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.
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.
Two years of daily use, boiled down: AI in UX is good at sorting research transcripts, flagging the obvious usability problems and drafting copy from client bullets. The judgment stays with you.
Small chunks lose context, medium chunks produce near identical similarity scores, large chunks are stable but noisy. How RAG chunk size affects retrieval, plus a PHP splitter that respects word boundaries.
Part 2 of the vibe proving series: how an RL loop built around a hard proof checker teaches a model to follow logic rules, and where it still falls over on nested proofs.
A client had ten thousand messy biographical records that needed to become a clean knowledge graph. GPT-4o would have done it and cost a fortune, so I used GliNER2 instead: schema-driven extraction that runs on a CPU.
A WooCommerce recommendation engine scored 0.85 AUC in staging and fell apart on launch day because the live audience did not match the training data. Inverse probability weighting is how I got an honest read on what the model was really doing.
Data work is moving away from generalists and toward people who know the domain, whether that is archaeology, renewable energy or sport. I got this wrong once on a solar reporting engine, and the fix was domain logic rather than a better query.
Predicting lead conversion from 800 WooCommerce rows, where XGBoost just memorized the noise. Why TabPFN and in-context learning made a better baseline than another afternoon of hyperparameter tuning.