Back in 1985, computer scientist Peter Naur wrote “Programming as Theory Building”. According to Naur — and I agree with him — the core output of software engineers is not the program itself, but the theory of how the program works. In other words, the knowledge inside the engineer’s mind is the primary artifact of engineering work, and the actual software is merely a by-product of that.
The third golden age of software engineering – thanks to AI, with Grady Booch
The third golden age of software engineering – thanks to AI, with Grady Booch
The Gap Through Which We Praise the Machine
My current theory of agentic programming: people are amazing at adapting the tools they're given and totally underestimate the extent to which they do it, and the amount of skill we build doing that is an incidental consequence of how badly the tools are designed.
Agentic Engineering Management
To what extent AI is OK to use in software development might be debated, but in general, the idea is not a controversial one anymore. The debate rather moved on from code completion and simple PR summarizations to Agentic Engineering, where an execution loop allows an AI Agent to function
Agents
Intelligent agents are considered by many to be the ultimate goal of AI. The classic book by Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (Prentice Hall, 1995), defines the field of AI research as “the study and design of rational agents.”
Running an AI-native engineering org | Claude
How the Claude Code engineering team’s processes and structure changed once agentic coding became the default way of working.
Why AI hasn’t replaced software engineers, and won’t
Coding agents as normal technology
AI should help us produce better code - Agentic Engineering Patterns
AI should help us produce better code - Agentic Engineering Patterns
How Cursor is building the future of AI coding with Claude
The Eternal Sloptember
I’m calling it now, the adoption of AI agents into software development will be one of the most costly mistakes in the field’s history. Agents cannot program, and it’s taking longer and longer to realize that they can’t. They are a highly sophisticated statistical model designed to mimic the distribution of programming. The output is broken, but in a way that’s getting harder and harder to detect. Which is exactly what you’d expect from an increasingly accurate statistical model.
The Eternal Sloptember
I’m calling it now, the adoption of AI agents into software development will be one of the most costly mistakes in the field’s history. Agents cannot program, and it’s taking longer and longer to realize that they can’t. They are a highly sophisticated statistical model designed to mimic the distribution of programming. The output is broken, but in a way that’s getting harder and harder to detect. Which is exactly what you’d expect from an increasingly accurate statistical model.
From OSS to Open Source AI: an Exploratory Study of Collaborative...
AI development is embracing open-source paradigm, but the fundamental distinction between AI models and traditional software artifacts may lead to a divergent open-source development paradigm with...
On AI Coding and Its Discontents - Cal Newport
Back in January, I received a note from a senior software engineer in Silicon Valley. He described himself as an AI skeptic who became converted ... Read more
AI Code
As AI Coding Agents write more code, it's more important than ever that we're intentional about the code it writes.
How building software is changing at Anthropic
A deepdive on what’s changed in how the leading AI lab makes software. Ever more code review and testing is done by AI, two-pizza teams very much alive, and more. Details from inside of Anthropic
AI for coding works so well because code has a bunch of built in self checks that allow an agent trained with reinforcement learning to find what works and what doesn’t. This will not so easily translate to eg machine design until we have way better simulations or much more “embodied AI” (robots).
