AI-generated code ships fast and then stalls, usually around month three, when nobody can safely change the file it all lives in. A WooCommerce example shows the monolith version next to the decoupled one.
Classic RAG runs one retrieval pass and stays predictable. Agentic RAG keeps searching until it has enough evidence, and pays for it in latency and tokens. How I pick between them, and what breaks when a loop has no budget.
Demo agents fall over in production because errors compound across steps and prompts break when the business process changes. Structured output, LLM-as-a-judge evals and OKR alignment are what get a prototype across.
Traditional RAG chunking strips the context that makes a chunk mean anything. Contextual retrieval fixes that by prepending a one-sentence summary situating each chunk in its parent document, and prompt caching keeps the ingestion cost down.
Five ways to turn continuous variables into bins: equal width, equal frequency, domain-defined intervals, K-Means, and a shallow decision tree. What each one does to outliers and skew, with the scikit-learn and Pandas code.
No model is interpretable in the abstract. Interpretability is a set of methods for answering a question, and in production that question is usually diagnosis, validation or discovery. Includes a way to store explanation data in WordPress meta and transients.
One agent cannot learn routing, packing and weight limits at once. A hybrid MARL-LP setup gives reinforcement learning the fleet-level strategy and hands the hard constraints to a linear programming solver, which trains faster and generalizes across hubs.
AI handles static problems well and struggles with systems that keep moving, which is where operational scar tissue still pays. On static versus flux systems, the physical limits on adoption, and why judgment stays scarce as cognition gets cheap.
A walk through the YOLOv3 architecture: why Darknet-53 drops maxpooling for stride-2 convolutions, how the three detection scales work, and why the class heads use sigmoids instead of softmax. With PyTorch code for both blocks.
Standard DDP makes every GPU keep a full copy of a 7B model, roughly 112 GB of VRAM. ZeRO shards those states across the cluster instead, PyTorch FSDP implements it, and the price you pay is network bandwidth.