Name: AI Coding Summit 2026
Start: 2026-02-25T23:00:00.000Z
End: 2026-02-26T23:00:00.000Z

Varun

Rajni Gediya

Agnieszka Labonarska

Violina Popova

Sander Vervaeke

Nick Taylor

Alfonso Graziano 

Joao Gaspar Silva

David Pugh

Thijs Broerse

Oscar Pagani

Ricardo Machado

Cvetanka Nechevska

Mangirdas Darguzis

Jim Hoffman

Svetlana Smirnova

Ivan Almasi

João Cidade Silva

Alex Perez

Hampus Rönström

Martin Jensen

Georges Chami

Filip Verswijver

Pagal3

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Francisco Herrera

Keertivaas S

avan

Foteini

Andrew Harasymiw

Sanketh Kumar

I've never had so much fun developing software than I have in the last few months. And I'm going to show you why. In this talk, I'll share my favorite workflows for building a web application using AI with Cursor. By the end of the experience, we'll have a real-world application that we built using AI along with deep full-stack web expertise. And you'll leave with ideas of ways to integrate your web expertise with the power of AI to build excellent user experiences for the web.

How I Build Web Applications in 2026

Kent C. Dodds

This talk will explore the ongoing fundamental transformation of the role of software developers. It will provide guidance on succeeding in this new rapidly evolving environment, where to focus your personal development, and explore the workflows and techniques that actually work, and aren’t hype or sales driven.

Your New Job

Ken Wheeler

AI coding tools work best when they can plan, act, and iterate - not just respond. This talk covers patterns for building autonomous workflows across the developer workflow and how you can get the most out of Claude Code.

Agentic by Default: Rethinking Developer Workflows with Claude Code

Ado Kukic

As you might know, the "Ralph Wiggum" loop has been generating quite a bit of hype online amongst developers. It's a concept that wraps an AI coding agent in persistent iteration loops that keep running until tasks are actually complete. This talk goes into the core concepts and principles, common pitfalls, and practical application of the method.

The Ralph Wiggum Method Explained

Eddy Vinck

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 &amp; 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&amp;A

Building AI Co-Worker with Codex SDK

Rudrank Riyam

As 2026 brings a shift from feature parity to differentiated feature sets, Antigravity offers a distinct approach to building software. This session dives into the platform's "get stuff done" mindset and unique ecosystem capabilities, including the Chrome Agent, and versatile model choices. Learn how to go from PRD to a production-grade solution, using the browser agent for research and testing, managing multi-agent tasks, and using simple tools to customize your environment. Ultimately, this empowers you to turn ideas into prototypes with speed and precision.

Beyond Vibe Coding: Differentiated Workflows with Antigravity

Alex Astrum

Rody Davis

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.

Advanced Cursor Techniques for Building Fullstack Apps

Mike Mikula

Code generation scales. Planning and review don't, limiting how fast AI-native teams can ship. This talk traces the evolution of developer tooling to the current explosion of coding agents, and makes the case for why the biggest opportunity now is helping engineers plan and review faster.

Why Software Engineering Is Becoming: Plan and Review

Louis Knight-Webb

Coding agents have crossed the threshold of solving non-trivial problems alongside engineers, as well as running autonomously in many ways. Their most surprising ability has been in their effectiveness at non-coding tasks too the moment universe is represented on a filesystem or accessible from unix-like environments. Agents will soon be writing, accessing and powering software — and this talk covers the fun challenges of designing sandboxed dev environments across these paradigms, enabling agents to run entirely within sandboxes or using sandboxes as programmable, ephemeral tools.

Designing Remote Dev Environments for Coding Agents

Naresh Ramesh

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!

Advanced Claude Code Techniques for 2026

Cole Medin

LLMs need access to the relevant context to succeed. One key element of Context Engineering that we can use to help agents remember is Memory.Let's dive into the world of memory management for agents. We'll discuss the difference between short and long term memory, how to manage memory, and the importance of preventing context poisoning and clash using techniques such as trimming and summarization.

How to Help Agents Remember

Carly Richmond

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:<ul><li>- Introduction &amp; Why MCP? Key challenges MCP solves and core benefits, including 20x throughput from federated layers.</li><li>- Architecture Deep Dive: Components, interactions, scalability principles, plus quantum-resilient encryption in MCP 2.1.</li><li>- Building Your Own MCP Server: Guided walkthrough with code snippets and best practices; live demo of multimodal data fusion.</li><li>- Future of MCP Developments: Potential enhancements like agentic workflows, emerging trends, and real-world scenarios.</li></ul>Key Takeaways:<ul><li>- Clear understanding of MCP's rationale.</li><li>- Insight into design patterns and scaling considerations.</li><li>- Practical steps to implement a prototype server.</li><li>- Awareness of upcoming trends and how to apply MCP in projects.</li></ul>

Model Context Protocol (MCP) Deep Dive: 2-Hour Interactive Workshop

Stepan Suvorov

A practical crash course for developers who want to skip weeks of research and get straight to building. We'll cover the essential terminology, current industry trends, and the real landscape of services and libraries. You'll learn when to use what - and why some choices matter more than others. I'll share lessons learned from building a production voice agent for 10,000+ Dutch truck drivers, including the problems nobody talks about. By the end, you'll have everything you need to build your first working Voice Agent.

Voice Agents Crash Course

Developers now create entire applications by simply chatting with AI tools. More and more startups rely on predominantly AI-generated code. But what initially thrilled developers quickly leads to frustration: generic code, poor architecture, and security vulnerabilities.In this talk, I demonstrate how Spec Driven Development (SDD) solves these problems. Instead of coding first and documenting later, SDD begins with precise specifications as the "single source of truth." Various tools and CLIs make this methodology practically applicable today with a structured 4-phase approach: Specify, Plan, Tasks, Implement.Using a real project, I show how SDD works with modern AI coding tools. I share honest insights from hands-on experience: where SDD excels, where it fails, and whether the specification overhead actually leads to better results.

Spec Driven Development: The End of Vibe Coding

Daniel Sogl

AI speeds up development — but it also introduces silent vulnerabilities. This talk exposes the hidden security risks of unreviewed AI-generated code and shows practical ways to protect your applications without slowing down your workflow.Table of Content: - How LLMs Generate Code &amp; Why It Can Be Dangerous- Real-World Examples of AI-Introduced Vulnerabilities- A Practical, Secure Workflow for AI-Generated Code- Q&amp;A

Security Pitfalls in AI-Generated Code: What Happens When Developers Skip Review

Kristiyan Velkov

AI code reviews often feel like tossing a coin. Heads - you get endless nitpicks about style. Tails - it finds real issues. And even when the signal is good, are we really getting quality and productivity benefits?The secret isn't the AI, it's how we're deploying it. This talk explores what actually drives value in AI-assisted code reviews: rich contextual integration, scalable specialization, and measurable impact on developer productivity. Learn to transform AI reviews from a compliance checkbox into a strategic advantage for your engineering organization.

Your AI Code Reviews Are Missing the Point (And How to Fix It)

Yishai Beeri

This presentation shows how Domain-Driven Design (DDD) turns agentic coding from “prompt spaghetti” into a maintainable engineering approach. Instead of one giant prompt and ad-hoc tools, it proposes bounded contexts for agent responsibilities, a ubiquitous language for consistent tool inputs/outputs, and a context map that makes integrations explicit. Attendees will learn how to design stable tool contracts (ports/adapters), add CI/evaluation gates that keep agents honest, and implement traceability and replay so agent actions remain auditable and debuggable. The result: agentic systems that scale to real codebases and teams without collapsing into chaos.

From Prompt Spaghetti to Bounded Contexts: DDD for Agentic Codebases

Nikita Golovko

As codebases grow, AI coding assistants struggle. Context windows overflow, agents lose track of dependencies, and simple text search fails to capture the semantic relationships that define real software. This talk explores three proven architectural patterns that enable AI agents to work effectively with production-scale codebases: semantic code intelligence through Language Server Protocol integration, specialized agent skills via context and tool bundling, and subagent delegation for efficient context management. Through live demonstrations on a popular open-source project like ShadCN, you'll see these techniques tackle the complexity of real-world software—multi-file refactorings,cross-module changes, and dependency tracking that would overwhelm traditional approaches.You'll leave with practical, product-agnostic strategies for building or enhancing AI agents that can handle large codebases with accuracy and efficiency. We'll examine why semantic understanding outperforms text search, how to design focused agent skills that improve task completion, and how parallel subagent architectures prevent context window exhaustion. Whether you're building AI developer tools, architecting multi-agent systems, or contributing to open source, these patterns will help you bridge the gap between toy demos and production-grade AI assistance.

Scaling AI Agents for Production Codebases: Patterns for Accuracy and Efficiency

Saurabh Dahal

Learn an architectural pattern to automate "glue work" and build self-executing workflows. This talk will demonstrate how to use "collaboration planes" with lightweight, locally-hosted AI agents as an invisible automation layer to eliminate manual tasks like exporting data and bridging tools. Through an example of a self-hosted AI stock analyst and insights from my previous projects for CI/CD automation, you'll learn how to shift from being the executor of repetitive tasks to the architect of autonomous systems.

Build Your Team's Automation Layer: The AI Glue Agent Pattern

Shubh

This talk challenges the overreliance on success rate and introduces a more practical and safety-aware framework for evaluating AI agents. Drawing from real deployment scenarios, we explore metrics that better capture agent usefulness and reliability, including time-to-completion, tool-call efficiency, error recovery rate, and cost per successful task.The session will show how teams unintentionally optimize the wrong metrics, leading to hidden failure modes such as excessive retries, inefficient tool usage, silent errors, and escalating operational costs. We will connect these issues to broader AI safety concerns, highlighting how poor evaluation practices can create misleading confidence in agent behavior.Attendees will leave with concrete evaluation strategies, a clearer understanding of trade-offs in agent design, and practical guidance on building metrics that reflect real-world performance not just benchmark wins.

Metrics That Actually Matter: Evaluating AI Agents Beyond Success Rate

John Robert

Don’t get left behind—discover how AI is revolutionizing software engineering at the AI Coding Summit. Dive into cutting-edge talks and hands-on workshops on AI-powered software development. Network with industry leaders, explore game-changing tools, and stay ahead in the fast-evolving world of AI-driven development. Do not miss the biggest shift in software engineering.

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