Updated 6d
Updated 6d
When "technically true" becomes "actually misleading"
Worst Take of the Week: Tyler Austin Harper, please download Claude Code
Domain Expertise Has Always Been the Real Moat
The hard part of writing software has never been the writing. It was building a working model of the domain in your head first. Before you could ship a payroll…
The Economic Benefit of Refactoring
Notes from my Thoughtworks colleagues on AI-assisted software delivery
The New Software Lifecycle
I co-wrote a Google whitepaper about how AI is changing the software lifecycle. I'm not going to summarize the whole thing. Instead, here are the handful of ...
Why you need a "WTF Notebook"
There's a very specific reputation I want to have on a team: "Nat helps me solve my problems. Nat get things I care about done."
108 PRs in eight days: Accidentally discovering loop engineering | Brittany Ellich | Offprint
How I shipped 108 PRs in eight days with an agent working my task board: the loop engineering setup, constraints, and lessons that made it work.
The Arguments Against Open Source AI are Very Bad | Tom Bedor's Blog
The release of Kimi K3 has opened a fresh round of angst and confused discourse. There's a loud cohort of journalists, business leaders, and politicians arguing that open source AI is a dangerous threat. OpenAI's Dean Ball:
AI Coding will Prevent Expertise | Lars Faye
The need for ongoing friction in long-term skill formation.
Cognitive Surrender
Cognitive offloading is delegating to the AI and still owning the answer. Cognitive surrender is when the AI's output quietly becomes your output and there i...
The most important thing when working with LLMs
Blog post: The most important thing when working with LLMs by Steve Klabnik
Arvind Narayanan (@aisnakeoil)
There’s a big, under-appreciated reason why people may have very different experiences and opinions about using AI for work — are they using it for tasks they’re already an expert at, or tasks they can’t do themselves? The former leads to a growth cycle and the latter leads to a dependence spiral. When I use AI to do something I’m an expert at, like coding, I treat it as a tool. I can build quickly, maintaining an understanding of the code, knowing that if necessary, I can fix the code myself. It feels empowering. It frees up my time to think about the complex, judgment-oriented parts of software engineering that I can’t or won’t delegate to AI. That means my own skills improve rapidly, and I get to climb the ladder of complexity and develop higher-level skills, much more so than when I write the code myself. I feel in control. I can lock in and achieve a flow state — when AI is working, I’m reviewing, building understanding, and planning the next steps. I never get the feeling that the tool is about to replace me. This is the growth cycle. (Of course, the growth cycle is not automatic. I still need to exercise agency to use AI responsibly. But it’s the same challenge with any productivity-enhancing technology, and those who’ve navigated such transitions before are well-equipped to navigate it with AI as well.) On the other hand, if I use it for tasks I don’t understand and haven’t learned to perform myself, I have no choice but to treat it as a superintelligence. If something breaks, the best I can do is ask AI to fix it and hope for the best. I generally can’t evaluate the quality of the output myself. The only way to find out if it's any good is if and when the work is ultimately reviewed by an actual expert. The experience is confusing, unsettling and disempowering. And forget about flow state. By over-relying on AI, I risk losing whatever skill I had at the task in the first place, even if it boosts productivity in the short term. This is the dependence spiral. It’s no wonder that entry-level workers and students preparing to enter the workforce find themselves in a bind. To compete with the AI-enabled productivity of more seasoned workers, they must adopt AI themselves, but doing so risks the dependence spiral. I have some thoughts on solutions that I will share in later posts, but I think having a clear diagnosis of the problem is a useful first step.
AI-Generated UI Is Inaccessible by Default
It doesn't mean you can't get AI to help with accessible code, you've just got to know what you're doing.
AI-assisted engineers are burning out, is this fine?—Martian Chronicles, Evil Martians’ team blog
AI-assisted code generation is not free. It comes with a hidden cost: burnout. Are we dangerously ignorant to this problem? And how can we cope with it? In this post, we discuss this question.
The machines are fine. I'm worried about us.
On AI agents, grunt work, and the part of science that isn't replaceable.
Agentic Coding is a Trap | Lars Faye
Remaining vigilant about cognitive debt and atrophy.
The future is low-tech: lessons from the early 2000s ft. Jésabel DC | Config 2026