CodeGenAI: 2-Day AI Coding Training for Engineers — Amsterdam Edition
A 2-day in-person training for software engineers who want to use Claude Code and agentic coding workflows in real development environments.
This training is focused on the practical engineering mechanics of working with AI coding agents.
What participants will work on
Flappy Bird clone Use Claude Code to modify an existing game loop, add features, re-theme assets, inspect diffs, checkpoint changes, and recover when the agent takes the implementation in the wrong direction.
Unfamiliar codebase analysis Use Claude Code to map repository structure, identify entry points, trace control flow, infer module responsibilities, generate documentation, and build a test scaffold around existing behavior.
Agile retro board Build a small full-stack application from spec to implementation using Claude Code, custom commands, subagents, acceptance criteria, and iterative review loops.
Netflix database analytics via MCP Connect Claude Code to a structured database through MCP, generate and inspect queries, validate assumptions, analyze outputs, and control what the agent is allowed to execute.
Hacked Space Invaders game Debug a broken game inside Docker by using logs, runtime errors, hypothesis-driven investigation, minimal patches, and validation instead of letting the agent guess blindly.
Chrome DevTools debugging Use Claude Code with browser tooling to inspect DOM state, console errors, network activity, UI behavior, and connect runtime failures back to the source code.
Spec-driven development Write implementation-ready specs with constraints, non-goals, interfaces, edge cases, acceptance criteria, and review checkpoints that Claude Code can execute without drifting from the intent.
Agent output review Review generated code like a pull request: inspect diffs, test quality, hidden coupling, deleted constraints, over-engineering, shallow assertions, and architecture degradation.
Core skills covered
Context engineering
Prepare Claude Code with the right repository context, file boundaries, architectural constraints, examples, dependencies, and task-specific instructions before allowing it to modify code.
Spec-to-implementation workflow design
Turn vague requirements into executable specs with interfaces, constraints, non-goals, acceptance criteria, edge cases, and review checkpoints.
Agent task decomposition
Break larger features, bugs, and refactors into small, safe, reviewable agent tasks that reduce drift and make output easier to validate.
MCP workflows
How to connect Claude Code to external context and tools through MCP, including databases, project management systems, documentation, and development tooling.
Generated code review
Review Claude-generated diffs for architectural regression, hidden coupling, deleted constraints, brittle abstractions, shallow tests, and unintended behavior changes.
AI-assisted test generation
Use Claude Code to generate, extend, and refactor tests while checking assertion quality, edge-case coverage, false positives, and gaps between generated tests and real system behavior.
Debugging agent output
How to inspect, redirect, recover, and continue when the agent produces broken, incomplete, or misleading output.
Checkpointing and recovery
Diagnose broken agent output using logs, runtime errors, browser inspection, Docker environments, checkpoints, rollback, and minimal corrective patches.
Autonomy control
Decide when to let Claude continue, when to narrow the task, when to interrupt, when to rewind, and when human engineering judgment should override the agent.
Team-level AI workflows
Create repeatable patterns for using Claude Code across a team, including shared specs, commands, review habits, task boundaries, and quality gates.
Who this is for
Software engineers from any language or stack who want structured hands-on experience with agentic coding workflows.
You should attend if you write code professionally and want to understand how Claude Code can be used beyond simple code completion, pair-programming, or one-off prompting.
You do not need prior Claude Code, Cursor, or AI/ML experience. The exercises use provided repositories and guided development environments.
What participants leave with
Working repositories for every exercise
Reusable patterns for working with Claude Code in real codebases
Practical understanding of MCP-connected development workflows
A clearer approach to spec-driven agentic development
Review and validation techniques for generated code
Experience debugging and recovering broken agent output
A Certificate of Completion from re:cinq, an official Anthropic Claude partner
Slack access with the trainers after the training
Trainers
The training is led by senior engineers who use Claude Code and Cursor in their own work.
Michael Müller — CTO & Co-Founder, re:cinq
Designed the multi-year engineer training programme that supported Adidas’ Cloud Native transformation. Helped define CNCF Kubernetes certification syllabi. Former DevOps lecturer at Lucerne University. Uses Cursor and Claude Code in production work.
Daniel Jones — Head of Product, re:cinq
Created training programmes for The Linux Foundation and VMware. Previously led EngineerBetter, known for immersive mob-programming training. Hosts the Waves of Innovation podcast, interviewing technology leaders on CodeGenAI adoption. Uses Claude Code and Cursor daily.
Michael Czechowski — Senior Engineer, re:cinq
Creator of Wave, a deterministic pipeline tool for AI-assisted development. Built the CFO Agent using spec-driven methodology. Lecturer at HdM Stuttgart and DHBW Stuttgart. Focuses on production validation of AI-generated code.
Practical details
The training is capped at 15 engineers. Registration closes once the cohort is full.
Invoices are available for employer reimbursement.
Cancellation policy: full refund up to 14 days before the training, 50% refund within 14 days.