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.
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.
Query optimization and neighbor expansion can add 40 to 50% to latency and cost for marginal quality gains. What the numbers say about when each add-on is worth shipping in a RAG pipeline, and when a naive baseline is fine.
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.
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.