Jumping straight to XGBoost hides the problems in the data. Notes from a 32,581-row credit dataset: a heavy class imbalance, quartile cuts on age and income, and what a missing loan date does to model stability.
Grace periods for the EU AI Act and the Cyber Resilience Act run out in 2026, which moves data governance out of the legal team and into the server room. What that means for provenance, SBOMs and brittle pipelines.
Vector search handles synonyms well and loses exact identifiers. Notes on pairing BM25 with vector retrieval, merging both with rank fusion, and letting the agent retry when the results come back empty.
Cleaning the dataset will not stop LLM hallucinations. Inside a transformer the right token is there in the early layers, and the model rotates away from it in the middle ones. What that means for monitoring an AI feature in production.
A look at the quantum software stack as it stands: the three modalities you can program against, the layers from QASM up to Qiskit and Q#, and why the transpiler and the debugging story matter more than the hardware headlines.
Core bundles its own AI client in WordPress 7.0, so older AI Experiments builds fatal the moment you activate them. What is graduating out of experiment status, the LLM kill switch in Trac 64706, and how to load your own AI code without crashing.
Building an AI application is mostly plumbing: keys kept out of the repo, a plan for 429 responses, and a chunking engine for anything longer than the token limit. Notes on what keeps an AI feature running once it leaves your laptop.
Nobody trains a vision language model from a blank slate. You freeze a ViT, train a Q-Former to bridge image features into the text embedding space, and wrap the language model in LoRA. This is how those three pieces fit and where the GPU hours go.
A dashboard shows you what moved. These five methods tell you what caused it: doubly robust estimation, instrumental variables, regression discontinuity, difference-in-differences, and CATE, with the Python I run on live WooCommerce data.
Chaining agents multiplies errors instead of dividing work: DeepMind measured up to 17.2x amplification. Here is the math behind that, the three topologies that hold up in production, and the checklist I run before shipping an agentic system.