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9 posts
AI coding tools feel transformative, but the productivity evidence is mixed. Part 2 of the LogCTL series on AI-native engineering examines the gap between perceived speed and measured outcomes, and why review is becoming the new bottleneck.
AI-native software engineering is not defined by how much AI a company uses. It describes a development model in which AI is treated as a primary participant in software construction rather than an occasional assistant.
AI-native engineering is not defined by removing humans from software development. Part 5 of the LogCTL series examines how roles, teams, economics and accountability change when implementation is no longer the dominant activity.
A useful AI-native workflow requires more than a better prompt. Part 3 of the LogCTL series maps the practical method: specifications, context engineering, agent workflows and the harness that enforces permissions, boundaries and stopping conditions.
One agent can misunderstand a requirement. Several agents can misunderstand it in different ways. Part 4 of the LogCTL series on AI-native software engineering maps coordination, multi-agent orchestration, layered verification and governance at scale.
An opinionated workflow for building good-enough software with AI coding agents without surrendering architecture, judgment, or security.
A subjective comparison of Claude Code, Codex, Cursor, OpenCode, and the models inside them, based on ordinary application work rather than benchmarks.
The pursuit of becoming a 10x developer through context-switching across six projects, multiple languages, and AI agents — and the quiet realization that 1.5x is honest work.
Frontier models are brilliant, but many everyday coding tasks run just fine on smaller models — if your workflow is clear. Stop buying the hype and choose tools for your actual use case.