Running more environments in parallel does not scale RL training, it just leaves the learner waiting. This post covers the actor-learner split, using Redis to move trajectories between them, and how V-trace corrects for stale off-policy data.
Most plugin and hosting picks come down to one benchmark or a hunch. This walks through building a decision matrix, reading the efficient frontier, and a short PHP function for weighted scoring.
Siloed training data produces models that fail confidently. This walks through the Federated Learning Flower framework on a biased MNIST setup, the pyproject.toml config, and the jump from 65% to 96% accuracy.
Prompting an LLM for plugin and WooCommerce code ships fast, then leaves you debugging plumbing you never wrote. Where vibe coding falls apart in WordPress, and what the vetted version of the same function looks like.
Make WordPress published its AI guidelines: disclose AI assistance, keep contributions GPLv2-or-later, and own the code either way. Includes the AJAX save function AI keeps getting wrong and the version that passes review.
WordPress 6.9.1 RC1 closes 23 core bugs and 25 Block Editor issues, including the mail function error, the double-escaped class names from the HTML API, and the broken Add Plugins screen. Plus how to test the RC on staging with WP-CLI.
How I use Cursor, Copilot, and Claude on real client work: mapping an unfamiliar legacy codebase, moving a decade-old build stack to Vite, and getting tests that cover failure modes instead of just the happy path.
Unbalanced cohorts do not invalidate an A/B test. What matters is that assignment was random and independent of anything that existed before the test. Includes why imbalance happens and a hash-based way to assign groups in PHP without a database lookup.
Data science still gets treated as an academic exercise. Engineering constraints, caching, timeouts and safe fallbacks are what turn a model into something you can actually run on a client site.
Nested boolean masks are hard to read and harder to debug. Cleaner ways to filter Pandas DataFrames with .query(), .isin(), .between() and the .str accessor.