I stopped retyping the same AI prompts and moved them into a commands folder. How slash commands work in Cursor and Claude Code, the two I use most on WordPress projects, and why that folder belongs in Git.
Penpot is testing an MCP server that lets assistants like Claude read real design files instead of guessing from your prompt. What is in the experiment, how the translation layer works, and why design-as-code cuts down on invented components.
Three ways to get rows into Postgres from Python and what each one costs: the SQLAlchemy ORM, SQLAlchemy Core, and Psycopg3 COPY. Includes the refactor that fixed a memory leak at 100,000 rows and a rule of thumb for choosing between them.
Recall in an HNSW index slips as the vector database grows, and nothing in your logs will tell you. What ef_search actually does, why a Flat search makes a useful baseline, and when to pre-filter on metadata instead.
Notes on four Advent of Code 2025 puzzles where the obvious answer falls over: splitting beams, junction boxes, factory switches and reactor paths. The fixes are memoization, Union-Find, MILP in SciPy and an explicit DFS stack.
The difference between data drift and concept drift, the two tests I reach for first (K-S and PSI), and how I wire the check into a WP-CLI command so a drifting model triggers an alert instead of a bad quarter.
Agents pick the tool with the longer description, not the right one. What that length bias looks like in a WooCommerce integration, how to keep internal arguments out of the prompt, and why a proxy beats editing someone else’s MCP server.
Three habits that stop a coding agent inventing hooks and tables: an AGENTS.md file it has to keep updated, real documentation and schema files in the context, and a fresh thread when the task changes.
The Plugins Team 2025 retrospective puts submissions at about 330 a week and reviews up 40.6%, with the same size team. Here is what the automated scans catch and how to run the Plugin Check plugin before the reviewers do.
A neural network trained on the Mandelbrot set comes out blurry because of spectral bias. Multi-scale Gaussian Fourier Features fix that by putting the high frequencies into the input instead of asking the model to find them.