How I built a Mandarin tutor on n8n: audio in through a webhook, transcription with pinyin, Gemini for the pronunciation feedback, and ElevenLabs for the voice. It runs under 1 euro a month.
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.
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.
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.
Manual prompt tweaking does not survive contact with vision models. A walkthrough of HRPO in the Opik-optimizer SDK on driving data: how the loop is wired, why you need a hold-out set, and the run that took accuracy from 15% to 39%.
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.
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.