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.
Notes from WordCamp Asia 2026 in Mumbai, written for developers: the Interactivity API replacing fragile jQuery toggles, the Tag Processor in the HTML API, and what WordCamp India 2027 and the 7.0 roadmap mean for the code you ship.
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.
A p-value is closer to a weirdness score than a probability that you are right. Here is how to read one, why the 0.05 threshold is arbitrary, and how to sanity-check sample size before you act on an A/B test.
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.
WooCommerce 10.7 drops HPOS order REST queries from 271 to 132, ships a beta Order Fulfillments API with typed methods, caches the Store API Last-Modified timestamp, and stops sanitization from eating % signs in gateway passwords.
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.
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.