Data scientist onboarding takes more than Snowflake keys. What the first 90 days should cover: context from the people around you, a working environment by day 5, and a starter project that earns trust.
Stephanie Kirmer, a staff ML engineer, on why the job leans on sociology as much as modeling, how code assistants took over the boilerplate, and why $200 billion of AI investment looks out of proportion to what the tech does today.
Tmux keeps your sessions alive when the connection drops. Sessions, windows and panes, the prefix shortcuts worth memorizing, a starter .tmux.conf, and why agents like Claude Code run on top of it.
AI Experiments 0.3.0 moves the plugin’s AI features into the editor itself: an AI Summary block, image generation in the post sidebar, and alt text on request. The developer side gets strict typing in the Abilities Explorer and cleaner asset handling.
GPUs sit idle during text generation because model weights move slower than the math runs. Nvidia’s TiDAR drafts tokens with diffusion and verifies them in one parallel pass, reaching up to 5.91x speedup on an 8B parameter model.
Notes from the recent AI Contributor meeting: a single Connectors screen for external service credentials, nested namespaces for Abilities, and whether any of it is ready in time for the WordPress 7.0 Beta 1 deadline.
Notes from the WordPress 7.0 product review meeting with Matt Mullenweg: a CSS-level admin refresh, a customizable dashboard, real-time co-editing over a pluggable transport, the AI Client and its settings UI, and PHP-only block registration.
Nine things I check when a WordPress site is not turning up in AI search: answer-first writing, topical clusters, JSON-LD schema, explicit authorship, and a robots.txt that is not quietly blocking GPTBot.
Lily Burton moved from biochemistry into science journalism and used the WordPress AI website builder to put a portfolio online in sixty minutes. Why shipping the prototype beat building the perfect site, and how to adjust the generated layout afterwards.
Linear RAG breaks down as soon as the solution path is not known up front. LangGraph swaps the chain for a stateful graph: a shared state object, nodes that update it, and conditional edges that pick the next step at runtime.