MCP Origins and Goals - MCP was created to address the challenge of constantly copying and pasting context into LLMs, aiming to give models the ability to interact with the outside world [4][5][6] - The goal is to establish an open-source, standardized protocol for model agency, enabling broader participation in the ecosystem [7][8] - Anthropic believes that enabling model agency is crucial for LLMs to reach the next level of usefulness and intelligence [8] MCP Development and Adoption - MCP was initially developed internally and gained traction during a company hack week [9][10] - Early feedback questioned the need for a new protocol and its open-source nature, given existing tool-calling capabilities [12][13] - Adoption by coding tools like Cursor marked a turning point, followed by broader adoption from Google, Microsoft, and OpenAI [14] Protocol Principles and Updates - The protocol prioritizes server simplicity, even if it increases client complexity, based on the belief that there will be more servers than clients [20][21] - Recent updates include support for streamable HTTP to enable more birectionality for agent communication [19] - Future development focuses on enhancing the agent experience, including elicitation to allow servers to request more information from end users [26][27] - Plans include a registry API to facilitate models finding MCPs independently, further supporting model agency [28] Ecosystem Opportunities - The industry needs more high-quality servers across various verticals beyond dev tools, such as sales, finance, legal, and education [31][34] - There is a significant opportunity in simplifying server building through tooling for hosting, testing, evaluation, and deployment [36] - Automated MCP server generation is a potential future direction, leveraging increasing model intelligence [37] - Tooling around AI security, observability, and auditing is crucial as applications gain more access to external data [38]

Join us to see how VS Code and GitHub Copilot's expanding suite of AI features can match or even surpasses the benefits of other popular AI developer tools. We'll focus on practical scenarios to ensure immediate applicability and work through live demos of Copilot features such as: Code generation using Edits, Planning/problem solving using Chat, Inline terminal command generation, Boilerplate code generation using Agent mode, Improving boilerplate with custom instructions and then refactoring using Agent m ...

As AI reshapes the global balance of power, the infrastructure behind it—chips, data centers, power, and supply chains—has become a new arena for geopolitical competition. This talk explores how nations are racing to secure critical AI hardware, control compute capacity, and assert influence over the technologies and talent that define the future. About Dylan Patel Dylan is the founder, CEO, and Chief Analyst for SemiAnalysis, the preeminent authority on all things AI and semiconductors. Through Dylan’s unw ...

Industry Focus: Autonomous Agents in Healthcare - The workshop explores building autonomous agents for managing complex processes like multi-day medical treatments [1] - The system aims to help patients self-administer medication regimens at home [1] - A key challenge is enabling agents to adhere to protocols while handling unexpected patient situations [1] Technology Stack - The solution utilizes a hybrid system of code and prompts, leveraging LLM decision-making to drive a web application, message queue, and database [1] - The stack includes LangGraph/LangSmith, Claude, MCP, Nodejs, React, MongoDB, and Twilio [1] - Treatment blueprints, designed in Google Docs, guide LLM-powered agents [1] Agent Evaluation and Human Support - The system incorporates an agent evaluation system using LLM-as-a-judge to assess interaction complexity [1] - The evaluation system escalates complex interactions to human support when needed [1] Key Learning Objectives - Participants will learn how to build a hybrid system of code and prompts that leverages LLM decisioning [1] - Participants will learn how to design and maintain flexible agentic workflow blueprints [1] - Participants will learn how to create an agent evaluation system [1]

Company Overview - Browserbase provides infrastructure connecting large language models and the web, enabling end-to-end workflow automation [1] - Browserbase views itself as the "last-mile" interface between large language models and the web [1] Funding & Investment - Browserbase raised $27.5 million in its first 12 months [1] - The funding includes a $6.5 million seed round and a $21 million Series A [1] - CRV, Kleiner Perkins, and Okta Ventures led the Series A funding [1] Technology & Innovation - The web browser may become the default MCP server for the internet, enabling production AI Agents [1] - Browserbase offers fast, reliable, multi-region headless-browser infrastructure for developers and AI agents [1]

Model Overview - Google DeepMind's Veo 3 is a state-of-the-art video generation model [1] - Veo 3 generates video with synchronized audio from text and image prompts [1] - Veo 3 understands intricate details, maintains coherence, and simulates realistic physics and camera movements [1] Capabilities & Features - Veo 3 offers advanced capabilities like semantic context rendering and cinematic control [1] - Veo 3 can generate dialogue, sound effects, and music [1] Accessibility & Integration - Veo 3 is accessible via Vertex AI (preview) [1] - Developers can integrate Veo 3 into their workflows or test it in the Gemini App, Flow, and via the Gemini APIs on Google Cloud [1] Potential Applications - Veo 3 empowers innovation in filmmaking, game development, and education [1]

Bad designs are still bad. AI doesn’t make it good. The novelty of AI makes the bad things tolerable, for a short time. Building great designs and experiences with AI have the same first principles pre-AI. When people use software, they want it to feel responsive, safe, accessible and delightful. We’ll go over the big and small details that goes into software that people want to use, not forced to use. About John Pham I'm John Pham, an engineer and a self-taught designer. I seek the dopamine hits of buildin ...

You've made the demo. How do you make the product? A lot of AI products don't actually work. Even worse, a lot of the techniques being advertised for making AI products better don't work either. We'll cover the challenges + techniques we've seen actually work in the real world. About Ben Hylak Ben Hylak is co-founder at Raindrop, building Sentry for AI products. He was previously a designer at Apple for 4 years, building the Apple Vision Pro. About Sid Bendre Sid Bendre is the co-founder of Oleve, a company ...

AI Model Fine-Tuning and Prompt Engineering - Workshop covers SFT, DPO, RFT, prompt engineering/optimization, and agent scaffolding [1] OpenAI Expertise - Ilan Bigio, a founding member of OpenAI's Developer Experience team, leads technical development for Swarm, the precursor to the Agents SDK [1] - Ilan Bigio contributed to Codex CLI and created the AI phone ordering demo showcased at DevDay 2024 [1] - Ilan Bigio partnered with companies like Cursor, Khan Academy, and Klarna to shape their AI products [1] AI Application and Development - Ilan Bigio created ShellAI, an open-source, AI-powered terminal assistant [1] - OpenAI provides in-depth technical guides on topics like Function Calling, Latency Optimization, and Agent Orchestration [1] Educational Background - Ilan Bigio designed and taught courses at Brown [1]

Code is the lingua franca for both software engineers and highly capable AI models. As we give agents the ability to build, test, and run code that they generate, the command line becomes their canvas—and their attack surface. This keynote explores what it takes to bring code-executing agents from research to real-world deployment while maintaining control and security. We’ll cover how terminals offer AI an ideal interface, why they’re deceptively risky, and what it means to embed security, guardrails, and ...