hardandsoft@hardsoftskills.dev
AI-Driven Development course
RU

AI-Driven Development course

An upgrade for working developers

Enrollment open
Starts July 27
7 weeks
2 sessions per week
Enroll now
AI-Driven Development
«The course felt like a whole universe opening up» – Pavel Loika, QA Lead
«It lit a fire in me – I want to keep growing» – Artur Elzhanov, Senior Java Developer
«I wanted to broaden my worldview – the course did that 100%» – Yauheni Yefimenka, Java Team Lead
«Exactly what you need to understand in a company of any scale today» – Ilya Chakun, Backend Software Engineer

Who this course is for

Experienced developers and IT professionals who want to bring ready-made AI tools – IDEs, agents, utilities – into their daily workflow and dramatically speed up their work.

Roles

Backend, Frontend, and Fullstack engineers, plus DevOps, QA automation engineers, and systems analysts with at least 1–2 years of professional development experience.

Situations
  • I want to ship higher-quality code in less time.
  • I'm tired of the routine: boilerplate code, documentation, repetitive configs.
  • I want to master Cursor, Windsurf, Copilot, ready-made MCP servers, and UI generators – and build them into my workflow.

What you'll gain

AI-IDE and coding
  • Speed: write code in Cursor, Windsurf, and Copilot several times faster with well-tuned prompts and .cursorrules.
  • Tests and templates: automated generation of unit and integration tests, standard configs, boilerplate code, documentation, test plans, and test cases.
  • AI in the terminal: developer, DevOps, and Git workflows handled by AI assistants right in the terminal.
Ready-made AI tools
  • MCP: plug ready-made public MCP servers (files, GitHub, databases, Jira/Confluence) straight into your IDE.
  • Zero-Coding UI: generate interfaces with v0.dev, Lovable, and Bolt.new from screenshots and text – plus prototyping in Figma.
  • Your own simple MCP: a minimal MCP server tailored to your project's needs.
Processes and Legacy
  • AI Code Review: automated PR checks for vulnerabilities and architectural compliance.
  • Docs and tests: automating README files, JSDoc, test plans, and test cases.
  • Safe Legacy: refactoring old code with behavior-preservation guarantees.

Course goals

1. Master the AI-IDE as a daily tool

Turn Cursor or Windsurf into a digital partner that understands your project's context, writes code in your style, and takes the routine off your plate.

2. Automate the routine

Delegate unit and integration tests, documentation, boilerplate code, and standard configs to AI – without sacrificing quality.

3. Plug in ready-made MCPs without building your own

Use public MCP servers to integrate AI with Jira, GitHub, databases, and your own APIs – without building complex custom agents.

4. Generate UI without manual layout

Build interfaces from screenshots and text with v0.dev, Lovable, and Bolt.new – from prototype to integration with a real backend.

5. Work safely with Legacy

Master refactoring techniques and test-coverage methods for legacy code with behavior-preservation guarantees, including how to decode undocumented logic.

6. Stay aligned with corporate standards

Understand what can be sent to the cloud, how to use local models, and how to roll AI out across your team with a step-by-step weekly plan.

What's on the course

  • 1
    The AI landscape: models and trends in 2026

    A systematic overview of the leaders (OpenAI, Anthropic, local SLMs) and agentic workflows. You learn to pick the right model for the job and avoid overpaying for Opus when a cheaper model will do.

  • 2
    The AI-IDE as your working environment

    Turn Cursor, Windsurf, or Copilot into a digital partner. .cursorrules, system prompts, automated unit and integration tests, AI right in the terminal – all the mechanics that save hours every day.

  • 3
    Ready-made MCP tools

    Plug public MCP servers into your IDE: file system, GitHub, databases, Jira – all from the AI chat without building your own integrations. You'll also assemble a simple MCP for the course's feature flag project (or for one of your own).

  • 4
    Zero-Coding UI

    Generate interfaces with v0.dev, Lovable, and Bolt.new from screenshots and prompts. The Figma + AI-IDE pairing for porting designs into React or Vue components without wrestling with CSS.

  • 5
    AI in development processes

    AI code review on PRs, automated upkeep of README and JSDoc, generation of test plans and test cases, spec-driven development that pairs analyst work with AI.

  • 6
    Agents, Legacy, and rollout

    n8n as a visual orchestrator for agents that read logs and make decisions. Safe legacy refactoring with AI. Local models and a step-by-step plan for embedding AI into your routine in a week.

Course practice

The course is built as a phased rollout of ready-made AI tools into a developer's daily workflow – from your first .cursorrules to an autonomous agent that manages feature flags.

You do every assignment on the end-to-end Feature Flags Management System project, on your real work code, or on a project of your own. The course mirrors how AI adoption actually plays out on a real team:

Stage 1: Quick start with the AI-IDE

In the first few days you set up Cursor or Windsurf, write your .cursorrules, and feel the speed boost right away. Coding, unit tests, and templates are generated straight in the IDE – nothing complex, just ready-made tools.

Stage 2: Expanding the arsenal

You plug in ready-made public MCP servers, assemble UI with v0, Bolt, or Lovable, and hand code review and documentation off to AI. Along the way, you build your own minimal MCP for the project.

Stage 3: Autonomy and Legacy

The finale: your own n8n agent that reads logs and decides whether to toggle flags or roll back a release. Along the way, you learn to refactor legacy code with behavior guarantees and to make the case for AI adoption to your team while staying within NDA constraints.

Course assignments

  • 1
    Modules 1–2. AI-IDE setup

    Pick an AI-IDE (Cursor, Windsurf, or Copilot), write .cursorrules for your project, and generate the first module with full test coverage through prompts.

  • 2
    Module 3. MCP for feature flags

    Plug ready-made public MCP servers into your IDE (files, GitHub, databases). On top of that, build a minimal custom MCP server to manage feature flags.

  • 3
    Module 4. Dashboard via UI generators

    Generate an admin panel for flag management with v0.dev, Lovable, or Bolt.new and wire it to the MCP server – no manual layout, no hours of CSS.

  • 4
    Module 5. AI in processes

    Set up AI code review for your repo, automate documentation (README, JSDoc), and generate test plans, test cases, and unit tests.

  • 5
    Module 6. Autonomous agent in n8n

    Build a workflow in n8n that makes data-driven decisions – rolling back releases or adjusting a feature's rollout based on logs.

  • 6
    Module 7. Legacy and rollout plan

    Safely refactor a tangled piece of code with behavior-preservation guarantees. Prepare a personal one-week plan for rolling AI tools into your workflow.

Certificates

Your final certificate depends on how much practice you complete:

  • Basic certificate: at least 70% of assignments completed and a basic working environment set up.
  • Advanced certificate: all assignments completed and a personal AI rollout plan defended.

Prerequisites

Professional development experience

At least 1–2 years in any stack. A solid grasp of the basics: writing code, testing, working with Git.

A working development environment

An IDE already installed (VS Code or JetBrains) and willingness to install a new AI-IDE: Cursor or Windsurf.

AI accounts

OpenAI, Anthropic, or GitHub accounts – either already set up, or willingness to create them and pay for subscriptions during the course.

Learning process

Materials and ready-made code: A course Git repository with theory, assignments, and working examples – you run them and study at your own pace.
Daily self-study: Theory, practice, and homework every day, with no rigid webinar schedule. Easy to fit around your main job.
Sync sessions: A kickoff at the start, regular Q&A sessions, and a wrap-up at the end – to keep the group in sync and answer questions.
Code review by mentors: A detailed review of your homework by working experts with hands-on experience rolling out AI tools.
Community: A private Telegram chat for participants and mentors – questions, peer learning, and professional networking.

Format

Daily self-study

Theory, practice, and homework at your own pace. All materials, videos, and repositories are yours to keep forever.

Sync meetings

A kickoff at the start, regular Q&A with the expert, and a wrap-up at the end – for group discussion and answering questions.

Code review and community

A detailed review of every assignment by mentors, plus a live Telegram chat with peers and instructors.

Course project
Feature Flags Management System

The end-to-end course project is a feature flag management system where you apply every AI tool and approach you learn. You build the project in layers as you work through the assignments:

Layer 1: MCP server. Your own minimal MCP that lets you manage flag state directly from an AI assistant or IDE.
Layer 2: Dashboard and custom skills. An admin panel built with v0 or Bolt, plus built-in skills that extend the capabilities of base agents working with flags.
Layer 3: Process automation. Automated generation of tests, documentation, and AI code review, all inside a single project repository.
Layer 4: Autonomous agent in n8n. The agent analyzes logs and decides on its own whether to toggle flags or roll back a release, closing the full AI-driven development loop.

Course program

Module 1. The AI landscape: trends and horizons

Learn to navigate the model landscape and pick the right stack for the job.

  • Systematic overview of models: OpenAI, Anthropic, and others. The difference between code-focused models and reasoning models.
  • Tooling evolution: the trend toward agentic workflows and Small Local Models (SLMs) in everyday development.
  • Choosing the stack and the model: when to pay for Opus on hard tasks, and when a cheaper model is enough for routine work.
  • Practice: testing different models on the same tasks, comparing results, and building a checklist for picking models for typical work.
Module 2. Diving into AI-IDEs

Turn the IDE into a true digital partner that understands the entire project context.

  • Cursor vs Windsurf vs Copilot: a comparison of the market leaders and basic environment setup.
  • Setting up your digital partner: creating and tuning .cursorrules, system prompts for the IDE, and code standards.
  • Core AI development skills: efficient code generation – from snippets to whole classes. Automated generation of unit and integration tests.
  • AI in the terminal: Warp, Copilot CLI, and other tools for AI work right in the console. Solving everyday DevOps and Git tasks with AI assistants.
  • Practice: writing .cursorrules for your own project, plus building a module with full test coverage through prompts.
Module 3. Using ready-made MCPs

Plug ready-made AI tools into your setup without writing your own integrations. And build a minimal MCP for feature flags.

  • The MCP tooling ecosystem: what Model Context Protocol is and what it brings to daily development. An overview of popular public MCP servers.
  • Connecting and using MCPs: integrating ready-made MCPs into Cursor or Windsurf without writing your own code.
  • Your simplest MCP: building a minimal MCP server to manage a project through feature flags.
  • Practice: connecting MCPs to work with the file system, GitHub, or a database directly from the AI chat in your IDE.
Module 4. Zero-Coding UI: from design to code

Build interfaces without manual layout – from screenshots and prompts.

  • Modern UI generators: an overview of v0.dev, Lovable, and Bolt.new. Generating UI from screenshots or text.
  • The Figma + AI-IDE pairing: plugins for clean export, porting designs into React or Vue components in Cursor.
  • Interfaces for automations: a control panel for your internal scripts and AI agents in minutes.
  • Practice: creating a page from a reference screenshot, plus integrating a ready-made design into an existing frontend application.
Module 5. AI in development processes

Delegate code review, documentation, and tests to AI – embedding AI into your team's daily workflow.

  • AI code review: tools for reviewing pull requests and merge requests, and setting up a basic AI reviewer for a repository.
  • Living documentation: automating README files, JSDoc / Docstrings, and API descriptions – keeping docs current.
  • Tests and test documentation: automating test plans, test cases, unit tests, and test documentation.
  • Spec-Driven Development: pairing analyst work with AI – from requirements to specifications.
  • Practice: setting up AI code review for your repo and generating documentation for the chosen project.
Module 6. Agents and n8n: building the orchestrator

Turn the project from a set of tools into an autonomous system that can make decisions.

  • Anatomy of an agent: the difference between a simple LLM call and the agent loop (Planning – Memory – Action). A breakdown of ReAct and Plan-and-Execute architectures.
  • Low-code orchestration in n8n: using n8n as a visual hub for agents. Connecting an LLM to MCPs and external services in the visual editor.
  • State management: giving the agent memory of past actions, self-recovery, and error correction during task execution.
  • UI + Agent + MCP pairing: 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 reach it.
  • Practice: an autonomous n8n agent that reads logs and decides on toggling feature flags or rolling back a release.
Module 7. Strategy: Legacy and rollout

Turn your new AI skills into a real competitive advantage on your team.

  • A tactical approach to Legacy: safe AI-assisted refactoring, writing tests for legacy code before changes, and decoding undocumented logic.
  • Security in the corporate environment: NDA compliance, what can be sent to the cloud, and an overview of subscriptions and limitations.
  • The final rollout plan: how to embed AI into your routine in a week – a step-by-step checklist.
  • Practice: refactoring a tangled piece of code with behavior-preservation guarantees, plus a personal plan for adopting AI tools in your workflow.

Why this course?

1. Ready-made tools, not building from scratch

Most AI courses teach you to build your own complex systems. This one focuses on ready-made tools – AI-IDEs, public MCPs, UI generators – that work right now and deliver results immediately.

Why this matters: A productivity boost in your first week, not after months of learning LangChain and vector databases. You leave the course with an updated daily workflow, not just theory.

2. The pragmatic 2026 stack

The program covers Model Context Protocol (MCP), agentic workflows, Small Local Models, and Zero-Coding UI – everything that has emerged in the industry over the past year.

Why this matters: A lot of people get stuck on the "classics" (OpenAI + LangChain). This course gives you tools that actually move daily work forward – Cursor, Windsurf, v0.dev, n8n, ready-made MCPs – and makes you the most up-to-date specialist on your team.

3. A focus on daily work

This course doesn't teach you to "build an AI startup from scratch." It teaches you how to start writing tests, documentation, and reviews with AI on Monday – on your real projects.

Why this matters: Every assignment is done on the end-to-end Feature Flags project or on your real work code, not a training ToDo list. It's a skill that pays off immediately.

4. The end-to-end Feature Flags project

All the modules add up to one real project – a feature flag management system with your own MCP server, dashboard, and autonomous n8n agent.

Why this matters: You leave the course with a finished portfolio project that demonstrates every key AI skill – from IDE and MCP to agents and AI review – not a folder of scattered screenshots.

5. NDA and corporate security

A dedicated focus on working under NDAs, using local models, and basic security when handling sensitive data.

Why this matters: This is the main blocker for AI rollout inside companies. Knowing what can and can't be sent to the cloud makes you a "safe" technology guide for leadership.

6. Self-paced study plus sync points

Daily self-study removes the dependency on a webinar schedule. At the same time, the kickoff, Q&A, and wrap-up keep the group in a shared rhythm and give you live time with the expert.

Why this matters: It's easy to fit around your main job – you can work through materials in the evening or on weekends, and bring real questions from your projects to the sync sessions.

Course instructor

Sergey Golubev

Product Manager & AI Creator

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

    Experience:
    • Senior Product Manager at an international B2B IT company
    • Head of QA
    • Rolling out AI tools across development processes
    • Speaker and mentor at AI workshops and hackathons
Sergey Golubev

Course price

STANDARD
$600
Included:
  • Full access to all 7 course modules
  • Homework review
  • Access to materials and recordings
  • Course chat
  • Electronic course completion certificate in EN and RU
MASTER
$1000
Included:
  • Full access to all 7 course modules
  • Homework review
  • Access to materials and recordings
  • Course chat
  • Electronic course completion certificate in EN and RU
  • + 2 hours of personal consultations with the course instructor

Master AI-driven development

Stories from the first cohort of the «AI-Driven Development» course – what they took away: a broad understanding of LLMs and agents, a practical toolkit, and the realization that you can now build products on your own.

Ilya Chakun
Ilya Chakun
Backend Software Engineer
AI-Driven Development Jun 11, 2026
«The set of tools and working with context is exactly what you need to understand in a company of any scale today».

For an entry level, the list of tools and everything about context is exactly what matters to understand in a company of any scale today.

For an entry level, the list of tools and everything about context is exactly what matters to understand in a company of any scale today.

I'd especially highlight assignment M7: it captures what many are actually trying to do in production right now – moving to local models for personal data and cloud models for everything else. I did all the assignments with pleasure.

Artur Elzhanov
Artur Elzhanov
Senior Java Developer
AI-Driven Development Jun 11, 2026
«This course genuinely got me hooked – I'm passionate about it and want to keep growing in this direction».

The course gave me a great deal in terms of theory and broadening my horizons in LLMs – before it, I had barely moved in this direction.

The course gave me a great deal in terms of theory and broadening my horizons in LLMs – before it, I had barely moved in this direction. I saw the announcement of the first version of the course in the student chat and, without a second thought, signed up right away: it resonated with me.

The practice, considering this was the first version, I really liked – the assignments were interesting, especially the ones using n8n (the fifth and seventh homeworks).

And the most valuable thing for me personally: a good course doesn't just make you a specialist, it gives you motivation. This course genuinely got me interested – I'm passionate about it and want to keep growing in this direction.

Pavel Loika
Pavel Loika
QA Lead
AI-Driven Development Jun 11, 2026
«For me, this course was like opening up a whole universe».

Before the course, I knew only the very basics about working with LLMs.

Before the course, I knew only the very basics about working with LLMs. And here a whole universe seemed to open up for me – so much new that you can't help wanting to dive in.

I'll definitely keep coming back to the materials we covered, rewatching and repeating things with my own hands.

I genuinely enjoyed it, and it was comfortable being on the course and in the communication. The start is made – and I'm glad we may see each other at the next stage.

Yauheni Yefimenka
Yauheni Yefimenka
Java Team Lead
AI-Driven Development Jun 11, 2026
«My goal was to broaden my worldview in this new technology – and the course did that 100%».

I'm a seasoned techie – and that makes me admire the topics Sergey raises all the more.

I want to express my gratitude to Sergey. I'm a seasoned techie – and that makes me admire the topics he raises all the more; at times it even makes me feel a little ashamed in a good way, and that motivates me to push further, because you see: it's possible.

My main goal was to broaden my worldview in this new technology – and in my opinion the course did that 100%, covering and touching on every aspect across all directions.

In learning, responsibility is split 50/50 between teacher and student – and the teacher delivered his 50% to the fullest. The rest of the baton is on us, and now it's up to us to decide where to go deeper.

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