Machine learning at scale is an infrastructure problem before it is a modeling one: why availability beats accuracy, how label leakage fakes good metrics, and why heavy models and fallbacks belong on different hardware.
Intel’s Gaudi accelerators lost half their throughput on AWS DL1 because every byte detoured through host DRAM. This is how Peer Direct, libfabric and DMA-BUF put that data path back on the network.
Quantum machine learning is not fast AI. It changes how data is represented, from bits and floats to amplitudes you can only read by measuring, and most quantum-inspired pipelines are not QML at all.
AI Experiments 0.4.0 puts prompt-based image generation in the Block Editor and the Media Library, and adds a Generate Review Notes pass for accessibility and readability. It also points at the WordPress AI rename in 0.5.0.
LatentVLA drops natural language reasoning for self-supervised latent action prediction, using VQ-VAE codebooks and distillation to hit real-time speed. It also explains why open-loop NavSim scores flatter these models.
Running OpenClaw on bare metal, skipping the system prompt, and handing it admin API keys are the three setups that go wrong most. What to do instead, with a docker-compose file and a scoped IAM policy.
Claude Skills keep instructions in files the agent reads only when needed, which cuts the token overhead of always-on MCP tools. Subagents push it further by throwing away their own context once the job is done.
AI-generated code ships fast and then stalls, usually around month three, when nobody can safely change the file it all lives in. A WooCommerce example shows the monolith version next to the decoupled one.
Classic RAG runs one retrieval pass and stays predictable. Agentic RAG keeps searching until it has enough evidence, and pays for it in latency and tokens. How I pick between them, and what breaks when a loop has no budget.
Demo agents fall over in production because errors compound across steps and prompts break when the business process changes. Structured output, LLM-as-a-judge evals and OKR alignment are what get a prototype across.