Five values that stay with you after the course

A complete chain of working with an agent: from idea, through plan and implementation with tests, to review and deployment - all tied together by 10x-cli, the tool that installs the course skills (ready, repeatable procedures for an AI agent) in your environment. Participants value it as a coherent system, not a pile of prompts - and like the Seldon Plan from "Foundation", it keeps working without its author: at your company, with no course behind your back.

A complete chain of working with an agent: from idea, through plan and implementation with tests, to review and deployment - all tied together by 10x-cli, the tool that installs the course skills (ready, repeatable procedures for an AI agent) in your environment. Participants value it as a coherent system, not a pile of prompts - it keeps working at your company, with no course behind your back.

The best evidence is what graduates do after the course: they bring the workflow into their companies and build their own tools on top of it - skill forks tailored to the way they work, team distribution repositories, code review pipelines built on the course method.

The market sells AI as pure acceleration. But code generated without structure soon becomes like the ocean of Solaris: alive, productive and less and less comprehensible to the people circling above it. A structured workflow operates on four levels at once: faster (less manual work), better (tests and verification at every stage), calmer (a predictable process instead of chaotic prompting) and with your understanding of the system intact.

The market sells AI as pure acceleration. A structured workflow operates on four levels at once: faster (less manual work), better (tests and verification at every stage), calmer (a predictable process instead of chaotic prompting) and with your understanding of the system intact.

That last level neutralizes the quiet price of working with AI - cognitive debt, the moment you stop understanding your own system. The work produces architecture reports, repository maps and domain diagrams you have to be able to defend. You remain an architect and reviewer with your own judgment, not someone rubber-stamping the model's output.

In the forty-first millennium technology is worshipped, because no one can repair it anymore. Legacy without a map works exactly the same way: "we do not touch that file", deployment rituals, tribal knowledge instead of understanding. The workflow covers the full range of work with such code: mapping an unknown repository, researching a specific feature, planning a refactor and analyzing code along Domain-Driven Design (DDD) lines. This is territory almost no AI course touches.

Most developers do not write applications from scratch - they work in someone else's years-old code. The workflow covers the full range of work with legacy: mapping an unknown repository, researching a specific feature, planning a refactor and analyzing code along Domain-Driven Design (DDD) lines. This is territory almost no AI course touches.

The core workflow skills - research → plan → implement → review - work in any repository, regardless of the stack and the project's complexity. They will get you through 90% of programming challenges, and you will handle the rest with specialized skills, which you will learn to build during the course.

In "The Three-Body Problem" survival depends on measurement, not on faith in someone else's declarations. An AI budget works exactly the same way: the course gives you a concrete overview of what is genuinely worth paying for - which subscriptions make sense and for what, how to pick a model based on the 10xBench benchmark (a comparison of models on real programming tasks, run as part of the course) and your own 10xEval evaluations for your team's specific tasks.

The course gives you a concrete overview of what is genuinely worth paying for: which subscriptions make sense and for what, how to pick a model based on the 10xBench benchmark (a comparison of models on real programming tasks, run as part of the course) and your own 10xEval evaluations for your team's specific tasks.

You test models on your own tasks and know when it pays to use open local models. The result: predictable cost and a return on every token, in a company budget and a private one alike.

As in "Hyperion": the road is what counts, and the weight is carried by the tale of the one who walked it. The certificate stands on three real, defensible projects, not on a knowledge test: a working MVP (Builder), an architecture report (Architect) and a CI/CD pipeline extended for the team's needs (Champion). Three stages of the pilgrimage - each with hard evidence in a repository, not an a/b/c answer.

The certificate stands on three real, defensible projects, not on a knowledge test: a working MVP (Builder), an architecture report (Architect) and a CI/CD pipeline extended for the team's needs (Champion). Each with hard evidence in a repository, not an a/b/c answer.

On top of that comes DemoDay, where the best present their work and win awards in a community vote. By July 27, 2026, participants had submitted 1,500 certification projects - real applications answering personal, family, team and company needs. Participants describe the effect as a real transformation and treat the effort involved as part of that value.