
Apple MLX vs NVIDIA CUDA: Same Problem, Different Machines
A practical comparison of Apple MLX and NVIDIA CUDA, where they overlap, where they differ, and how those differences should shape your choice.

A practical comparison of Apple MLX and NVIDIA CUDA, where they overlap, where they differ, and how those differences should shape your choice.

Use explicit acceptance criteria, test results, risk-based review and production feedback before accepting AI-generated changes.

Why many apparent multi-agent gains are really test-time compute gains, and when extra agents are still worth the complexity.

When machines master the ordinary, humans are freed to pursue the extraordinary. From photography to AI music to vibe coding, the same pattern repeats.

AI coding tools and autonomous agents are shipping faster than the guardrails meant to govern them. Here is where the risks are and what thoughtful adoption looks like.

A practical guide to running AI coding agents safely and efficiently in a monorepo, including the wins, the configuration changes, and the common failure modes.

Engineers judge AI coding differently because its value depends on tests, review capacity, code ownership and how teams develop technical skill.

Why many teams use agile tooling and ceremonies but still deliver in large, slow, phase-gated batches, and how to get back to real fast-feedback delivery.