Basic embeddings only get you so far. Ahmad Wael explains the two-stage pattern behind advanced RAG retrieval: a bi-encoder for recall, a cross-encoder for precision, and the caching tricks that keep the second pass off most of your traffic.
Asking a chatbot for a WooCommerce snippet is a lever pull, and the losing pulls are the hard ones to spot. Where LLM generated code breaks WordPress sites, what it does to coding standards, and why per-token pricing makes it worse.
Cowork gets written off as Claude Code for people who don’t code. I use it for architecture work and multi-file debugging, and this is how I keep the context window lean, why I stay in plan mode, and which repeat jobs I turned into skills.
Connecting Claude to a WordPress site over the Model Context Protocol turns your posts and comments into something it can read. Here is the setup, the gap-analysis prompt I use, and how I score the ideas that come back.
Standard vector RAG shreds documents into contextless fragments. Proxy-Pointer RAG keeps the heading hierarchy through skeleton trees and breadcrumbs, and on 66 queries against 10-K filings every number matched the ground truth.
Six things I ran into building a GPT-2 clone from scratch in PyTorch: the scaling flaw RsLoRA fixes, why RoPE replaced additive positional embeddings, why weight tying stopped paying off, and what KV-caching costs you in VRAM.
Notes from WordCamp Asia 2026 in Mumbai: plugins and themes landed on every paid WordPress.com plan, the speed build put AI against raw FSE work, and Nirav Mehta’s session covered where LLMs still break. Here is what held up as useful for real builds.
Claude Code is not only for refactoring PHP. Three things I run through it that never touch a codebase: LaTeX decks kept in Git, cross-app search over Slack and Notion, and prospect research.
LLMs forget your stack the moment you close the tab. Here is what actually sticks: a project rules file in the repo, global rules for your own preferences, and MCP when the assistant needs data from outside the codebase.
One LLM cannot draft emails, read documents and optimize a sales pipeline well. A look at splitting the work across reasoning, orchestration and execution layers, with TD learning doing the sequential decisions.