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The hardest thing with intensity is narrowing the focus, which Slootman notes as important. The best intensity is extremely focused. Which, ironically, in my experience, doesn't even feel that intense in the moment, time just flies.

Yeah, for sure, but at least to me, it feels like we will figure that out. We're in a transitionary phase. But it also seems to me like, to your point, we're busier than ever, and in a lot of ways, the problems we're working on are harder than ever.

The US is in debt to itself, in a currency it can print. What could go wrong?

I'm wondering if mathematicians are sort of going through what programmers have been going through recently, really questioning whether their craft can continue. But now it feels like, in the software world, at least from my seat, people can handle much larger scope and work on much more complex problems. If anything, my job has gotten a lot _more_ interesting.

So I wonder if mathematics will go through that too? A period of chaos and doubt, but finding new and more effective ways of working.

The Quakers are pretty chill.

This is a weird article.

If anything - it seems that AI is _attacking_ the things the elite have. Elite people have private tutors/expert networks, now people do too for $20/month. What if the cost of education collapses to zero? It already sort of was - I learned to program on a ~$1,000 computer while Google engineers had access to $100s of millions of hardware, and it was enough.

Also, open-weight models are powerful and relatively cheap to run. So it's not like OpenAI can just close the doors (short of extremely strict regulation which seems to be a lost cause).

Does anyone do approaches where developers run CI locally on their machine and then somehow push up signed or authenticated artifacts?

Many medical practices (and other businesses) are poorly operated and administered. I think of my dentist: terrible website (even by 1995 standards), awful at follow up, weird insurance coverage (since she doesn't have time to follow up with new plans it seems) and almost no appointment reminders. There are obvious things to do that could drive business for her.

It makes sense to me that someone could come in and say "hey, let me run the business + finance side of the house while you practice medicine" and at least on paper I can see a real world where that works out for everybody.

Of course, soon you end up with dentists pushing unnecessary procedures and more, so it doesn't always works out that way.

Maybe a tangent, but I've gotten a lot of the joy back with 3D printing. You can just describe something, and Astra can model it in CAD extremely well. It usually takes a few iterations, and then you can just print it. I found that before, it would take days to build even simple models in Tinkercad - which while fun, was high enough friction that a lot of things would stay in the "ideas" folder - now I can have something real in an hour or two.

I still make time to write code by hand.

But I think it's really about finding the right abstraction. As a staff or principle engineer in the beforetimes, I also didn't understand most of the code being written by the org. I dove in where needed, but more importantly I found the right layer to understand what was going on - dependencies between teams and services, data flows and data models, etc. And where I found myself most effective was when I was shaping and understanding those effectively.

I don't know. We've managed to avoid most of this, I think. We have tech debt, but it feels more like regular tech debt than sloppy engineering practices. What's worked for us:

- Very strict linting, type checking, and so on.

- An opinionated flow of data and clear layers in the codebase.

- Good code review workflows with multiple AI code reviewers, along with a pretty opinionated SDLC.

- Agents that look for duplication and opportunities to simplify.

- Strong opinions about our core data model.

I really think that the companies that are struggling with slop probably struggled with slop before. It just moved slow enough that things could be fixed.

It's my own ignorance speaking, but is this as true in math? I could see this being a problem for programs where you have few, if any, real axioms and the axioms themselves change. But if we're talking math, the axioms should be fixed.

For what it's worth, I really enjoy superpowers. In particular, it does a great job with TDD that stops the model from jumping to conclusions, and I've been able to get it, even with Opus, to execute on much longer specs quite well.