Fireside chat
People have long been the audience for the software we build. What happens when agents become customers too? Tobias Lütke and Guillermo Rauch discuss the implications for commerce, application design, and the relationship between developers and the users—human or machine—on the other side of an interface.
Awards ceremony
Join the Ship community to celebrate the people and projects building with Vercel. The Vercel Awards bring the program together for a moment of recognition and a look at what developers are making possible.
Workshop
Eve is Vercel's framework for durable backend agents, and it starts with a directory. In this hands-on session, you'll go from empty folder to running agent in under an hour: filesystem-first authoring, the channel/harness/workflow runtime, and shipping agents that survive restarts. No experience required.
Talk
Most agents handle one prompt and a quick burst of work. Hyperagent runs agents that wake on a schedule, work for hours in real browsers and code environments, and escalate to humans. Built on Vercel Sandbox with AI Gateway, we tested 14 models on 42 real jobs and found a 50x cost gap on identical tasks.
Talk
Learn how SpaceXAI's `.grok.me` surface runs 300k+ subdomains and 125M monthly Edge Requests on Vercel. Understand the infrastructure powering Grok and, most importantly, use Grok to ship.
Demo
Vercel isn't just for frontends anymore. With Fluid Compute and Framework-Defined Infrastructure, you can deploy real backends, FastAPI, Express, NestJS, from a single git push. In this session we will ship a full-stack app live: frontend, backend, and database, all in one workflow.
Workshop
Build an agent into a Next.js application that works with application data and generates interactive tools. This 40-minute workshop brings together Codex and Vercel Sandbox for a focused, end-to-end demonstration.
Talk
See how Notion uses Vercel's primitives to build a platform designed for both developers and AI agents.
Talk
Building a custom agents platform is one of the key pieces to unlocking AI for complex business workflows. Monte Carlo is the sales-specific system we've built to help scale Monaco's revenue: a platform where revenue teams encode their own playbooks as agents that run against everything we've ingested about their business, including pipeline, outbound, calls, and CRM. Sales is a particularly complex vertical for the following reasons: no two revenue orgs look alike, the data is large and heterogeneous, and the expertise that matters lives in per-rep workflows nobody has written down. Making it work at scale meant rebuilding the agent runtime on per-run Vercel Sandbox microVMs, a decision that propagated much further than we expected into how we handle context, how much of a playbook ends up as prose versus code, and what an agent is permitted to do at all. In this talk, I'll break down the components a verticalized agent platform needs in order to be fast and correct, including an execution substrate, memory and context, how expertise gets represented, and the evaluation loop, along with what is still unresolved.
Talk
The real world runs on conversation, yet today most ai agents live inside a chat box. Iz will spend time digging into this problem, and share why voice is the next interface for agents, what it takes to make them feel truly human, and our journey in creating the most natural, human-like voice models in the market
Talk
The software shift in robotics has arrived. A new generation of AI models, simulation tools, and edge computing is expanding what robots can learn and do. Drawing on his work building Gambit, Ben explores why these advances are setting up an explosion in robotic capabilities
Talk
AI coding tools make it easy to track tokens, licenses, and model spend. The harder question is what that spend actually produces. In this session, Emilie Schario, VP of Engineering at Anaconda and Co-Founder of Kilo Code, introduces AI Cost to Ship, a metric that connects AI spend with merged pull requests, and explores how leaders can use it alongside adoption, quality, and model choice to understand whether AI investment is translating into shipped engineering output.
