AI Coding Summit 2026

Cole will walk through his agentic coding workflow that allows him to delegate all coding to the agent while still keeping him in the driver's seat (no vibe coding!). The key is to create a structured approach for both the up front planning and the validation - and he'll demonstrate what that looks like. Plus, he'll show the true power of creating a system for AI coding - with a proper system, every coding mistake the LLM makes is an opportunity to not just address the bug manually, but fix the system so the bug doesn't happen again. That's what makes your coding agent more and more reliable over time!

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 a focused 2-hour session covering MCP's purpose, architecture, hands-on server implementation, and future directions. Designed for developers and system architects aiming to integrate contextual data with ML models effectively, using MCP's latest 2026 features like semantic routing and federated context.Agenda:- Introduction & Why MCP? Key challenges MCP solves and core benefits, including 20x throughput from federated layers.- Architecture Deep Dive: Components, interactions, scalability principles, plus quantum-resilient encryption in MCP 2.1.- Building Your Own MCP Server: Guided walkthrough with code snippets and best practices; live demo of multimodal data fusion.- Future of MCP Developments: Potential enhancements like agentic workflows, emerging trends, and real-world scenarios.Key Takeaways:- Clear understanding of MCP's rationale.- Insight into design patterns and scaling considerations.- Practical steps to implement a prototype server.- Awareness of upcoming trends and how to apply MCP in projects.

Join this interactive 2-hour workshop to learn how to create your own AI co-worker using the Codex SDK, inspired by Rudrank’s work on a GitHub/Slack bot he created to automate code reviews, bug fixes, and feature additions to overcome the opposite timezone differences at work.

We will explore how to shift from manual "vibe coding" to take a managerial role, where AI handles the heavy lifting while you focus on high-level planning and validation. We will cover iterative prompting, and integration with GitHub and Slack for automation with the rest of the team.By the end, you will deploy a functional bot to your repository, ready to assist in your projects and even free up time for personal goals like finishing that games backlog!This session is ideal for developers who are curious and looking to build an "army" of AI helpers.Table of Contents1. Introduction & Why AI Co-Workers?: - Discuss the motivation behind AI-driven development, going from just AI-assisted a year ago. How to take advantage of the ChatGPT plan and use the Codex SDK to program codex directly to create structured agents.- Overview of Codex SDK's strengths in code generation, audits, and multi-step reasoning, with real examples from building my AI coworker.
2. Architecture:- Explore components like command parsing, session persistence (24-hour TTL), and GitHub integration via Octokit. - Cover agentic patterns: planning with high-precision models (e.g., Codex xHigh), implementation with efficient ones Codex Medium, and error handling for long-running tasks.
3. Building The Bot: - Set up the environment (clone example repo, configure .env with API keys), create a GitHub App, implement core features (e.g., @bot to fix the bugs)- Test with sample commands. Include live demos of code audits, branch/PR creation, and polling for stop commands. Participants will fork and customize their own bot. 
4. Future Directions: - Parallel sub-tasks, MCPs, skills- Q&A

In this hands-on session, we'll dive straight into the practical mechanics of working with Google DeepMind’s latest research and models. We'll explore Google AI Studio, Antigravity, and the Gemini APIs, focusing on how to seamlessly integrate these capabilities into your own projects. If you're looking for the modern developer's equivalent of Python's "import antigravity" (pun intended!), this is it.We'll prioritize a hands-on, hacker-friendly approach so you can walk away with a solid intuition for how these tools work under the hood. You'll leave fully empowered to use these models in your own architecture, with the exact steps needed to get up and running with Gemini for code generation, analysis, and everyday tinkering as quickly as possible.

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.” 

This workshop introduces Sandboxes as a general computing primitive and uses Cloudflare Sandboxes as the concrete implementation. Participants explore common, real-world use cases for sandboxed execution and learn how to reason about when and why sandboxes are the right tool.

The workshop is intentionally product-adjacent rather than product-specific. Cloudflare Sandboxes are used throughout, but the concepts apply broadly to modern secure execution environments.


Workshop Goals

By the end of this workshop, participants will be able to:

- Explain what a sandbox is and why it exists as a compute primitive
- Identify common problems that sandboxes are well suited to solve
- Safely execute untrusted or dynamic code
- Use sandboxes for development, testing, and automation workflows
- Reason about security, performance, and tradeoffs when choosing sandboxed execution

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.

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.

Ready to bring conversational AI to life? In this hands-on workshop, we'll walk through deploying a real-time voice agent using Render's cloud platform. You'll work directly with LiveKit's voice agent template to create an interactive AI assistant that can listen, think, and respond naturally through voice.You'll discover:How to set up and configure a LiveKit voice agent using Render's deployment templateThe architecture behind real-time voice AI systems and how different components work togetherBest practices for managing environment variables, API keys, and cloud service integrationsTechniques for testing and debugging your deployed voice agent in productionStrategies for monitoring performance and scaling your AI application as usage growsBy the end of this workshop, you'll have a fully functional voice agent running in the cloud, ready to handle real conversations. You'll gain practical experience with modern AI deployment tools and understand the complete pipeline from template to production.This workshop is ideal for developers interested in conversational AI, engineers looking to add voice capabilities to their applications, and anyone curious about deploying real-time AI systems without getting lost in infrastructure complexity.