How I get Claude Code to improve itself overnight
I stopped hand-holding Claude Code and gave it a nightly review instead: a self-reflection skill, a 2 AM cron job, and a rules file it edits itself. This is the setup I run on real WordPress work.
Category: AI in WordPress
Why agentic AI systems fail in WordPress production
Most AI pilots never make it past the demo. These are the five kinds of production debt that kill agentic AI systems in WordPress, from brittle prompt handling to the legal review nobody booked.
6 AI production trade-offs that show up after launch
Six trade-offs that decide whether an AI feature survives production: what a cheap API really costs at scale, why a complex model gets expensive 18 months later, and when batch inference beats real-time.
WordCamp US 2026 tickets are on sale for the Phoenix event
WordCamp US 2026 runs August 16 to 19 at the Phoenix Convention Center. The schedule covers Contributor Day, Showcase Day, and two days of sessions on WordPress and AI. Ticket tiers run from $25 for students to $750 for micro-sponsors.
Why your AI coding assistant drifts into the wrong language
Mixing technical English tokens like run.py can push an AI coding assistant to drift in the embedding space, sometimes replying in Korean when prompted in Chinese. How task registers shape the behavior, and how to keep your prompts grounded.
LLM evaluation for WordPress AI agents, beyond vibe checks
Vibe checks do not scale. Here is how to score a WordPress AI agent on five measurable dimensions, accuracy, reliability, latency, cost, and business impact, using golden datasets and the LLM-as-a-judge pattern to move from fragile demos to production.
Hybrid search and re-ranking for more accurate RAG
Semantic search alone misses exact technical terms. Combining BM25 with dense vectors, then re-ranking the top results with a cross-encoder, is what made our production RAG accurate. RAGAS metrics show the difference.
B2B document extraction: why I built it twice (regex vs LLM)
PDFs are where automation goes to die. I built the same B2B document extraction pipeline twice, first with regex and then with a local LLaMA 3 model. This is where each approach wins, where it breaks, and when the extra cost is worth it.
When to scale AI to a multi-agent system
Moving from a single AI agent to a multi-agent system pays off once workflows get complex. This covers the ReAct loop, specialized roles like retriever, writer, and verifier, and when to drop the monolithic prompt to cut hallucinations.
Agentic AI optimization and the end of guess-the-bottleneck
Manual performance tuning is mostly hypothesis and hope. Agentic loops like pi-autoresearch take a metric and a budget, run the experiments themselves, and keep only the versions that measure better.