Learn to build AI products
Open courses from our team: from LLM fundamentals to agents in production. No sign-up — just read.
Courses
18Практический AI Product Engineering Playbook
Десять оригинальных уроков от основ LLM до production operations: без демо-магии, с упором на проверяемые решения и архитектурную дисциплину.
Your First AI Agent: From Zero to Autonomy
A course on what turns an LLM call into an agent: the decision loop, tools, memory, guardrails, and shipping to production via an API — from 50 lines of code to a GitHub issues triage agent.
Claude Code: Agentic Development in the Terminal
A complete Claude Code course: from the agentic CLI mental model to headless CI automation — CLAUDE.md, slash commands, hooks, subagents, worktree isolation, and MCP with real examples.
Vibe Coding — and How to Take It to Production
From prompt to production: build an app fast in Lovable, v0, or Bolt — without going broke, leaking data, or fearing your own code.
MCP in Practice: Building Model Context Protocol Servers
The full MCP engineer path: from the N×M integration problem and JSON-RPC frames to your own server on the TypeScript SDK — with auth, secrets, Inspector debugging, and connections to Claude, Cursor, and VS Code.
Context engineering: beyond the prompt
Why prompts stopped being enough: the context window as a budget, retrieval and memory, compression, repository maps for coding agents, and context-quality evals in production.
AI process automation with n8n
A hands-on n8n course: from your first workflow and expressions to the AI Agent node, document RAG, reliable webhooks, and safe self-hosting — capped with an AI lead-qualification pipeline.
Multi-agent orchestration: LangGraph, CrewAI, and agent teams
When one agent is not enough: supervisor, swarm, and pipeline topologies, LangGraph state machines, CrewAI role-based crews, hand-offs and shared memory, parallel coding agents in git worktrees, run economics, and a capstone — a three-agent team.
Spec-driven development: the spec is the prompt
Why ad-hoc prompting does not scale: executable specs built from intent, constraints, and acceptance criteria, GitHub Spec Kit in practice, the spec as a single source of truth, fighting drift, and team review workflows.
AI evals engineering: testing systems that never repeat themselves
Why “looks right” is not a testing strategy: golden datasets from production traces, LLM-as-judge with bias control, regression CI gates for prompts, pass@k for coding agents, and statistical significance when switching models.
AI agent security: from prompt injection to the OWASP LLM Top 10
A defensive course on agent security: why LLMs do not separate instructions from data, direct and indirect prompt injection, the lethal trifecta and exfiltration, sandboxing and least privilege, MCP and supply-chain security, red-teaming and incident response — grounded in the OWASP LLM Top 10 and public incident reports.
AI-native IDEs: Cursor, Copilot, and the new editor workflow
Autocomplete, chat, and agent mode as the three gears of an AI editor: a Cursor deep dive (Tab, Cmd+K, Agent), rules files and project context, Copilot in VS Code, disciplined review of inline edits, and a final refactor in all three modes.
Production RAG: retrieval systems that do not hallucinate
An advanced RAG course covering structure-aware chunking, hybrid search and reranking, query rewriting and multi-hop retrieval, agentic RAG, grounded answers with citations, evaluation, capacity planning, caching, SLOs, and safe rollouts.
LLM fine-tuning: LoRA, QLoRA, and when not to do it
When fine-tuning pays off and when RAG or a prompt is enough: a decision tree, dataset curation as 80% of the work, LoRA and QLoRA on a single GPU, DPO for preference alignment, honest evaluation against the base model, and serving with quantization — up to the capstone: a small model turned code-review specialist.
Code Review in the AI Era: Trust, Verify, Merge
AI writes code faster than your team can read it. A course on reviewing diffs you did not write: a failure taxonomy of «almost right» code, security checks, agent reviewers, tests as the trust boundary, and a team auto-merge policy.
LLMOps and agent observability: debugging the black box
Traces and spans instead of printf: instrumenting agents with OpenTelemetry, LangSmith, and Langfuse, token and latency dashboards, hunting failure patterns in production traces, alerting on quality drift, and the traces → eval sets → fixes loop.
От JavaScript до Fastify: один Task API, четыре уровня
Практический backend-трек для отдела разработки. На протяжении четырёх уроков один Task API взрослеет вместе со стеком: от чистого JavaScript и безопасной асинхронности к строгому TypeScript, событийной модели Node.js и production-границам Fastify.
Prompt Lab: от мысли к проверяемому результату
Интерактивный курс для обычных пользователей: формулируем цель, даём полезный контекст, управляем форматом, улучшаем ответ итерациями и безопасно подключаем рабочие данные.
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