Bayesian work hands you densities you cannot integrate or invert. Metropolis-Hastings samples them anyway, with a random walk and an acceptance ratio that cancels the normalization constant. Includes the NumPy implementation and the three ergodicity conditions.
Vector infrastructure gets expensive fast: 100 million 1024-dimensional float32 vectors need over 1.2TB of RAM. Pairing Matryoshka truncation with int8 quantization cuts storage by roughly 80%, and this post covers where the savings stop and recall starts to break.
K-means assumes clusters are round, so it cuts the two moons dataset straight down the middle. Spectral clustering reads the data as a graph instead: build the Laplacian, take its smallest eigenvectors, cluster in that space. With NumPy code and notes on tuning gamma.
WordPress 7.0 Beta 3 keeps the AI client in core small and pushes the providers out to plugins: Anthropic, Google and OpenAI are on the repository already, with Ollama being prepped. Plus where the MCP adapter is going and what AI Experiments 0.5.0 drops.
Weighted BCE starves when fraud is 0.2% of your data. I walk through adding a differentiable rule loss in PyTorch, what it does to ROC-AUC, and why both models need thresholds tuned the same way.
Google Trends normalizes every score per region, so a 100 in the US and a 100 in the UK mean different things. Borrowing the index-basket idea from finance gives you an anchor and makes the numbers comparable.
Raw averages describe a world that was not reacting to you. This post uses the penalty kick problem to show why competitive data needs mixed strategies, and what that looks like in pricing, fraud detection and WordPress backends.
Vibe-checking search results is how teams end up rebuilding a backend for a system that scores worse than the SQL it replaced. What a real AI search evaluation needs: a golden set, repeated trials, and an ICC check before you ship.
AI infrastructure spending hit nearly $400 billion in 2025 against barely $100 billion of revenue. The skills that survive that correction are the classical ones: causal inference, experiment design, Bayesian reasoning, domain modeling and SPC.
A tool is one capability, a skill is the orchestration around it. Notes on writing agent skills in Python instead of Markdown, and where MCP fits when you build agents outside Claude.