Build AI that delivers in production.

Modern AI platforms don’t have to choose between deterministic precision and probabilistic exploration—they need both.

Since the first compiler, software has been a stack of human-friendly abstractions translated into machine instructions. The engineers who understood at least some of what was going on under the hood were essential—indispensable even. But now?

Details coming soon.

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

Deep Research as a consumer product redefined the AI space delivering true impact to many by searching through hundreds of websites, deeply thinking through the content, and generating a comprehensive report.

How do you deliver relevant and personalized recommendations to nearly a billion professionals—instantly, reliably, and at scale? At LinkedIn, the answer has been a multi-year journey of architectural reinvention.

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.

Delivering AI-powered features in mobile apps is not just about calling an LLM API. It's about crafting fast, reliable, and engaging user experiences.

This talk presents a comprehensive journey through modern AI post-training techniques, from Pinterest's production-scale content discovery systems to enterprise agent training through Veris AI’s simulation.

In this talk, we’ll walk through how DoorDash is redefining personalization by tightly integrating cutting-edge large language models (LLMs) with deep learning architectures such as Two-Tower Embeddings (TTE) and Multi-Task Multi-Label (MTML) models.

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.

AI inference is expensive, but it doesn’t have to be. In this talk, we’ll break down how to systematically drive down the cost per token across different types of AI workloads.

The AI coding agent landscape evolves weekly. This talk compares today’s frontrunners, shows where each shines, and shares prompts, policies, and “rules templates” that turn code suggestions into production-quality output.

Over the past year, Zoox has invested in integrating Large Language Models (LLMs) into internal developer workflows through a company-wide initiative called Zoox Intelligence (ZI).

Details coming soon.

Behind every productivity leap is a rock-solid platform. Go under the hood with Intuit’s GenOS team to see how vector stores, prompt management, RAG pipelines, and agent orchestration come together to serve ~100 million users.

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

In this session you'll learn how product leaders turn GenAI enthusiasm into an enterprise-ready blueprint for real productivity gains.

As AI accelerates delivery, new attack surfaces and compliance risks emerge. This session distills best practices for threat-modeling AI pipelines, guarding sensitive data, detecting prompt-injection, and validating AI-generated code before it merges.