One working directory holds one state of the project, which is why you and your agent keep colliding. Git worktrees give each branch its own folder, and here is the script that pays the setup tax: env files, installs and a unique port offset.
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.
MareNostrum V is 8,000 nodes sitting in a Barcelona chapel, and getting code to run on it means SLURM time slots, airgapped storage and Amdahl’s Law. Here is what that setup teaches about scaling anything smaller.
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.
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.
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.
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.