For the senior engineer carrying the verification tax — and for the leader trying to turn private AI usage into a capability the company can actually see.
Thirty minutes. No deck, no script. We talk about where your team really is with AI, what you're trying to reach, and whether what we do is a fit. If it isn't, you'll leave with a straight answer and a few ideas anyway.
You'll be talking to Troels — the person who built the training — not a sales rep.
Most people on this call come from one of two seats. Both are welcome. The conversation adapts to the seat you're in.
Choose a time below and you'll get a calendar invite with the video link straight away. Need a slot that isn't listed? Email [email protected].
Thirty minutes is someone's attention — yours and ours. Here's an honest read for each seat.
Maybe skip it if you're after a single personal seat — the free guides are a better place to start. Also skip if you want a vendor who'll promise 10× and a number for your slide.
Four beats. Same arc the call always follows — pain, picture, gap, path — read from whichever seat you're in.
For engineers: the verification tax — plausible output, missing context, edge cases, security details, hidden assumptions that quietly become your problem.
For leaders: the review burden and hidden risk behind the seat licenses, demos, and "AI productivity" anecdotes.
For engineers: who you want to be as code generation gets cheap — judgment, specs, verification, recovery, repo readiness.
For leaders: what visible AI capability looks like — clearer specs, sharper review, fewer production surprises, a story you can tell without theatre.
For engineers: session-by-session habits vs. a repeatable system you could hand to a teammate — and why scattered blog posts don't close it.
For leaders: private experimentation vs. an operating discipline you can govern, review, and explain — and why tool rollout alone never gets there.
For engineers: whether the system fits your stack, what a 30-day rollout on one real repo looks like, and what it would cost to keep things as they are.
For leaders: cohort size, scope, cost, timing, and who else needs to be aligned before this becomes real.
Background in quantum computing research, compiler development, and blockchain infrastructure. Has spent the last several years building production software with AI — most of it livestreamed, five days a week.
Talks with engineering leads about repository structure, context engineering, evaluation, and recovery. Talks with CTOs and CEOs about turning private AI usage into a teachable, governable operating capability.
The training your team would take is a distillation of what actually works on that screen. The call is a chance to ask the person who wrote it whether it's right for you — directly, with nothing in the way.
Thirty minutes, a straight conversation, and a clear next step. That's the whole offer — whichever seat you're in.