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

All it takes is a module and a CLI tool to build and deploy production peer-to-peer applications.In this workshop:Convert an existing React application so that it no longer needs a server to deployPrepare the application and setup deployment flow for production releaseGo to production with multisig - decentralized quorom-based cryptographic signoff gated releasesThis is for anyone who builds with React or JavaScript and anyone who deploys applications.

Every engineering organisation is experimenting with AI coding assistants, but few have built production-grade LLM integrations into their core developer infrastructure. I have such an experience, and I will share real patterns from deploying an AI-powered code review system across a 400+ person engineering organisation (~200 developers) — covering a competitive evaluation of 4 tools across 18 dimensions, building a webhook-based review architecture with slash commands and auto-review, evolving context enrichment from static rules to AI-powered document selection, managing a 4-model fallback chain on Vertex AI, and measuring impact through a feedback dashboard. Attendees will leave with a battle-tested
playbook for integrating LLMs into their own engineering workflows — not as toys but as production infrastructure.

Workshop Structure
1. The Code Review Bottleneck at Scale
2. Tool Evaluation — 4 Candidates, 18 Dimensions
3. Architecture — Webhook Server & Auto-Review
4. Context Enrichment — From Path Rules to AI- Document Selection
5. Model Strategy — Migration & Fallback Chain
6. Measuring Impact — Feedback Dashboard

Discover the power of Server Components in React without the need for a framework. In this workshop, we will explore how to build a server-side rendering solution using "only" vanilla JavaScript and Node.js. We'll dive into the core concepts of Server Components, including how to render components on the server and manage data fetching. By the end of this session, you'll have an understanding of how to implement Server Components in your own projects without relying on a framework.

Every engineering leader has been told "We don't have budget" - whether for tackling technical debt, modernising legacy systems, or adopting AI tooling. The problem is rarely the idea itself. It is how we present it. We frame platform health as an engineering preference when it should be positioned as business risk.

In this interactive workshop, Viktor Didenchuk shares a battle-tested framework from leading cloud platform delivery at JPMorganChase that translates any technical investment - from incident tooling to AI adoption - into the three languages executives actually speak: Revenue Risk, Regulatory Exposure, and Operational Resilience. Through three real-world scenarios with live audience polling, attendees will practise reframing technical asks into compelling, quantified business cases that survive quarterly financial reviews.

Walk away with a repeatable playbook you can apply on Monday morning to secure budget for the initiatives your organisation needs - including AI.

All it takes is a module and a CLI tool to build and deploy production peer-to-peer applications. In this workshop:Build a realtime peer-to-peer applicationConnect with others on your own application, completely peer-to-peer.Understand how to deploy, including production practices, principles and toolingThis is for anyone who builds with JavaScript and anyone who deploys applications.

All it takes is a module and a CLI tool to build and deploy production peer-to-peer applications. In this workshop:Build a realtime peer-to-peer applicationConnect with others on your own application, completely peer-to-peer.Understand how to deploy, including production practices, principles and toolingThis is for anyone who builds with JavaScript and anyone who deploys applications.

AI agents are becoming part of the software development process, but most teams treat them like isolated tools rather than participants in a structured workflow. Without coordination, agent-driven development quickly turns into chaos: duplicated work, endless retries, and unpredictable delivery.

I will introduce a practical approach to organizing AI agents using principles from lean manufacturing and Kanban flow systems. By applying concepts such as pull-based work, WIP limits, and bottleneck management, engineering teams can orchestrate multiple AI agents—system analyst, developer, and tester—into a predictable software delivery pipeline.

Through a live demonstration using GitHub Projects and modern AI coding tools, I will show how agents autonomously pull tasks, move work across pipeline stages, and escalate to humans only when necessary. The result is a development workflow that reduces coordination overhead while dramatically improving throughput and visibility.

LLMs are powerful but unpredictable and most tutorials skip the part where you actually have to deal with that in production code. This workshop teaches you to use generative AI as an engineering tool: how to structure prompts around schemas, validate and repair non-deterministic output, and run models locally so you control the cost and the carbon.

We'll generate a world for a roleplaying game as our teaching context it's a fun, low-stakes domain that makes the hard problems visible. The patterns you leave with apply anywhere you want to bring LLMs into a typed system.

No ML background required. Just TypeScript and a laptop.

LLMs are powerful but unpredictable and most tutorials skip the part where you actually have to deal with that in production code. This workshop teaches you to use generative AI as an engineering tool: how to structure prompts around schemas, validate and repair non-deterministic output, and run models locally so you control the cost and the carbon.

We'll generate a world for a roleplaying game as our teaching context it's a fun, low-stakes domain that makes the hard problems visible. The patterns you leave with apply anywhere you want to bring LLMs into a typed system.

No ML background required. Just TypeScript and a laptop.

In this workshop, we will explore React's latest features: Server Components, Server Actions, and Forms. Gain insights into optimizing server-side rendering, enhancing application interactivity through Server Actions and React 19 hooks, and mastering form creation for robust data handling and validation.

Designed for developers of all levels, this workshop provides practical skills to build scalable, performant web applications.

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

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

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

In this workshop, we will explore React's latest features: Server Components, Server Actions, and Forms. Gain insights into optimizing server-side rendering, enhancing application interactivity through Server Actions and React 19 hooks, and mastering form creation for robust data handling and validation.

Designed for developers of all levels, this workshop provides practical skills to build scalable, performant web applications.

Many front-end developers build modern applications with confidence, but struggle when it comes to shipping those applications to production. Docker, CI/CD, environment configuration, performance tuning, and deployment often remain “someone else’s job” — until something breaks and becomes your responsibility. This workshop closes that gap.
It’s a practical, production-focused workshop designed for front-end developers who want to take full ownership of their front-end applications — from local development to stable, production-ready deployments.

You’ll learn how to:
Dockerize front-end applications for both development and productionCreate optimized, production-ready Docker images for modern front-end frameworksOptimize front-end applications for performance, stability, and reliabilityAvoid common production mistakes seen in real-world front-end projectsDocker for Front-end Developers is a practical book designed specifically for front-end engineers who want to truly understand and confidently use Docker in real-world projects — written by Docker Captain and author of the official Docker React.js sample documentation Kristiyan Velkov — with 30% off using code DOCKER30.

Many front-end developers build modern applications with confidence, but struggle when it comes to shipping those applications to production. Docker, CI/CD, environment configuration, performance tuning, and deployment often remain “someone else’s job” — until something breaks and becomes your responsibility. This workshop closes that gap.
It’s a practical, production-focused workshop designed for front-end developers who want to take full ownership of their front-end applications — from local development to stable, production-ready deployments.

You’ll learn how to:
Dockerize front-end applications for both development and productionCreate optimized, production-ready Docker images for modern front-end frameworksOptimize front-end applications for performance, stability, and reliabilityAvoid common production mistakes seen in real-world front-end projectsDocker for Front-end Developers is a practical book designed specifically for front-end engineers who want to truly understand and confidently use Docker in real-world projects — written by Docker Captain and author of the official Docker React.js sample documentation Kristiyan Velkov — with 30% off using code DOCKER30.

Many front-end developers build modern applications with confidence, but struggle when it comes to shipping those applications to production. Docker, CI/CD, environment configuration, performance tuning, and deployment often remain “someone else’s job” — until something breaks and becomes your responsibility. This workshop closes that gap.
It’s a practical, production-focused workshop designed for front-end developers who want to take full ownership of their front-end applications — from local development to stable, production-ready deployments.

You’ll learn how to:
Dockerize front-end applications for both development and productionCreate optimized, production-ready Docker images for modern front-end frameworksOptimize front-end applications for performance, stability, and reliabilityAvoid common production mistakes seen in real-world front-end projectsDocker for Front-end Developers is a practical book designed specifically for front-end engineers who want to truly understand and confidently use Docker in real-world projects — written by Docker Captain and author of the official Docker React.js sample documentation Kristiyan Velkov — with 30% off using code DOCKER30.

In this workshop, we'll go beyond the fundamentals and explore some of the more powerful features React Query has to offer. You'll gain a deeper understanding of how it works under the hood and learn how to write scalable, maintainable React Query code.Together, we'll build a simple example app and incrementally enhance it with one core objective in mind: delivering the best possible user experience. That means snappy interactions, minimal layout shifts, and avoiding unnecessary loading spinners wherever we can.To achieve this, we'll dive into advanced techniques like various forms of prefetching (including integration with route loaders), seeding the query cache, crafting smooth paginated experiences, and even persisting query state through full page reloads using persistence plugins.Note: You should have prior knowledge about React Query if you attend this workshop.

In this workshop, we'll go beyond the fundamentals and explore some of the more powerful features React Query has to offer. You'll gain a deeper understanding of how it works under the hood and learn how to write scalable, maintainable React Query code.Together, we'll build a simple example app and incrementally enhance it with one core objective in mind: delivering the best possible user experience. That means snappy interactions, minimal layout shifts, and avoiding unnecessary loading spinners wherever we can.To achieve this, we'll dive into advanced techniques like various forms of prefetching (including integration with route loaders), seeding the query cache, crafting smooth paginated experiences, and even persisting query state through full page reloads using persistence plugins.Note: You should have prior knowledge about React Query if you attend this workshop.

In this workshop, we'll go beyond the fundamentals and explore some of the more powerful features React Query has to offer. You'll gain a deeper understanding of how it works under the hood and learn how to write scalable, maintainable React Query code.Together, we'll build a simple example app and incrementally enhance it with one core objective in mind: delivering the best possible user experience. That means snappy interactions, minimal layout shifts, and avoiding unnecessary loading spinners wherever we can.To achieve this, we'll dive into advanced techniques like various forms of prefetching (including integration with route loaders), seeding the query cache, crafting smooth paginated experiences, and even persisting query state through full page reloads using persistence plugins.Note: You should have prior knowledge about React Query if you attend this workshop.

!!! IMPORTANT - the number of seats are limited, so make sure to register for the workshop before June 11. In this hands-on workshop, attendees split into teams - Project Management, Development, QA, and Marketing - to build an AI-enabled development for a fictional airline, AgenticAIrways.

Each team builds an AI agent for its role in the delivery workflow. The PM team defines requirements and passes them to the Dev team, who build features with tool use and domain-specific logic. The Dev team hands off to PM and QA for review and stress-testing with adversarial scenarios and edge cases. Once the system passes muster, it's handed to Marketing to define how the product is communicated to end users.

Agents communicate and coordinate using MCP and Agent-to-Agent (A2A) protocols, mirroring how agentic software will increasingly be embedded in real development workflows.

Whether the airline launches successfully or descends into chaos, every team will walk away with practical experience designing, constraining, and orchestrating multi-agent systems — and a clear understanding of how AI agents can augment each role on a dev team.

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.

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.

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.

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