All journeys

I want to work in climate tech

The field is full of people with strong positions and no measurements. This route makes you the one in the room who brought numbers — about energy, about payback, and about what a system actually costs to run.

You end with: Measured energy and carbon figures for your own work, and a growing system that ran six weeks unattended.

Explore

Both halves of the argument: what is worth automating, and where the energy actually goes.

  1. The economics of automation — Why one job gets automated and the near-identical one next to it does not. (3 lessons, 65 min)
  2. Where the energy actually goes — Joules, not vibes. Measure what your own work costs before joining an argument about data centres. (3 lessons, 65 min)

Build

Local inference is where watts stop being abstract — this is the only course in the catalogue that makes you measure them.

  1. The eval you actually need — Stop reading leaderboards. Build the harness that measures your task, on your inputs. (4 lessons, 8 min)
  2. Local or nothing — Run open-weight models on hardware you own, and know what they cost you in watts and seconds. (5 lessons, 6 min)

Earn

Climate work is mostly funded work: a defensible cost model, and evidence somebody wanted it.

  1. Price the work — Scope it, cost it, quote it — and know the number below which the job loses you money. (3 lessons, 80 min)
  2. Validate it in 48 hours — Find out whether anybody wants it before you spend six weekends finding out they don't. (3 lessons, 65 min)

Launch

A closed loop in a growing system, where failure is measured in dead plants rather than in error rates.

  1. Build an autonomous greenhouse — A closed loop that runs for six weeks unattended, in a system where failure is measured in dead plants. (3 lessons, 100 min)