Let AI worry about the code, you worry about the work
Cultivating LLM productivity requires comfort with the vernacular of code, not the writing of it.
software engineering
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The right answer has to be woven
Domain-driven design, content architecture, and AI context engineering each solved the same shared-understanding problem; LLMs are finally dissolving the boundary that kept them apart.
Four gears of AI-assisted development: from coding to context
Developers are moving from copy-paste to orchestration. Each gear shifts what the human actually contributes.
Why context engineering was always the job
AI knows the world in general but not your domain; the engineers who get real results were always doing the context work.
The ladder was always context
Matteo Collina splits software engineering into three tiers (tech firms, enterprises, small businesses), and in each the bottleneck shifts from building code to holding context.
Comprehension debt, when AI-generated code outpaces human understanding
AI-generated code is outpacing human understanding, and teams need to self-govern before someone else does.
Large projects need large communicating
Ben Kuhn shares his playbook for managing complex projects at scale, from maintaining detailed plans to delegating management itself.
Why AI can't be debugged like traditional software
Boyd Kane explains why treating AI like debuggable code misses the fundamental difference in how these systems work.
Plenty to learn from Fedora's approach to AI code
The first community-driven policy I've seen that treats AI as a tool requiring human accountability, not a shortcut.
It's true, short disruptions costs lots of time
Meeting creep, quick questions and context switching crush flow.