What happens when you need to refactor 377 files in a SvelteKit codebase? Without AI, you retire. With Claude Code, you ship it in 11 hours.

This talk walks through real-world examples of using Claude Code for massive refactoring work that would be soul-crushing to do manually. I'll cover:

• How I approach large-scale refactors with Claude (377 files → 364 files → and counting)

• Building and activating Claude Code skills reliably (based on my viral post that's getting 4k+ views in 3 days)

• The workflow patterns that actually work vs the ones that waste your time

• Real examples from production codebases

This isn't about AI replacing developers - it's about using AI to handle the tedious parts so you can focus on the interesting problems.

Justine discutera de la rémédiation automatique du code avec le moteur de refactorisation déterministe OpenRewrite, une technologie née chez Netflix en 2016. Il est conçu pour manipuler la représentation de l'arbre sémantique sans perte (LST) du code à l'aide de recettes (programmes) qui aboutissent à des transformations de code précises, tout en préservant le style à 100 %. Vous verrez comment associer la précision d'un système basé sur des règles à la puissance généraliste de l'IA. Divers cas d'utilisation seront présentés, tels que l'obtention d'une vue d'ensemble sémantique de votre code, la recommandation de modernisations adaptées à votre base de code, et la manière dont les LSTs fonctionnent comme guide et garde-fou pour les modèles.

🇬🇧 Justine will discuss automated code remediation with the deterministic OpenRewrite refactoring engine, a technology born at Netflix in 2016. It’s built on manipulating the Lossless Semantic Tree (LST) representation of code with recipes (programs) that result in 100% accurate style-preserving code transformations. You will be shown how to couple the precision of a rules-based system with the generalist power of AI. Various use cases will be presented such as getting a semantic-based bird's eye view of your code, getting codebase-tailored recommendations for modernization, and how LSTs function as a guide and guardrails for models.

In this session, I’ll explore how AI-powered tooling is transforming large-scale refactoring and codebase migrations, making these complex tasks faster and more efficient. By leveraging tools like Large Language Models (LLMs), static analysis, and refactoring frameworks, we can automate repetitive code transformations, accelerate migration paths, and reduce human error. I’ll share practical examples of how we can migrate legacy systems to modern frameworks, break monolithic architectures into service-based structures, and automate large-scale code changes across thousands of files.

I’ll demonstrate how tools like OpenAI Codex, GitHub Copilot, and fine-tuned models for domain-specific transformations can assist in these processes, while still integrating with traditional migration tools and CI/CD systems. I’ll also cover the importance of developer oversight, highlighting lessons learned from real-world production rollouts and how to balance automation with manual reviews.

By the end of the talk, I want attendees to walk away with a clear understanding of how to use AI to enhance their refactoring and migration workflows, ensuring they can handle large-scale transformations while maintaining code quality, consistency, and governance.