Over the past month I’ve been doing a crash course on machine learning as I come up to speed on AI progress. In 2025 I had a child, and between the pregnancy leading up to it, a fun project at my day job, and all the dirty diapers since I have been paying a lot less attention to the field than is warranted. Claude Opus 4.5 burst this bubble - it’s now agentic enough to be a Software Engineer I or II. Scaling laws haven’t broken down, we’re very close to AGI, it’s time for me to catch back up.

Learning machine learning has been very fun - LLMs fundamentally have shifted what learning looks like, and it’s been refreshing to play around unshackled by the normal levels of friction I’ve been used to my entire life when learning a new subject.

Particularly experiments are a lot faster now - before Claude Opus 4.5 I had to spend numerous hours on a side project to make it work. With an intensive day job, and now a wonderful baby, this became impossible. But Claude lifts this constraint away - it still takes effort to produce a good project. But it takes a lot less, and I can do it in between diaper changes and reading books.

I’ve landed on a good learning regimen:

  1. Learn - read papers, watch videos, explore side tangents and unknowns with language models As you go take notes of all the ideas flowing in your head
  2. Build - after learning new things, build something! Use claude code, you don’t have to do it yourself but explore a problem space and build a small project to understand better.
  3. Write - finally blog about it. Writing both serves to refine my own thinking, as well as share my progress with the world.

It can be easy to get stuck in just one stage of the loop, but I find all steps of the loops valuable as I learn through a new field.