WordCamp Asia 2026: what the Mumbai schedule is telling us
The WordCamp Asia 2026 schedule is out: Mumbai, April 9 to 11, leaning hard on AI, enterprise scale, and the Interactivity API. My notes on which sessions are worth a slot in your day.
Tag: AI
A speaker guide for WordPress Core AI projects needs review
The Core AI team has a draft guide for anyone presenting on Abilities, the AI Client or the MCP Adapter at a WordCamp or meetup. Comments on it close on 21 April 2026.
Embedding models: how AI search actually finds a match
Embedding models turn text into coordinates, and that is what vector search is really matching on. This covers tokenization, chunking, cosine similarity and fine-tuning a model so it fits your own catalog rather than the general web.
Why I skipped the 8GB MacBook Neo for dev work
The $599 MacBook Neo pairs a fast A18 Pro chip with 8GB of soldered memory. I go through where that 8GB runs out during local development and data work, and the few people I would still recommend it to.
AI in WooCommerce workflows: the missing piece is context
AI tools guess at your store because they cannot see it. MCP gives them real context: your files, your data, your WP-CLI. We talk it through at WooCommerce office hours on April 15, 2026, on Slack.
The Google Memory Agent pattern replaced my vector DB
Pinecone and Chroma are a lot of machinery for a few hundred Markdown notes. The Google Memory Agent Pattern keeps structured memories in SQLite, consolidates them in the background, and loads them straight into a large context window.
The geometry behind the dot product: projections and unit vectors
Why dot product geometry matters if you build search relevance or recommendations. Unit vectors, scalar and vector projections, the shadow analogy, and a plain PHP cosine similarity function you can read.
Recursive reasoning: how a 7M model beat DeepSeek R1
A 7M-parameter Tiny Recursion Model beat DeepSeek R1 on the ARC-AGI benchmark by looping over the problem instead of scaling up. What that says about trading parameters for iterations.
Linear regression as a projection in column space
Why the column space view of linear regression beats the scatter plot: features as vectors, the intercept as a vector of ones, and the normal equation as the point where the error runs perpendicular to the feature plane.
Running Claude Code agents in parallel with git worktrees
Most people run one Claude Code agent and then wait for it. Git worktrees let you keep several going in separate folders on the same repo. How I lay that out in Warp or Tmux, plus the shared local database problem nobody warns you about.