When machine learning matters

Writing code for a computer is hard enough. You take something big and fuzzy, some large vague business outcome you want to achive. Then you break it down recursively and think about all the cases until you have clear logical statements a computer can follow.

As I am en route to see my first total solar eclipse, I was curious how hard it would be to compute eclipses in Python. It turns out, ignoring some minor coordinate system head-banging, I was able to get something half-decent working in a couple of hours.

CIA produced a fantastic book during the peak of World War 2 called Simple Sabotage. It laid out various ways for infiltrators to ruin productivity of a company. Some of the advice is timeless, for instance the section about “General interference with Organizations and Production”:

Long story short: I'm working on a super cool tool called Modal. Please check it out — it lets you run things in the cloud without having to think about infrastructure. Scaling out, scheduling, containerization, using GPUs, setting up webhooks, and all kinds of other stuff.

This is is in many respects a successor to a blog post I wrote last year about what I want from software infrastructure, but the ideas morphed in my head into something sort of wider.