2,101 topics stand between an operations seat and an engineering one. I’ve checked 17.
Two checklists — machine learning and robotics — mapping everything I’d need to learn to do this work for real. Every box I check has a published explainer behind it, because a checked box with nothing to show for it isn’t evidence of anything.
A site about the distance between a demo and a deployment has no business hiding its own denominator. So this is the honest version: the size of the territory, my position in it, and the artifacts that prove I was there.
- Topics mapped
- 2,101
- Boxes checked
- 17
- Explainers published
- 16
- Percent of the map
- 0.8%
The Machine Learning Checklist
14 / 1158The maths, the tooling, the models, and everything downstream of them. Sections 1–8 are the common core; the rest is specialization.
Open the checklist →The Robotics Checklist
3 / 943Not a field so much as an intersection — mechanical, electrical, control theory, computer science, and increasingly ML, all meeting at a machine that has to work in real time without hurting anyone.
Open the checklist →One box checked, one explainer shipped.
Interactive, plain-English, and written for the person I was three months ago. If I can’t explain it clearly enough to publish, I don’t get to call it learned.
- 01
The map comes first
Both checklists were written before I started, so the denominator can't move to flatter the numerator.
- 02
A box is only checked when something ships
An explainer, a build, or a written teardown. Reading about a topic is not the same as understanding it, and only one of those leaves evidence.
- 03
The gaps stay visible
Nothing is hidden. You can see every topic I haven't touched, which is nearly all of them.
- 04
The floor is the lab
I work around industrial automation every shift. When the textbook and the deployment disagree, that disagreement is the post.