Modern Languages & Infrastructure at QCon San Francisco 2025

Maintaining up-to-date and secure software across a polyglot fleet is a challenge for any engineering organization. Manual migrations and urgent updates disrupt productivity and require coordination across many teams.

At Uber’s scale, ensuring an always-green mainline while processing hundreds of changes per hour is a massive challenge— especially when those changes span multiple language monorepos supporting dozens of business-critical apps.

Developing SDKs for your users in multiple languages can come at a high cost - especially if you need to implement complex logic client side, but traditionally options for sharing logic across those languages have been quite limited.

Details coming soon.

Claude Code is the first developer tool built specifically to maximize AI development velocity.

Front-end has long been about reactivity frameworks and client-side state management. However, the alpha in these is receding.

Reinforcement Learning with Performance Feedback (RLPF) unlocks a new way of turning generic GenAI models into customized models fine-tuned for specific tasks. This approach is especially powerful when combined with in-house data and performance metrics.

AI agents are only as strong as the infrastructure beneath them. In this talk, we’ll walk through the architecture behind Browserbase’s model context protocol (MCP), built to support stateful browser automation at scale.

Rust makes things faster but it slows down engineers, right? This was our team’s assumption when we decided to rewrite our code from Kotlin into Rust. But, we were wrong in completely unexpected ways.

In high-performance code, a single misplaced counter increment can cost more than the operation it’s measuring. That creates a paradox: instrument too much and you slow the system down; instrument too little and you miss the insights you need to continuously deliver.

Ever wondered what happens when a bunch of performance-obsessed developers decide their blazing-fast database isn't quite blazing-fast enough?

In order to improve the performance of existing applications and services, we can identify the most performance-critical pieces and reimplement them in Rust as opposed to completely rewriting the applications from scratch.

As AI applications demand faster and more intelligent data access, traditional caching strategies are hitting performance and reliability limits.

The popularity of Python means insurance and financial services companies have a growing body of actuaries, quantitative developers, and software engineers capable of building innovative and customized solutions for both data management and modeling.