Why supply chain data science beats another LLM wrapper in 2026: heatmaps before neural networks, Lean Six Sigma for diagnosing processes, PuLP for network design, and Streamlit so the model actually gets used.
WordPress 7.0 drops PHP 7.2 and 7.3 support. What the new floor lets core use, where the filter extension debate stands, and how to check for the extension before it throws a fatal error.
The January 2026 core performance chat: speculative loading may default to moderate eagerness on cached sites, the admin view transitions PR is stuck on E2E failures, and modern image formats still have rough spots.
Vanilla vector search breaks down as soon as your data is relational. Notes on GraphRAG, enforcing a data contract on LLM output in PHP, and the production RAG lessons worth stealing from the Towards Data Science write-ups.
Polynomial features go wild at the edges of your data. Scikit-Learn’s SplineTransformer keeps the fit local: how to build the pipeline, tune the knot count with cross-validation, and use periodic extrapolation on cyclical features.
Most LLM agents ship with no visibility between the input and the answer. How the NeMo Agent Toolkit traces every tool call and scores trajectory rather than answer accuracy alone, using Arize Phoenix and W&B Weave.
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.