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.
AI now generates faster than any of us can type, which makes judging the output the actual job. Where LLM code tends to break in WordPress: hooks that no longer exist, uncached get_option calls in loops, and raw SQL that HPOS will punish.
Most machine learning projects fail on framing, not on model quality. Five checks to run before you open a notebook: name the decision, price the two kinds of error, audit the target variable, simulate the deployment, and write down how the project fails while the metrics look fine.
WordPress 7.0’s AI updates are heading into Beta 2. This post covers the pivot away from the Abilities API, the new Connector Screen, and the dependency issues in the AI Experiments plugin.