are the three checks I run before shipping one, with the Python for two of them.
A static A/B test keeps sending half your traffic to the losing variation until someone stops the test. Thompson sampling moves the traffic as the numbers come in. Here is the Beta distribution behind it and how to store the state in WordPress.
My Clippings.txt is an append-only mess, and the expanded highlights inside it will wreck any summary you generate from the raw file. Here is the parser, the deduplication logic and the local Ollama call I use instead.
An ad hoc prompt works once, then quietly stops matching the result you got the first time. Claude Code Skills wrap the workflow in a SKILL.md file, with structured JSON for the data and Python for the parts that have to be deterministic.