The Inception score compares two probability distributions to judge GAN output, so a high number can still hide mode collapse or an ImageNet mismatch. Here is how the KL divergence works and why the score belongs next to FID and human review.
How I use Claude Code with WordPress Studio to scaffold plugins: setting up a local site and terminal, installing the CLI, the context a prompt needs, and why the naive admin notice it hands you is an XSS bug waiting to happen.
Agentic analytics moves the consultant’s job from writing queries to architecting the systems agents read from. What that looks like in practice, including a clean WordPress REST endpoint built for agent consumption.
How I set up Claude Code context: local docs and meeting transcripts in a /knowledge folder, a .claudignore that keeps the agent away from secrets, and a bash script that bundles only the files a given feature needs.
Voyager 1 still self-corrects on 69.63KB of memory. That comparison is the frame for quantization, knowledge distillation and TinyML, plus a small PHP example of how weight quantization actually works.
Andrew Adetitun had more than 160,000 followers and no platform of his own. He is a former WordPress developer, and he still used the AI website builder to get The King’s Monologue online fast, then pointed it at his Kickstarter book.
A three-step way to point an agent at a broken WordPress site: let it read debug.log, make it write a hook-level plan before it edits anything, then iterate on what it ships.
Notes from the February 4 contributor meeting on the WordPress AI Client merge into 7.0: why OpenAI, Google and Anthropic are moving out to separate plugins, why the off switch is a constant rather than a filter, and what MCP adapters mean for the Abilities API.
WordPress.com now has an official Claude Connector built on MCP and OAuth 2.1. It gives Claude read-only access to site data like traffic, comments and content, with no custom REST bridge to maintain. Here is what it can do, how to switch it on, and where it stops.
Part 2 of the SageMaker vs Azure ML series, this time on compute. Azure keeps clusters as persistent workspace assets while SageMaker spins them up per job and tears them down after, plus spot instances, checkpointing and container control.