/// PROCESS

HOW IT
WORKS

From free enrollment to a verifiable certification in your hands. Every step is designed to be transparent, rigorous, and impossible to fake.

/// CERTIFICATION PIPELINE

7 PHASES. ZERO SHORTCUTS.

PHASE 01

2 MIN

ENROLL FREE

Create an account. No credit card. Immediate access to the full course library.

PHASE 02

SELF-PACED

LEARN THE TOOLS

Watch video lectures on Cursor, Claude Code, OpenCode, and AI-assisted development workflows. Each module covers a specific tool and set of patterns.

PHASE 03

10-15 MIN EACH

PASS THE QUIZZES

Timed quizzes after each module confirm comprehension. Anti-cheat tracking ensures you watched the lectures at normal speed before attempting.

PHASE 04

$150 ONE-TIME ($50 .EDU)

PURCHASE CERTIFICATION

When ready, pay the one-time $150 fee ($50 with a .edu email) to unlock the final exam. This grants you one exam attempt with retake options if needed.

PHASE 05

05:00:00

TAKE THE EXAM

A 5-hour server-timed challenge. You build a full SaaS application from scratch using AI coding agents. No pauses, no extensions. Submit your GitHub repo before the timer expires.

PHASE 06

48-72 HOURS

HUMAN CODE REVIEW

A senior engineer reviews your submission line by line. They evaluate architecture, code quality, deployment readiness, and whether the product actually works. Not a rubric checker.

PHASE 07

INSTANT ON PASS

GET CERTIFIED

Pass and receive a verifiable digital certificate with a unique ID. Add it to LinkedIn, your resume, or share the verification link directly with employers.

/// TOOLS

WHAT YOU WILL LEARN

[ AI-NATIVE IDE ]

CURSOR

Semantic indexing is the most important feature — it turns your entire codebase into context the model can actually use. Multi-file editing, inline generation, composer flows. Your Swiss army knife for AI-first development.

[ CLI AGENT ]

CLAUDE CODE

Proactive and hyperactive — great for automation and long-running sessions. Anthropic's terminal-first agent with full repo access, autonomous multi-step task execution, and tool use that keeps going while you context-switch.

[ OPEN SOURCE AGENT ]

OPENCODE

For teams who want full control without vendor lock-in. Self-hostable, transparent, extensible. Swap models, audit every token, and own your entire AI tooling stack end to end.

[ METHODOLOGY ]

BEST PRACTICES

The three-model strategy, cursor rules, compression awareness, and context engineering. The human side of AI development — knowing when to trust output, when to intervene, and how to keep the model productive across long sessions.

/// MODEL STRATEGY

THREE MODELS. THREE PERSONALITIES. ONE WORKFLOW.

Not every model is good at everything. Experienced AI developers match the model to the task. This is one of the first things we teach.

PLAN WITH ANTHROPICDEBUG WITH GPTEXECUTE WITH GEMINI
/// THE CORE INSIGHT

EVERY AGENT IS A NEW JUNIOR DEVELOPER.

This single insight is worth the entire course.

[ WITHOUT DOCS ]

You spin up an agent. It has zero context about your project. It guesses at your API routes. It reinvents services that already exist. It writes code that conflicts with your architecture. You spend 3 hours debugging what should have taken 30 minutes.

> agent: creating new auth service...
> ERROR: auth service already exists at /lib/auth.ts
> agent: I'll create it at /services/auth.ts instead...

[ WITH DOCS ]

You spin up an agent. It reads your internal docs first. It knows where auth lives, how your schema works, which API patterns you use. It traces imports, follows references, and builds on what exists. The feature works on the first run.

> agent: reading docs/AUTH.md...
> agent: found existing auth at /lib/auth.ts
> agent: extending auth with new provider...
> ✓ all tests passing

[ THE LOOP ]

DOCS PREVENT ERRORS

AGENT UPDATES DOCS

FEWER ERRORS NEXT TIME

This is the methodology. Not a one-time setup—a self-reinforcing system that gets better with every session. Cursor rules track recurring mistakes. Documentation evolves with the codebase. Every new agent starts smarter than the last.

/// ADVANCED CONCEPT

COMPRESSION EVENTS WILL DESTROY YOUR PROJECT. UNLESS YOU KNOW WHEN THEY HIT.

Every AI coding agent has a limited memory. When it runs out, it compresses — summarizing everything it knows into a fraction of the original. After 4+ compressions, your agent is working from a summary of a summary of a summary. We teach you exactly when to start fresh.

[ COMPRESSION 1 ]

FULL CONTEXT — SAFE

[ COMPRESSION 2 ]

SUMMARIZED — STILL RELIABLE

[ COMPRESSION 3 ]

SUMMARY OF SUMMARY — WATCH CLOSELY

[ COMPRESSION 4 ]

DEGRADED — START NEW WINDOW

[ COMPRESSION 5+ ]

LOST — AGENT IS GUESSING

PLANS SURVIVE COMPRESSION

Agent plans are immune to compression. Write a detailed plan before building, and the agent can reference it even after multiple compression events.

CURSOR RULES GET LOST

After compression, cursor rules context degrades. This is why mandatory rules are kept minimal and documentation is externalized to files the agent can re-read.

“The first compression event is totally fine. There’s no downside. But after four or more compressions, the agent is working from summaries of summaries — that’s where accuracy degrades and you need a fresh window.”

/// FROM COURSE LECTURES

/// DEBUGGING INNOVATIONS

TWO TRICKS THAT SAVE HOURS EVERY DAY.

These are not in any other course. They come from 5,098 hours of hard-won experience.

[ INNOVATION 01 ]

ROLLING LOGS — THE AI READS THE ERRORS, NOT YOU

Traditionally, you’d look at your terminal, see an error, copy it, and paste it into the AI chat. That’s a human bottleneck. Instead, we write all dev server output to a rolling log file. The agent reads the file directly. No copy-paste. No human in the loop. Debugging speed goes from minutes to seconds.

> npm run dev >> logs/dev.log 2>&1
> agent: reading logs/dev.log...
> agent: found TypeError at line 47 of /api/auth/route.ts
> agent: fixing... ✓ resolved

[ INNOVATION 02 ]

QUEUE COMMANDS — CHAIN SAFE OPERATIONS WHILE YOU SLEEP

Cursor lets you queue messages that send after the current task completes. Use this to chain guaranteed-safe operations. Step 1: Have the agent analyze docs vs. codebase for discrepancies. Step 2: Queue “now update the documentation.” You walk away. When you come back, both tasks are done correctly.

YOU:
Compare current docs to codebase. Find gaps.
AGENT:
[analyzing 12 files...] Found 3 discrepancies.
QUEUED →
"Now update the docs to match reality."
AGENT:
[updating docs/AUTH.md, docs/STRIPE.md...] ✓ Done.
/// ZERO RISK

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