Price hikes often get blamed for churn that is really about a finished project. This covers flagging project milestones in WooCommerce Subscriptions and using DiD and Shapley values to tell a price shock apart from value exhaustion.
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.
Rare event forecasting breaks most models because they optimize for accuracy instead of impact. Here is how a multi-head Transformer, the Generalized Pareto Distribution, and the True Skill Statistic model the tail, with solar flares as the running example.
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.
How I stopped re-explaining my projects to Claude every session. I keep a persistent knowledge base built from CLAUDE.md rules and a custom skill, and a nightly cron job keeps it current.
My reproduction of the Maas et al. (2011) word vectors for sentiment analysis paper: why semantic context alone misses polarity, and how a hybrid objective plus a linear SVM gets the accuracy back.
Reasoning models think before they answer, and those hidden tokens land on your compute bill. Here is why inference scaling is an architectural trade-off, and how a simple routing policy keeps it from wrecking your margins.
The four places an autonomous agent gets attacked (prompt, tools, memory, and the planning loop) and how to lock down a WordPress and PHP backend with least privilege, schema validation, and reasoning logs.
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.
Plugging in the OpenAI API is not engineering. Notes on what actually matters when you build LLM features on WordPress: token costs, attention limits, RAG over fine-tuning, and keeping a 30-second API call from taking down PHP-FPM.