Course
AI-Driven Development

Upgrade for Developers

Enrollment Open
Starting April 6

7 weeks
2 times a week

Course is ideal for

Engineers ready to move beyond vibe coding into professional AI-powered development.

What will you get from this course?

Middle+

Role shift: Transition from vibe coding to managing contexts and prompt architecture.

New stack: Master MCP and agents for building large-scale systems whose codebase goes beyond copy-pasting from LLM chats.

Multiplied speed: Deep integration of AI into the development workflow.

Senior

Managing delegation: Skills to build autonomous auditor agents for code review, test writing, and documentation.

Taming legacy: Techniques for rapidly parsing and refactoring legacy code with AI.

Process orchestration: Experience building complex decision-making chains.

Architect

AI ecosystem design: Building AI systems with data security and cost of ownership in mind.

AI transformation: A ready-to-use strategy for integrating agentic technologies into development processes.

Efficiency management: ROI calculation methodology.

Learning Outcomes

1. Systems Engineering Instead of Prompting

Learn to build fully functional autonomous systems with hands (MCP) and a brain (Agents).

2. Deep AI IDE Integration

Transform Cursor or Windsurf from an advanced autocomplete into a true AI colleague that understands your codebase and handles routine tasks.

3. Self-Regulating Tech Workflow

Learn to delegate the most tedious development stages to AI — from writing unit tests and documentation to automated code review.

4. Taming Legacy Chaos

Master techniques for rapid refactoring and decoding of legacy code using specialized agents.

5. Become an AI Architect

Learn to design AI-powered solutions and choose between GPT/Claude/OpenAI and local models based on security requirements and budget.

6. Speak Business Language with Numbers

Master a ready-made ROI calculation methodology for AI tool adoption and build a safe implementation plan for the corporate environment.

What's inside

1

Deep Dive into AI IDEs

We don't just learn to use the chat, we turn Cursor or Windsurf into a full-featured command center. You'll build your own MCP servers that act as the hands of the LLM: it will learn to manage databases and interact with external APIs.
2

Context Management (RAG vs CAG)

You'll learn how to feed AI massive codebases and technical documentation without losing quality. We'll explore when to build complex indexes (RAG) versus when it's enough to dump everything into the context (CAG), leveraging the power of long-context models like Gemini.
3

Autonomous Agent Orchestration

In the n8n module, you'll build a "digital employee". This isn't just a bot — it's an agentic system that can reason, plan steps, and use tools. Your agent will be able to monitor logs autonomously and decide whether to enable a feature flag or roll back a build.
4

AI Quality Engineering and Legacy Code

The most powerful practice: building an auditor agent. You'll teach AI to act as a tech lead that reviews your (and others') code, finds security vulnerabilities, writes tests, and restores documentation for spaghetti code.
5

Designing Zero-Coding Interfaces

We'll learn to instantly turn abstract logic into live dashboards. You'll build a full admin panel to manage your AI tools through buttons and charts, using generators like v0.dev or Bolt, connected to a real backend.
6

Strategy, Security, and ROI

Step up to architect and CTO level. We'll cover how to justify AI adoption to the business with numbers, how to ensure data security (PII masking), and when it makes sense to replace expensive APIs with local models like DeepSeek or Llama.

Course Practice

You'll experience firsthand the main paradox of modern development, AI lets you write code faster than you can comprehend it. The practice teaches you not just to code, but to structure that volume of code using AI as an architect and auditor. You'll understand how to survive in a project that starts turning into legacy just a month after launch.

The course is a simulator of a typical IT product lifecycle, compressed into 7 weeks. Its logic reflects not an ideal world or theoretical recommendations, but a real development cycle:

Stage 1: Fast Start

In the first weeks, you're riding high and quickly building the foundation with AI: logic and interface. This is the startup stage — everything is flying, documentation is non-existent, and the first crutches appear in the code. For now, you feel like a productivity god.

Stage 2: Accumulating Complexity

You add the brains — agentic scenarios and external integrations. The project grows, there are too many connections. This is where the legacy moment hits: you open code you wrote two weeks ago and realize you no longer remember how it works. The project turns into a tangled mess.

Stage 3: AI as the Solution

You create agents that analyze the code: find bugs, write tests, and restore documentation.

Course Assignments

1

Modules 1–2. Foundation and Environment

We design the architecture of the future system and configure the AI IDE for deep code work. We prepare the project foundation that will be understandable to the LLM.
2

Module 3. MCP Server

We develop a server for managing feature flags via files and data. We create the "execution layer" through which the LLM can physically change product settings.
3

Module 4. Dashboard

We generate an interface in React/Tailwind without manual layout. We bring scattered tools together in one unified interface.
4

Module 5. Autonomous Agent

We assemble a workflow in n8n that makes decisions based on data autonomously. We bring the system to life: it now rolls back releases or changes feature scope on its own, by reading logs.
5

Module 6. Auditor Agent

We create a new agent for reviewing and refactoring code from the earlier modules. We learn to tackle technical debt by delegating test writing, documentation, and bug detection to AI.
6

Module 7. Business Strategy

We calculate the ROI of implementation and design the security perimeter (PII masking). We package the technical project into a business-friendly implementation plan with numbers and data protection.

Certificates

Each section of the course has basic and advanced difficulty levels. The final certificate depends on which path you choose:

Completed with Distinction — for those who completed all advanced-level assignments.
Successfully Completed — for those who completed all basic-level assignments.
Attended — if some basic-level assignments were left incomplete.

Prerequisites

Middle+ Level

Confident understanding of APIs, JSON, and client-server architecture.

Basic Stack

Ability to read and run code in JavaScript/TypeScript or Python.

Toolset

Willingness to pay for AI service subscriptions (Cursor, Claude/GPT API).

Learning Process

Materials and ready-to-use code: The learning platform provides theoretical materials, assignments, and links to repositories with working examples that you can run and study immediately.

Theory discussion: Once a week — a group meeting. Instead of lectures, we hold a live discussion and exchange of opinions and experience based on pre-studied materials.

Assignment review in groups: Once a week, we meet in small groups to review completed practice work and address questions in detail.

Feedback: You receive reviews of your solutions and consultations on complex questions from a practicing expert with real AI implementation experience.

Community: A shared chat for answering questions, sharing experience, and professional communication with peers.

Learning Format

Interactive Webinars

2 online sessions per week. Lifetime access to recordings and study materials

Practice

Homework assignments + project work to strengthen your portfolio and competencies

Active Community

Live communication with instructors at sessions, Telegram chat, and detailed feedback when reviewing assignments

Capstone Project

Feature Flags Management System

You will build a complete release management system (Feature Flags), evolving from a simple script to a "smart" enterprise solution.
Your project will consist of five key layers, assembled through your homework assignments:

Layer 1: Executive. Your own MCP server — the "hands" of the system that physically change the state of your software.

Layer 2: Visual. A professional Dashboard — the "face" of the project through which you control processes not in the console, but in a user-friendly interface.

Layer 3: Intelligent. An autonomous agent in n8n — the "brain" of the system, making decisions based on data and logs without your involvement.

Layer 4: Engineering. The result of deep refactoring and auditing — a project cleaned of legacy code and fully covered with tests and documentation using AI.

Layer 5: Strategic. A ready business case with an ROI calculation and a configured data security perimeter.

Course Curriculum

Module 1. Landscape Overview: 2026 Trends and the Art of Prompting

Learning to extract high-quality architectural decisions from neural networks and design complex systems

Systematic landscape overview: Who's leading in 2026? Model comparison, the difference between executor and reasoning models.
Trends and predictions: From chatbots to agentic workflows. Why the future belongs to small local models (SLM) and narrow specialization.
Prompting as design: Chain-of-Thought, few-shot, and other techniques, and system roles.
- Meta-prompting: How to get AI to write the perfect prompt for your task.
- Limitations: How to fight hallucinations and model fatigue in long conversations.
Outcome: You understand how neural networks work and can get predictable results on the first try.

Module 2. Using AI IDEs

We configure the AI IDE so that the neural network sees your entire project and almost writes code on its own.

Cursor vs Windsurf vs Copilot
Environment setup: Working with .cursorrules. Creating context instructions and code standards.
AI in the terminal: Using CLI to automate routine tasks in the console.
Outcome: Your IDE transforms into a full-fledged partner that understands the context of the entire project.

Module 3. Context and Data: RAG, CAG, and MCP

We figure out how to feed large database volumes to AI and give it MCP to take actions.

RAG vs CAG: When to build a vector database versus when a long context and caching are sufficient.
MCP: How to connect a neural network to your environment (Jira, GitHub, Notion, local DBs) via a unified standard.
"MCP in 10 Minutes" workshop: Writing a simple server in Python/TS that gives AI access to your local system or API.
Outcome: You know how to extend AI capabilities with your own data and tools, avoiding hallucinations.

Module 4. Tool Prototyping: From Idea to UI in 15 Minutes

We build a beautiful admin panel without manual layout to manage tools via buttons and charts.

Prompt-to-UI: Using v0.dev, Bolt.new, and Lovable for instant frontend generation from a description or screenshot.
Figma + code combination: How to quickly transfer design logic into code via plugins and VS Code without spending hours on CSS.
Automation interface: How to quickly build a control panel for your internal scripts and AI agents.
Outcome: You become a "one-person army" capable of building a full product prototype on your own.

Module 5. Agents and n8n: Building an Orchestrator

This module transforms your project from a set of tools into an autonomous system capable of making decisions.

Agent anatomy: The difference between a simple LLM call and an agent cycle (Planning → Memory → Action). Breakdown of architectures: ReAct, Plan-and-Execute.
Low-code orchestration in n8n: Using n8n as a visual hub for agents. Connecting LLMs to your MCPs and external services in the visual editor.
State management: How to give an agent memory of past actions and teach it to correct its mistakes during task execution.
UI + Agent + MCP integration: Designing an end-to-end flow where the user sets a high-level goal in the interface and the agent decides which tools to call to achieve it.

Module 6. AI Pipeline and Legacy: Industrial Standard

Here we use AI as a code sanitation and quality engineering tool.

AI Code Review: Building automated code inspection systems. Setting up censor prompts to find vulnerabilities, memory leaks, and architectural pattern violations.
Living documentation and knowledge base: Using AI to keep README files, API descriptions, and internal documentation up to date.
Legacy code strategies: Methods for untangling old code. How to use AI for refactoring monoliths, migrating to new library versions, and deciphering undocumented logic.
Synthetic Testing: Generating complex test scenarios and synthetic data to verify system resilience under extreme conditions.

Module 7. Strategy: Security and Implementation

A final look at how to turn AI skills into a real competitive advantage within your company.

Privacy & Security: How to work with AI under strict NDAs. Overview of solutions for running models locally and filtering data.
Economics and stack selection: When it's worth paying for top model tokens versus fine-tuning your own or using cheaper alternatives.
Implementation plan: How to present AI solutions to business stakeholders, overcome team resistance, and assess the real ROI of automation adoption.
AI Horizons 2026: What comes after agents? Preparing for the next wave of technology and long-term skills planning.

Why This Course?

1. Systematic Thinking Over a "Lifehack Library"

Most free tutorials are just a collection of disconnected tips. This course gives you a mental map.

Reasoning: You'll stop randomly trying every model one after another. You'll understand the architecture: where heavy RAG is needed, where cheap CAG is sufficient, and where a small local model does the job. This turns wandering into a plan.

2. Pragmatic 2026 Stack (MCP and DeepSeek)

The curriculum includes Model Context Protocol (MCP) and work with new market leaders (DeepSeek, Windsurf) that don't have textbooks yet.

Reasoning: Many get stuck on the "classics" (OpenAI + LangChain). This course gives you tools that right now solve the problem of AI isolation from data (Jira, GitHub, local DBs). This makes the student the most up-to-date specialist on the team.

3. Focus on Legacy and Real-World Processes

The course doesn't offer abstract solutions in a vacuum. It teaches you to parse old code, dry documentation, and perform endless reviews.

Reasoning: We don't cover how to write a ToDo list from scratch. This course teaches you how to join a 10-year-old project and use AI to cover it with tests and perform smart refactoring. This is a skill businesses are ready to pay for immediately.

4. Low-Code for Rapid Automation

Including n8n and tools like v0/Bolt expands the capabilities of a single developer.

Reasoning: We teach you not to "write code for code's sake." If an automation can be assembled in 15 minutes in a visual tool, an engineer should know how to do that. This frees up time for truly complex tasks.

5. Addressing Security and Privacy

Special emphasis on local models and working within NDA constraints.

Reasoning: This is the main barrier to AI adoption in companies. A student who knows how to deploy Ollama and use AI without leaking data to the cloud becomes a "safe" and valuable technology guide for their management.

6. From Prompts to Agents

We teach not just how to prompt AI, but how to build chains where AI plans tasks and uses tools on its own.

Reasoning: Prompting as a skill is slowly dying, replaced by the ability to design agent behavior. This course gives you this future skill today.

Course Instructor

Sergey Golubev

Product Manager & AI Creator

Technical Product Manager with 16+ years in IT. Started as a QA engineer, led the QA department for 10 years, then transitioned to product management. For the past 3 years, he has been actively integrating AI into development processes, building his own products and agentic systems. Author of a Telegram channel about AI tools.

Work Experience:

Senior Product Manager at an international B2B IT company
Head of QA Department
Integration of AI tools into development processes
Speaker and mentor at AI workshops and hackathons

Course Pricing

STANDARD

$800

Included:

Full access to 7 sessions
Homework assignment review
Access to materials and recordings
Participants chat
Digital completion certificate in EN and RU

MASTER

$1200