#artificial intelligence

A comprehensive workshop designed specifically for React developers ready to become AI engineers. Learn how your existing React skills—component thinking, state management, effect handling, and performance optimization—directly translate to building sophisticated AI applications. We'll cover the full stack: AI API integration, streaming responses, error handling, state persistence with Supabase, and deployment with Vercel.Skills Translation:- Component lifecycle → AI conversation lifecycle- State management → AI context and memory management- Effect handling → AI response streaming and side effects- Performance optimization → AI caching and request optimization- Testing patterns → AI interaction testing strategiesWhat you'll build: A complete AI-powered project management tool showcasing enterprise-level AI integration patterns.

LLMs are not just fancy search engines: they lay the ground for building autonomous and intelligent pieces of software, aka agents.
Companies are investing massively in generative AI infrastructures. To get their money's worth, they need developers that can make the best out of an LLM, and that could be you.
Discover the TypeScript stack for LLM-based development in this 3 hours workshop. Connect to your favorite model with the Vercel AI SDK and turn lines of code into AI agents with Mastra.ai.

The way we write code is fundamentally changing. Instead of getting stuck in nested loops and implementation details, imagine focusing purely on architecture and creative problem-solving while your AI pair programmer handles the execution. In this hands-on workshop, I'll show you how to leverage Cline (an autonomous coding agent that recently hit 1M VS Code downloads) to dramatically accelerate your development workflow through a practice we call "vibe coding" - where humans focus on high-level thinking and AI handles the implementation.You'll discover:The fundamental principles of "vibe coding" and how it differs from traditional developmentHow to architect solutions at a high level and have AI implement them accuratelyLive demo: Building a production-grade caching system in Go that saved us $500/weekTechniques for using AI to understand complex codebases in minutes instead of hoursBest practices for prompting AI agents to get exactly the code you wantCommon pitfalls to avoid when working with AI coding assistantsStrategies for using AI to accelerate learning and reduce dependency on senior engineersHow to effectively combine human creativity with AI implementation capabilitiesWhether you're a junior developer looking to accelerate your learning or a senior engineer wanting to optimize your workflow, you'll leave this workshop with practical experience in AI-assisted development that you can immediately apply to your projects. Through live coding demos and hands-on exercises, you'll learn how to leverage Cline to write better code faster while focusing on what matters - solving real problems.

In this workshop I’ll cover a repeatable process on how to spin up full stack apps in Cursor.  Expect to understand techniques such as using GPT to create product requirements, database schemas, roadmaps and using those in notes to generate checklists to guide app development.  We will dive further in on how to fix hallucinations/ errors that occur, useful prompts to make your app look and feel modern, approaches to get every layer wired up and more!  By the end expect to be able to run your own AI generated full stack app on your machine!
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Knowledge of AI tooling is critical for future-proofing the careers of React developers, and the Vercel suite of AI tools is an approachable on-ramp. In this course, we’ll take a closer look at the Vercel AI SDK and how this can help React developers build streaming interfaces with JavaScript and Next.js. We’ll also incorporate additional 3rd party APIs to build and deploy a music visualization app.
Topics:- Creating a React Project with Next.js- Choosing a LLM- Customizing Streaming Interfaces- Building Routes- Creating and Generating Components - Using Hooks (useChat, useCompletion, useActions, etc)

In this workshop, we discuss the merits of serverless architecture and how it can be applied to the AI space. We'll explore options around building serverless RAG applications for a more lambda-esque approach to AI. Next, we'll get hands on and build a sample CRUD app that allows you to store information and query it using an LLM with Workers AI, Vectorize, D1, and Cloudflare Workers.

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.

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.

Over the past year, model intelligence has skyrocketed, and agent tooling has improved significantly. However, there's still a gap: most AI agents don't have the procedural, domain-specific expertise needed to do real work inside real products, especially in React apps, where a small behaviour change turns into user-facing regressions.

In this workshop, I'll show how leveraging Agent Skills changes how you build AI features in React: from prompt tweaking to shipping packaged guides, resources and scripts that turn a general-purpose agent into a product-specific one via the Agent Skills protocol.  I'll be showing a practical way to do this in React apps without changing your current frameworks or LLM, with evals and versioning in place to prevent regressions.

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.

This workshop explores how standalone agents operate at the runtime level and how they differ from traditional AI pipelines. We’ll examine agent architecture, planning loops, memory models, and tool execution. We’ll also cover multi-agent coordination, including state isolation and resource control. A key focus is security and governance — capability-based access, sandboxing, and injection risks. Finally, we’ll address observability and supervision: tracing reasoning, auditing tool usage, and implementing control mechanisms for production systems.

All examples and concepts will be grounded in the Node.js stack and we will explore why Node.js is particularly well-suited for building production-ready agent runtimes — serving as the control plane for supervision, integration, streaming execution, and distributed coordination.

References: OpenClaw, n8n, LangChain.

Ready to go from zero to deployed voice agent?Voice AI is moving fast, but most tutorials end at "hello world" and leave you wondering how to actually ship something to production. This intensive workshop takes you through the entire journey: building a complete voice agent with TypeScript and React, deploying it to production, setting up observability, and creating evaluation frameworks—all the pieces you need for real-world JavaScript applications.What you'll build:A full-stack voice agent using LiveKit's TypeScript SDKReact UI with real-time audio visualization and state managementProduction deployment with proper observabilityAutomated testing and evaluation frameworksTopics covered:Setting up WebRTC audio streams with TypeScriptIntegrating STT, LLM, and TTS models in your JavaScript stackBuilding React UI for voice interactions with real-time stateHandling interruptions and natural turn-takingDeploying to production (Vercel)Setting up observability and session replayBuilding evaluation frameworks for quality assuranceThis isn't a demo – it's a bootcamp. You'll write TypeScript code, debug issues, and deploy to production. By the end, you'll have a working voice agent and the confidence to build more.

A new ecosystem of apps is emerging inside generalist conversational agents like ChatGPT and Claude. These apps serve as a direct digital storefront for brands. They serve as a compelling alternative to traditional mobile and web applications for the billion users who now begin their online journey in a chat.

AI apps are built on standards like Apps SDK, MCP Apps, and MCP UI. They all rely on MCP as the underlying protocol to expose context and tools, while adding a human-facing presentation layer in the form of UI widgets.

Building these React apps introduces a genuinely new set of challenges: you're no longer designing for a user interacting with a UI alone, but for a three-way dynamic between a human, a UI, and a model. Every interaction must account for whether it originates from a click on a widget or a message typed in the chat.

Skybridge is the full-stack TypeScript framework built on React that we created at Alpic to help developers around the world ship these new apps at the speed of light.

Come and learn how to build the next generation of AI apps with me!

In this hands on workshop participants will learn how to create a production‑ready Model Context Protocol (MCP) server on Cloudflare Workers. The session covers defining tool endpoints, integrating external APIs, persisting state with KV storage, and globally deploying the server so AI assistants can invoke custom tools in real time. By the end of the workshop every attendee will have a live MCP server they can extend and secure for their own AI‑agent projects.  

Join us for an interactive workshop where you'll train your first neural network using the classic MNIST handwritten digit dataset. In this hands-on session, participants will use OpenShift AI 3.x Workbenches to build and train a PyTorch model that recognizes handwritten digits from images.This beginner-friendly workshop provides a practical introduction to machine learning workflows in an enterprise-grade AI platform. Working directly in Jupyter notebooks, attendees will clone a ready-to-use GitHub repository, explore the MNIST dataset, and walk through the complete model training pipeline—from data preprocessing to model evaluation. The session uses CPU-optimized configurations, making it accessible without requiring specialized GPU resources.Whether you're new to AI/ML or curious about how the models you use actually function, this workshop offers practical, immediately applicable experience.Takeaways: Participants will leave with a working neural network, a complete training notebook they can reference, and foundational knowledge of deploying and utilizing ML workloads on OpenShift AI.

Modern AI coding agents don’t just autocomplete. They execute, connect, and automate. With the wrong settings, they can access more of your system than you intended. In this session, I’ll demonstrate how agent autonomy, MCP integrations, and Skills can lead to unexpected security risks. Through examples using Cursor.ai, we’ll see how seemingly harmless configuration choices stack into real exposure and how to set secure defaults for safe, high-velocity “Vibe Coding.” 

In this interactive workshop, you’ll build a working internal browser extension you and your team can use to share context across apps you use daily. We’ll start with a practical, real-world example: a web clipper that you’ll customize to send content from GitHub to your team’s communication tool (Slack, Discord, or Teams). You’ll learn how to generate extensions with natural language, modify forms and logic, connect to apps like Slack and GitHub, and deploy what you build to your team. By the end, you’ll have a functioning automation running in your browser and the skills to create more.
This workshop is ideal for developers and tech leads who want to quickly build and deploy secure, internal extensions without writing full browser extensions and handling infrastructure.

In this workshop, you’ll learn a repeatable process for building full stack apps in Cursor. We’ll start with an idea and use an agent to shape clear requirements. Next, we’ll generate a database schema and set up the build tools. Then we’ll create the backend and frontend, and wire the layers together. Along the way, you’ll learn how to utilize context loops, catch hallucinations, fix errors, and prompt for modern UI. By the end, you’ll be able to build a working AI generated full stack application on your own.

Join us to learn how to build your own Zapier-like interface in days, not months. Discover how to save 1,000+ hours of design and coding upfront, building a complex workflow editor UI from scratch. Customize it for AI orchestration, automations, business processes visualization and other real-world workflow needs. Workflow Builder SDK is designed to quickly deploy node flow interfaces that let you tailor properties, layouts, and interactions to suit unique business workflows. Connect it with any backend and reduce the dependency on N8N, Make or Zapier for your own automation needs.Agenda and key takeaways:Explore diagramming tools: Understand and compare libraries for building diagram UIs in React. We analyze the strengths, trade-offs, and best use cases (Web/Canvas/SVG/DOM).Hands-on: Workflow Builder SDK: Build a real-world AI workflow editor interface using our SDK and the React Flow foundation.Plug & play architecture: Implement the core features of a modern editor:Custom nodes and edgesState management and drag-and-drop supportAuto-layout, minimap, and zoom controlsProperty-editing sidebarPolishing user experience (UX): Learn how to enhance usability and visual appeal. We'll use Overflow - Open Source UI components to streamline development and keep the focus purely on business logic.

Have you ever wanted to build a generative UI application like V0 where you simply prompt it and it creates an entire interface for you? Well lets build that with the power of Server Driven UIs and AI. Well walk through just how easy Server Driven UI makes the creation a generative UI application. All the while we'll be leveraging React 19 APIs to make this generative UI application as performant as possible!

In this workshop, we will incrementally augment an existing React application with AI. I'm sure most of you probably already maintain a React app, so rather than starting from the ground-up, let's take something that already works, and develop some patterns for adding practical, real-world AI features without making them feel "bolted on" just for the sake of chasing the hype. We'll explore questions such as "which model should I use?", "should the LLM run on-device or on the cloud?", "am I risking vendor lock-in by picking a proprietary model over an open-source one?", and we'll dive into the deeper topics of using AI frameworks, creating agents, and the clients and servers of the Model Context Protocol (MCP). By the end of this workshop, you should have some applicable knowledge and skills which you can bring to your own applications, and hopefully, you'll walk away with a greater understanding of how and when to actually use AI 

This workshop introduces how to use open-source LLMs on-premise for enhanced data control, privacy, and cost efficiency. It highlights Ollama for simplified LLM management (downloading, running models offline) and FastAPI for efficient local hosting, enabling fast API development and seamless integration. This combined approach offers superior security, regulatory compliance, and customization compared to cloud-based solutions.

In this workshop, I’ll guide participants through a process for building full-stack applications using Cursor. We’ll cover setting up Cursor workflows, including rules, commands, and custom modes, to streamline development. We will use Cursor to draft PRDs, database schema, etc and a simple SOW. We’ll turn notes into actionable checklists to guide the build, connecting the front end, API, and database.Additionally, we’ll create workflows for debugging, testing, reviewing, and fixing the app while reducing hallucinations with precise prompts. The session will also include a security review workflow and techniques for applying prompts to achieve a modern UI design. Attendees will leave with a functioning app on their machine and a reusable development process.

Workshop Objective:Show how teams can go from AI-generated UI to live production components in under an hour.Workshop Agenda:Part 1: Kickoff & Setup - Quick intro to the Pluggable AI method- Walk through a real example already running in production- Align on the selected AI/visual tool (Claude, Gemini, etc.)- Choose 1–2 UI components to work with- Define what’s needed to make the component pluggable- Select the host app Part 2: Generate the Component - Use the selected AI tool to generate the UI component- Myop team helps ensure it fits integration guidelines (structure, format)Part 3: Integration Preps- Identifying where the component connects (logic hook)-Review generated component and prepare for connectionPart 4: Plug It Live - Plug the component into the live app using Myop's open source- Optional: test versioning, A/B testing, segmentation, gradual rolloutPart 5: Wrap-Up- Recap effort vs. outcome- Q&A