Most developers using AI coding tools hit the same wall on real projects: the demos look magical, but production work falls apart. This workshop shows why — and what to do about it.The core lesson: execution is fast and cheap only when preparation is deep.What we'll build, liveFrom an empty directory to a deployed production app — a mobile-first Conference Companion App with the event schedule, speaker profiles, search, and favourites. Attendees will open it on their phones before the session ends.
The four stages of shipping with AIYou'll see a complete production workflow, deliberately weighted toward the work that actually determines quality:- Research — Gathering everything the AI will need upfront: official docs, code patterns, real data. By execution time, nothing has to be searched for. This is where quality comes from.- Planning — Breaking the app into clear tasks with acceptance criteria, written live. Skipping this is why most AI builds fail.- Execution — Ten tasks running in parallel via git worktrees, orchestrated by Navigator (a Claude Code plugin) on a React/Next.js stack. Code and docs written together.- Review & Ship — Quality gates (tests, lint, types, build), clean commits, merge, deploy to Vercel. Live URL shared with the room.
Bonus: Human + Claude Code vs. fully autonomous agentWhile the live build runs, Pilot — our autonomous coding agent — builds the same app from the same spec, on its own, in a separate repository. At the end, we open both side by side: two working apps, two pull request histories, two live URLs. One human-driven, one fully autonomous.
You'll leave withA repeatable workflow for shipping real software with Claude Code, plus concrete techniques you can apply to your own projects on Monday morning.

There is no single correct way to assemble a software factory. Labs want us to trust agents more than we should. Tech influencers gloss over important, practical details. This workshop introduces several composable patterns for making a codebase more factory-like: to improve the throughput, quality, and verifiability of autonomous agent work in your project.We will cover: - Planning the work that gets handed over to a software factory- Converting plans and specifications to durable internal docs (and rules) that don’t go stale- Task decomposition for coding agents, and the tools that help with it- Review strategies and tools that can manage massive PRs - Agent-friendly QA setups that allow coding assistants to verify their work and fix bugs before human review- When you can safely skip human review entirely- The role of sandboxes (local and remote) for scaling and handling agent work on your codebase

AI coding assistants are evolving from simple autocomplete to autonomous agents that can interact with external systems. But how do you teach an agent to follow your workflows and use the right tools at the right time?In this hands-on workshop, you'll discover:What MCP (Model Context Protocol) is and how it standardizes tool integration for LLMsHow Agent Skills package domain-specific knowledge and workflows for AI agentsThe key differences between MCP servers and Skills, and when to use eachHow to create a custom Skill that orchestrates multiple MCP tools into a cohesive workflowBy the end of this session, you'll build your own AI Skill that uses MCP tools — a Matrix-themed Neo fighting skill.Who should attend: This workshop is ideal for software developers who use AI coding assistants (Claude Code, Cursor, or similar) and want to extend their capabilities with custom integrations and workflows. No prior MCP or Skills experience required — just bring your curiosity and a laptop.

The workshop focuses on theory and practice building production-ready AI agents with Node.js and modern Agent SDKs. We will use a real codebase as an execution environment to explain the core concepts behind agents: the agent loop, tool calling, structured outputs, context management, guardrails, tools, and human approval. We will build a Node.js SDK-based engineering agent that receives a development task, inspects a repository, proposes and applies safe code changes, runs validation checks, and exports execution artifacts to a messenger/storage. We will also cover how this agent fits into a broader production architecture: where MCP, orchestration, multi-agent patterns, CI/CD, security boundaries, observability, and workflow tools such as n8n may be useful. By the end of the workshop, participants will understand not only how to use an Agent SDK, but also what remains their responsibility when designing safe and maintainable agent runtimes.

In this workshop we'll dive into the latest advancements in React and best practices for building modern React apps. We'll take a look at modern NextJS and React Router 7 Framework along with React's "React Server Components". We'll also talk about improving the data-fetching strategies of your SPAs along with options for migrating your SPA to modern React Router.

In this workshop we'll dive into the latest advancements in React and best practices for building modern React apps. We'll take a look at modern NextJS and React Router 7 Framework along with React's "React Server Components". We'll also talk about improving the data-fetching strategies of your SPAs along with options for migrating your SPA to modern React Router.

In this workshop we'll dive into the latest advancements in React and best practices for building modern React apps. We'll take a look at modern NextJS and React Router 7 Framework along with React's "React Server Components". We'll also talk about improving the data-fetching strategies of your SPAs along with options for migrating your SPA to modern React Router.

In this workshop we'll dive into the latest advancements in React and best practices for building modern React apps. We'll take a look at modern NextJS and React Router 7 Framework along with React's "React Server Components". We'll also talk about improving the data-fetching strategies of your SPAs along with options for migrating your SPA to modern React Router.

AI is writing more code every day. The skill that won't get automated is knowing what to build and the system design thinking to make it happen at scale.

In this full-day workshop, you will develop the judgment that sits upstream and downstream of implementation: how to validate a problem before you commit to a solution, how to understand the users you are actually building for (not the ones you imagine), how to prioritize ruthlessly when everything feels urgent, and how to establish feedback loops that keep you building the right things after you ship.

These are not PM skills. They are the technical judgment that the best engineers have always had, and that becomes the defining skill as implementation gets faster and cheaper.

What you will learn:- Validate before you build: surface real user pain, not solution-shaped stories- Understand users in context: close the gap between your mental model and theirs- Prioritize what deserves to exist: apply the Kano model and know what to cut- Translate decisions for stakeholders: navigate contested requirements productively- Close the loop: establish post-ship feedback habits that drive continuous improvement
Who it's for: Software engineers with professional experience. No specific stack required. PMs and designers welcome.

AI is writing more code every day. The skill that won't get automated is knowing what to build and the system design thinking to make it happen at scale.

In this full-day workshop, you will develop the judgment that sits upstream and downstream of implementation: how to validate a problem before you commit to a solution, how to understand the users you are actually building for (not the ones you imagine), how to prioritize ruthlessly when everything feels urgent, and how to establish feedback loops that keep you building the right things after you ship.

These are not PM skills. They are the technical judgment that the best engineers have always had, and that becomes the defining skill as implementation gets faster and cheaper.

What you will learn:- Validate before you build: surface real user pain, not solution-shaped stories- Understand users in context: close the gap between your mental model and theirs- Prioritize what deserves to exist: apply the Kano model and know what to cut- Translate decisions for stakeholders: navigate contested requirements productively- Close the loop: establish post-ship feedback habits that drive continuous improvement
Who it's for: Software engineers with professional experience. No specific stack required. PMs and designers welcome.

In this hands-on workshop, you’ll learn how to integrate AI directly into your application using TanStack AI. We’ll build a working AI chat feature end-to-end, starting from server setup and finishing with a streaming client, tool calling, human-in-the-loop workflows, and real debugging using TanStack DevTools.

Through guided exercises, you’ll learn how to set up TanStack AI on the server, connect a client application to a streaming AI backend, build a functional chat interface, create your first AI tools, and implement approval flows so humans can stay in control when tools are invoked.

By the end of the session, you’ll understand the core building blocks of AI-powered applications and walk away with a solid foundation for adding intelligent chat and tool capabilities to your own apps.

Workshop outcomesWhen you're finished with this workshop you will:Understand how to set up TanStack AI on the server and wire it to a provider (like OpenAI)Know how to connect a client app to a streaming AI endpoint and handle incremental updates cleanlyBuild a functional chat UI with a solid state model for messages, streaming tokens, and tool resultsCreate and use your first AI tools with clear inputs, outputs, and predictable behaviorBuild human-in-the-loop workflows with tool approvals so your app can ask for confirmation before executing sensitive actionsDebug and inspect your AI app using TanStack DevTools, including tool calls, responses, and streaming behaviorWhat you'll learn

TanStack AI gives you the primitives to build real AI features, not just a demo prompt box. This workshop focuses on wiring everything together properly, from server-side streaming to client UX, then layering in tools, approvals, and debugging so the final result is something you can confidently evolve into production features.

You’ll learn the following through these exercises:Server setup - Configure TanStack AI on the server, connect to your model provider, and expose endpoints that support streaming chatStreaming and SSE - Implement and consume streaming responses, understand the lifecycle of a stream, and build UI that stays responsive while tokens arriveChat UI and state - Build a chat interface that handles message history, partial responses, loading states, and tool outputs in a clean wayTools - Define tools, validate inputs, return structured outputs, and integrate tool results back into the conversation flowHuman in the loop - Add approval steps for tool execution, implement “approve/deny” flows, and keep users in control when actions matterDebugging with TanStack DevTools - Inspect requests, responses, tool calls, timing, and streaming behavior so you can troubleshoot fast and iterate safelyPrerequisites

This workshop assumes you can build and run a React + TypeScript app locally and you are comfortable working with a basic server setup.Basic understanding of SSE and streaming is required (we’ll use streaming heavily throughout the workshop)Experience with React is required (components, state, props, rendering lists)Basic TypeScript knowledge is required (we’ll rely on types for tools and structured outputs)You will need an OpenAI API key with available credits to use during the workshop exercises

This workshop bridges the long-standing gap between React developers and production-ready delivery. Many front-end developers know how to build great interfaces but they stumble when it comes to building, testing, deploying, and monitoring those apps in real-world environments.This workshop gives React developers the DevOps superpowers they need.You’ll learn how to:Dockerize your React application for development and production following best practices from Docker Captain Leader.Build scalable CI/CD pipelines using GitHub ActionsOptimize your apps for performance and reliabilityThis is not theory. It’s not a shallow overview. This is a production-focused, real-world workshop by a front-end developer who lives and breathes this every day.