Turn one bounded workflow into a system with an explicit contract, governed implementation, role handoffs, guardrails, tests, evaluation evidence and a next learning loop. You can read Python or TypeScript and use Git and a terminal.
Vibe coding can accelerate a prototype. This course teaches the product system around it: what people can inspect, change, hand off, test and deliberately hold. By the end, you will be able to assemble a reusable starter folder and make the next decision from evidence — not a promise of traffic, revenue, certification, product-market fit, or production readiness.
Before you start
This is neither product-management theory nor a no-code/vibe-coding course. Learners need Git, a terminal and the ability to read Python or TypeScript. The required path stays offline and uses synthetic data; paid APIs, cloud deployment, analytics and domains are optional adapters.
Course path
Complete APD.1 → APD.15 in order. APD.1–APD.9 establish the system; APD.10–APD.15 extend it into specs, agents, guardrails, evals, an export kit and a research ledger. From APD.6 through APD.13, one fictional Event Brief decision record travels with the case: it says which version is being reviewed, what the checker did or did not test, who still owns the decision, and what to do next. Each matching lab is required practice: run it after its module, interpret the synthetic result, and keep the artifact for the next decision. The course download includes a starter folder with the companion templates.
Public preview · read first
Inspect the first decision before deciding
Read APD.1 to inspect one bounded decision on a fictional workflow. The matching offline scope-card lab is included with the complete course; it produces one complete and one held card, demonstrating a decision artifact rather than demand, safety, or launch readiness.
APD.1 is the public preview. APD.2–APD.15 and the matching labs require enrollment in the complete course.
This is a public inspection path, not a purchase flow or production authorization.
System foundation — APD.1 to APD.9
The foundation follows one fictional, low-risk workflow through nine reviewable decisions. Every exercise remains synthetic and non-production evidence; it teaches an inspectable development system, not a claim about a live product.
The second half extends the same fictional case into executable specifications, agent handoffs, execution guardrails, evaluation disagreement, export review and source-ledger practice. Together, the 15 modules make the full course path.
The offer includes the full EN/PT course, 15 offline Python labs, a reusable starter folder and the composed fictional capstone. It is individual self-study for technical builders.
Offer · USD
US$249
Regular price: US$299
The USD offer is shown on this EN route. The final total at checkout is authoritative; local taxes, payment-method costs or conversion can change it before confirmation.
Offer · BRL
R$1.190
Regular price: R$1.490
The BRL offer is shown on this PT-BR route. The final total at checkout is authoritative; taxes and payment-method costs can change it before confirmation.
15-day guaranteeRequest through Hotmart under the rules shown at checkout.
Yours to keepPersonal license for the downloaded release, with 12 months of package updates.
Offline deliveryBilingual ZIP in the Hotmart library after payment confirmation; labs run on a computer.
Focused supportEmail help for access, download and a reproducible first-run issue.
Individual self-study. No certificate, academic credit, live tutoring, implementation consulting, community, or lifetime updates. Git, a terminal and the ability to read Python or TypeScript are prerequisites; the required practice remains offline and synthetic.
This course teaches
A sequence for making technical AI-product decisions inspectable: scope, specification, governed implementation, agent handoffs, guardrails, tests, evaluation evidence, export and the next experiment.
What it does not establish
A synthetic exercise does not establish deployment approval, production-data safety, demand, SEO performance, revenue, or market evidence.