A 2026 roadmap for learning Python for data science without the tutorial detours: which fundamentals actually matter, the three libraries you will live in, and how much time to give data structures before interviews.
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.
Vector databases have become the default answer for AI agent memory, and for a project-scoped agent they are usually the wrong one. Plain Markdown files plus a local SQLite index give you grep, git diff and no server to babysit.
LLM agent memory architecture is what decides whether an agent holds up over a long task. This covers the four temporal scopes, the manage step almost everyone skips in the write-manage-read loop, and how summarization drift creeps in.
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.
Deep Evidential Regression has the network predict Normal Inverse-Gamma parameters, so one forward pass separates noise in the data from what the model never learned. The awkward part is tuning the regularization term.
Qiskit, PennyLane, Cirq and Amazon Braket are built for different jobs. What each one is good at, where each gets in the way, and how to choose without stalling the project for a week.
Linear regression handles more WordPress and WooCommerce work than the plugin marketing suggests. The math, RMSE against MAE, PHP inference without a Python bridge, and where regularization saves you.
WooCommerce ran a bug blitz: support engineers, not the core team, shipped more than 170 fixes across 20-plus products in a few weeks, helped along by Claude Code and a purpose-built dev environment skill. Here is how that workflow held together.
Reinforcement learning in Unity treated as state management rather than magic: the Bellman equation, Q-values for state and action pairs, the learning rate, and the epsilon-greedy trick that stops an agent from settling for a good enough path.