Opening Remarks

You Don't Have Any Good Ideas

Everything We got Wrong About RPI

No Vibes Allowed is the second most-watched talk of the 2025 AI engineer code summit, and in the top five most-watched AI Engineer talks of all time. From it, you know I'm a big fan of Research / Plan / Implement. For the last 6 months, we've been working with large orgs (hundreds or thousands of engineers) to adopt advanced context engineering techniques for coding agents. We learned a ton of things we got wrong, and are here to share some lessons trying to scale these techniques to large teams working on a broad variety of complex codebases with hundreds of repos spanning platform, product, DevOps, and more. We'll talk about the shortcomings of previous approaches, and how we're now breaking down research and planning into more discrete steps, using control flow instead of prompting for managing workflows, and unique perspectives on "what is PR slop, where does it come from, and how to combat it", and a whole lot more.

What Does Good Taste in DX Look Like in the Age of Agents?

As coding agents become part of the development workflow, great developer experience is no longer just about features, speed, or abstraction—it is about taste. What should feel seamless, what should stay explicit, and how do you build tools at the right level of abstraction so that agents can use them well immediately? This panel will explore what good taste in developer experience looks like in the age of agents: the judgment to create developer experiences that are intuitive, legible, and trustworthy for humans and agents alike.

From Local to Remote: Working with Coding Agents over SSH

Many engineers have already adopted terminal-based coding agents. This talk is about the next step: moving those workflows off your laptop and onto remote machines. I’ll show why I like running coding agents over SSH: remote machines stay up 24/7, don’t compete with local resources, make persistent sessions easier, and create a better environment for long-running or multi-agent work. We’ll cover remote machine choices, tmux workflows, CLI agent options, terminal-friendly browsing and screenshot tools, TUI development, editors, and review workflows. If you already use coding agents in the terminal, this talk is about how to make that setup more durable, scalable, and agent-native.

The Rise of AI Agents in the Wild

Static benchmarks tell only part of the story. At OpenRouter, we observe a different reality—one shaped by billions of real-world requests, rapidly evolving models, and agents operating in production. In this talk, we’ll explore how the shift toward agent-driven workflows is redefining how we evaluate model performance. We’ll look at data from across the stack to understand trends like exploding token usage, longer context windows, and the rise of tool-calling systems. Along the way, we’ll highlight what actually matters in practice: reliability, cost, and the ability for models to take meaningful actions. Beyond benchmarks, you’ll see how real-world usage reveals the true capabilities—and limitations—of modern AI systems.

How to Embed AI Code Quality Gates in Your SDLC

AI is a force multiplier that turns weak standards into architectural chaos. As code review becomes the ultimate bottleneck, engineering teams must bridge the gap between human intuition and machine output. This talk introduces a holistic framework for designing quality-driven systems around AI coding. We'll explore how to codify development best practices into machine-readable guardrails. We'll walk through how to leverage context engineering at scale to ensure your AI code tools respect your system’s design, preserving long-term maintainability without sacrificing the speed of the AI era. Attendees will learn how to embed code quality into the SDLC from AI-generated output. They will leave with a practical methodology for encoding best practices into their workflow, where developers already operate (CLI, IDE, and Git).

Software Development Now Costs Less Than Minimum Wage

The cost of software development has fallen to $10.42 an hour—less than minimum wage. A burger flipper at Macca's earns more. What does it mean to be a software developer when everyone in the world can develop software? Tools like Cursor have commoditised the knowledge and skill of software development, enabling non-developers to build and ship. In this talk, Geoffrey Huntley shares a cold, stark view of how AI is reshaping the unit economics of business. Drawing on a year of game theory around Ralph Loops, and conversations with venture capitalists in Australia, South Korea and San Francisco, he explores the K-shaped divergence: model-first companies operating as lean apex predators versus incumbents struggling through people transformation.

How to quantize models (without killing quality)

Four-bit quantization has a bad reputation for destroying model quality. While it’s true that post-training quantization in 4-bit integer formats makes models noticeably worse, new microscaling data formats like MXFP4 and NVFP4 deliver on the promise of fast low-precision inference without meaningful quality loss. This talk introduces these data formats along with a shift from quantization as a binary decision to quantization as a granular process with model-level considerations (quantization across weights, activations, KV cache, attention) and layer-level considerations (quantization of input, output, and hidden layers) to help you preserve quality while accessing improved performance and cost characteristics from low-precision inference.

From Prompt to Production: Maximizing Value with Google's GenMedia Models

Unlock the product potential of Google’s latest generative media models in this focused deep dive. Move beyond the hype and discover how to extract tangible user value from cutting-edge models like Veo, Nano Banana, and Lyria.

From Tickets to PRs: Shipping a Governed Snowflake Ops Agent with LangGraph and MCP

Most AI agents never make it past the experiment phase, especially when they touch sensitive data and regulated workflows. At Pinterest, we built Agent Snowy, a LangGraph‑based agent that automates routine Snowflake data warehouse operational requests end‑to‑end, designed to cut median resolution time from hours down to minutes for supported flows. The agent takes requests from Slack and Jira ticket intake through to generating auditable SQL and GitHub PRs, all without direct write access to production. This talk will walk through how we wired LLMs, the Model Context Protocol (MCP), and existing CI/CD pipelines together, and the concrete guardrails we put in place to keep the system secure and compliant. Attendees will leave with a practical blueprint for turning their own routine operational tickets into safe, auditable agent workflows—without handing the keys to production over to an LLM.

Build a Free Agent

Most AI assistants are still just chats with tool access. This talk shows a different approach: using MCP as a personal runtime. I'll walk through how Kody uses `search`, `execute`, and `open_generated_ui` to discover capabilities, run sandboxed workflows, manage and use memory, keep secrets out of prompts, and turn generated interfaces into reusable software. The goal isn't a better model. It's making AI assistants portable, secure, and actually useful across MCP hosts. In this talk, you'll learn that with the right primitives, you can create a highly capable assistant without paying an extra cent for inference.

Building Infrastructure That Scales to a Billion (or Trillion!) Agents

Here's some napkin math: 100 million US knowledge workers, 15% concurrency, one agent each. That's 15 million simultaneous sessions. Now give each person three agents. Now do the rest of the world. Containers can't touch this. So let's talk about things agents actually need: a way to get their own compute instantly without dragging along an entire OS, and the ability to just write code when tool-calling gets awkward (which is often). Dynamic Workers give you an isolated execution environments that materialize in milliseconds and vanish when they're done. We'll talk all the details how it works, what it looks like in practice, and what's next.