AI Dev Tools Zoomcamp

Links:

• #course-ai-dev-tools-zoomcamp on Slack
• Telegram Channel with Announcements
• FAQ
• Course Playlist
• Course Launch Stream
• Article with details about the course

How to Join?

We're starting the first cohort of this course on November 18, 2025!

Sign up here to join us.

Who Is This For?

This course is for anyone who wants to use AI tools to help with coding.

You don't need any AI experience to start - just curiosity about using AI tools to help with your coding!

What We'll Cover

Module 1 — Introduction to Vibe Coding / AI Tools Overview

• AI-assisted development with Snake game example (React + JS)
• Chat applications: ChatGPT, Claude, DeepSeek, Microsoft Copilot
• Coding assistants / IDEs: Claude Code, GitHub Copilot, Cursor, Pear
• Project bootstrappers: Bolt, Lovable
• Agents: Anthropic Computer Use, PR Agent, others

Module 2 — End-to-End Project (Snake)

• Use a coding assistant for an end-to-end project
• Build Snake in React/TS
• Define API with OpenAPI
• Generate FastAPI server from OpenAPI specs
• Add CI/CD
• Deploy the application

Module 3 — Model-Context Protocol

• Enhancing AI assistants with tools
• Core servers: GitHub, Filesystem, DB/SQL, HTTP/API, CI
• Practical workflows: repo triage, PR summarization, scripted edits, data queries
• Local vs. remote servers
• Security/permissions

Module 4 — Build an AI Coding Agent (for Django)

• Build your own coding agent that can scaffold and extend projects
• Use a Django template as the base project
• Learn how agents act as project bootstrappers
• Explore multiple agent orchestration frameworks
• Outcome: a Django app created and modified by your AI agent

Module 5 — AI for Testing, CI/CD & DevOps

• AI-assisted PR reviews/summaries and change-risk hints
• Automated test generation, coverage gates, and LLM evals in CI
• Release notes, changelog drafting, and deployment runbooks
• Incident postmortems and on-call copilots

Module 6 — Automation with Low-Code and No-Code AI (n8n)

• Install N8N
• Create posts for LinkedIn
• Tailor your CV for a specific position

Your Instructors

• Alexey Grigorev
• Bhavani Ravi
• Moein Foroughi

Testimonials

This course fundamentally changed how I approach AI development. I moved from “building models” to designing AI-assisted systems that are faster to ship and easier to iterate on.

During the course, I built:

• A portfolio optimization tool powered by AI-assisted development
• A full-stack application using ChatGPT, Lovable, and Antigravity
• A structured GitHub project with clean documentation and reproducible workflow

What changed for me: I now think in terms of system design rather than isolated scripts. I learned how to structure AI tool usage, validate outputs, and integrate generated code into disciplined engineering workflows. The biggest shift was moving from experimentation to controlled, production-oriented iteration. I can now prototype and deploy AI-enabled tools significantly faster without sacrificing rigor.

— Yann Pham-Van, Freelance Data Scientist

The course taught me how to use coding agents effectively, debug issues, and gave me exposure to MCPs, tools, and prompts. It helped me conceptualize any idea into a working prototype. And finally, it helped me land a job after a long career break!

— Revathy Ramalingam, Senior Software Engineer at Yalabs Solutions

During the course I built a Finnish learning website which helps English users learn and practice their reading, writing, listening and speaking skills for the Finnish language.

Tech Stack:

• IDE: Antigravity IDE with Gemini 3 Pro High & Claude Opus 4.5 Thinking (switching LLMs depending on available capacity relative to rate limits)
• MCP server: Context7 documentation MCP server (for Antigravity IDE's LLM to retrieve the relevant documentation if it is unsure of a library's syntax)
• Language: TypeScript (frontend), Python (backend)
• Framework: Next.js (frontend), FastAPI (backend)
• Database: SQLite
• Styling: Tailwind CSS
• Package Manager: npm
• Final Deployment: Render (serving the full frontend AND backend as a "Single Docker Container" Microservice)
• Transcription: Client-side Google Web Speech API
• LLM: gemma-3-27b (grades Finnish speech transcribed to text format)
• CI/CD with GitHub Actions to run backend unit tests using Pytest, frontend unit tests using Jest, and full-stack end-to-end tests using Playwright

What Changed For Me

1. Learning a systematic way to think about the requirements and design an application, before developing and testing various components of the application iteratively
2. Learning to package frontend and backend components into a single container for easier deployment
3. Practising how to debug frontend and backend tests, which tend to break things when I started to integrate the frontend, backend and database together, and when moving from deploying the container locally to deploying on the cloud

— Kaiquan Mah, Data Scientist at Total eBiz Solutions

About DataTalks.Club

DataTalks.Club is a community of data enthusiasts learning and growing together. We're all about sharing knowledge, helping each other out, and making data science more accessible.

Join us: • Website • Slack Community • Newsletter • Events • Calendar • YouTube • GitHub • LinkedIn • Twitter