11 min read
AI Coding Adoption Depends on the Engineering System
AI coding tools improve delivery only when teams have clear rules, fast feedback, small changes and enough review capacity.
Blog posts tagged
SOFTWARE ENGINEERINGPage 3
10 min read
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.
6 min read
Require Evidence for AI-Generated Code
Use explicit acceptance criteria, test results, risk-based review and production feedback before accepting AI-generated changes.
7 min read
Why Single Agents Often Beat Multi-Agent Systems
Why many apparent multi-agent gains are really test-time compute gains, and when extra agents are still worth the complexity.
10 min read
The Automation Avant-Garde
When machines master the ordinary, humans are freed to pursue the extraordinary. From photography to AI music to vibe coding, the same pattern repeats.
6 min read
We Gave AI the Keys to Production. Now What?
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.
16 min read
AI agents in monorepos: what to configure differently from a single-product repo
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.
3 min read
AI Coding Divides Engineers Over Trust and Ownership
Engineers judge AI coding differently because its value depends on tests, review capacity, code ownership and how teams develop technical skill.