Video Library
JAX London
The Conference for
Enterprise AI Engineering
Selected features, talks and interviews from JAX London
Sessions
Beyond the prompt: Evaluating, testing, and securing LLM applications l
Mete Atame
Pimp Your LLM! – Model Context Protocol with Spring AI
Kai Tödter
Applying Diffusion to Technology Innovation
Jeremy Deane
Observed, Not Scanned: The Future of AppSec
John D Wood
Intelligent Applications with Spring AI
Patrick Baumgartner
Application Modernization made easy with OpenRewrite and AI
Emily Jiang
All the way to 11: Delivering new features in the JDK
Simon Ritter
Doing Software Architecture Continuously
Eoin Woods
Rethinking JSON in Functional Kotlin: Kondor‑JSON
Uberto Barbini
Before you continue…
The reading list you'd build – if you had time.
Weekly
Articles + tutorials
The reads you'd find if you had time
2× / mo
Live webinars
Experts you can actually ask
Monthly
Magazine + whitepapers
Deep dives worth your weekend
On-demand
Recordings + courses
Past conferences, ready when you are
Explore other Tracks
Engineering Teams in the Age of AI
The team structures that worked yesterday need rethinking for AI adoption
The team structures that worked yesterday need rethinking for AI adoption
DevOps, CI/CD & Platform Engineering
The platform is what makes AI systems trustworthy in production
The platform is what makes AI systems trustworthy in production
Software Architecture for Intelligent Systems
Designing systems that stay coherent as intelligent capabilities enter the architecture.
Designing systems that stay coherent as intelligent capabilities enter the architecture.
Software Engineering Practices
Engineering judgment is what gives AI-generated code its quality.
Engineering judgment is what gives AI-generated code its quality.
AI in Development & Agentic Systems
The engineer who understands systems is the one who can direct AI
The engineer who understands systems is the one who can direct AI
Enterprise AI: Security, Scale & Performance
Keeping AI systems secure, cost-efficient, and stable under real-world conditions.
Keeping AI systems secure, cost-efficient, and stable under real-world conditions.