Remote or On-site1 DayTokyo, Japan

AI Engineering for Dev Teams in Tokyo

Your team in Tokyo is already using AI tools — but there's no consistent workflow. This 1-day workshop closes that gap. From setting up AI dev environments with Cursor and Claude Code to shipping with AI-integrated code review and CI pipelines, on your actual codebase.

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Why Take This Workshop in Tokyo?

Tokyo is Asia's leading tech capital with a massive engineering workforce. Japanese companies are rapidly modernizing their web stacks with TypeScript, Angular, and React. Our AI engineering workshop in Tokyo helps dev teams go from ad-hoc AI usage to a structured, production-grade AI workflow — in one day.

Workshop at a Glance

Duration

1 Day

Format

Remote or On-site

Participants

Up to 15 participants

Price

€4,500/team

What Your Team Will Learn

This workshop is for dev teams already shipping code with AI tools — or about to start. By the end of the day, every engineer has a configured environment, a shared review process, and a playbook they actually follow.

A fully configured AI-native dev environment for every engineer
A shared code review process designed for AI-assisted development
Clear rules for when to trust, edit, or reject AI-generated code
An AI-integrated CI/CD pipeline with security and quality gates
A written AI engineering playbook your team actually follows

Workshop Curriculum

From AI-native dev environments to production CI pipelines — your team works through real scenarios: writing features with AI, reviewing AI-generated code, catching bugs before they ship, debugging in production, and defining ownership when AI writes most of the code.

AI-native dev workflows — from setup to shipping

What an AI-native engineering workflow looks like in 2026
Cursor and Claude Code in practice: agent mode vs. completion mode
Context management: .cursorrules, architecture docs, README hygiene
Hands-on: set up your AI environment and ship a feature on a real codebase
AI in the PR and review cycle: CodeRabbit and alternatives
AI-powered static analysis in CI: tests, security, and quality gates
AI-assisted debugging: logs, traces, errors → root cause
The handoff problem: code ownership when AI writes 80% of a feature
Debrief: build your team's AI engineering playbook

Tools & Focus Areas

IDE

Cursor & Claude Code

AI-assisted coding in practice. Context management, agent mode, and building a workflow that scales across a team.

Review

CodeRabbit & PR Review

AI as a first pass in your review cycle. Integrating automated review into PRs without replacing human judgment.

CI/CD

Snyk, Semgrep & CI

AI-powered static analysis, security scanning, and quality gates in your CI pipeline for AI-generated code.

Your Instructor

Davy Engone

Founder & Software Mentor @ Hackages

Davy has trained 5,000+ engineers and built AI agents in production. This workshop is what he wishes every dev team had — practical, hands-on, and designed to change how your team works with AI starting the next day.