WordPress 7.0 AI updates: what’s shipping in Beta 2
WordPress 7.0’s AI updates are heading into Beta 2. This post covers the pivot away from the Abilities API, the new Connector Screen, and the dependency issues in the AI Experiments plugin.
Tag: AI
Scaling ML inference on skewed Databricks tables
A 420-core Databricks cluster spent 10 hours on 18 partitions because the data was skewed. Here is how salting and liquid clustering fixed the distribution, and what each one is actually good for.
Context engineering is your only durable AI edge
Context engineering means filling an AI model’s context window with your own domain knowledge, using structured graphs, deterministic tools, and persistent memory instead of simple RAG.
Senior data scientist skills: it’s not about the code
Senior data scientist skills are often mistaken for mastery of algorithms. The real gap between junior and senior practitioners is judgment: knowing when to pause, how to frame the problem, and what actually moves the business.
Machine learning engineering lessons from a short February
February was short but it made a few things obvious: solo work stalls ML projects, code is not documentation, and an MLOps stack that ignores the deployment environment never ships. Notes from a month of ML and backend work.
7 reasons WordPress.com site ownership beats the DIY stack
After 14 years of stitching together WordPress hosting stacks, here is why I moved to WordPress.com: managed infrastructure, 99.999% uptime, real developer tools, and backups that actually restore.
Scaling feature engineering pipelines with Feast and Ray
Most feature pipelines are just Python scripts and CSV files until they break in production. This post covers using Feast for feature management and Ray for distributed compute to fix training-serving skew and cut latency.
Agentic RAG caching: reducing latency and token waste at scale
Agentic RAG setups waste time and money answering the same questions repeatedly. This post covers a two-tier semantic and retrieval cache, plus the validation logic needed to keep it from serving stale answers.
Production data architecture for real AI systems
Most AI features never leave the notebook. Notes on the production data architecture that gets them into a live site: separated layers, database integrity over raw insert speed, and the state and permission problems agents bring with them.
Sharpness-Aware Minimization: Why Flat Minima Beat Zero Loss
Sharpness-Aware Minimization finds flatter minima instead of chasing zero training loss, though it doubles training cost and can quietly break your BatchNorm stats if you’re not careful.