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Amp Code: Next Generation AI Coding – Beyang Liu
Introduction to Amp Code and its approach to AI-powered software development. Speaker: Beyang Liu | Co-founder & CTO, Amp Code / Sourcegraph https://x.com/beyang https://www.linkedin.com/in/beya...
Making Codebases Agent Ready – Eno Reyes, Factory AI
Agents are eating software engineering. Yet teams deploying these tools face mixed results. Agents work great in demos but fail unreliably in production, frustrating engineering teams who expected ...
Autonomy Is All You Need – Michele Catasta, Replit
AI agents exhibit vastly different degrees of autonomy. Yet, the ability to accomplish objectives without supervision is the critical north star for agent progress, especially in software creation....
The War on Slop – swyx
Why we need to eliminate low-quality code and work in AI engineering. Speaker: swyx | Organizer, AI Engineer https://x.com/swyx https://www.linkedin.com/in/shawnswyxwang/ https://www.swyx.io/
The Infinite Software Crisis – Jake Nations, Netflix
In 1968, the term ""Software Crisis"" emerged when systems grew beyond what developers could manage. Every generation since has ""solved"" it with more powerful tools, only to create even bigger pr...
From Arc to Dia: Lessons learned building AI Browsers – Samir Mody, The Browser Company of New York
What happens when you take a polished, beloved browser and rebuild it from the ground up around AI? In 2024, The Browser Company did exactly that: transforming Arc, a human-designed browser, into D...
Leadership in AI Assisted Engineering – Justin Reock, DX (acq. Atlassian)
To realize meaningful returns on AI investments, leadership must take accountability and ownership of establishing best practices, enabling engineers, measuring impact, and ensuring proper guardrai...
Paying Engineers like Salespeople – Arman Hezarkhani, Tenex
Most software teams still run on an outdated unit of measure: hours, days, years. That single choice misaligns every incentive—clients want fewer, engineers want more, and everyone loses speed. A...
AI Leadership - Alex Lieberman, Tenex
more at https://ai.engineer
Dispatch from the Future: building an AI-native Company – Dan Shipper, Every, AI & I
The central thesis is that there is a "10x difference" between an organization where 90% of engineers use AI versus one where 100% do. At 100% adoption, the fundamental physics of software engineer...
AI Consulting in Practice – NLW, Super ai
Insights from consulting on AI implementation across various organizations. Speaker: NLW | Host, AI Daily Brief & CEO, Super.ai https://x.com/nlw https://www.youtube.com/@AIDailyBrief
AI Kernel Generation: What's working, what's not, what's next – Natalie Serrino, Gimlet Labs
In this talk, we'll talk about how AI generated kernels can meaningfully speed up custom PyTorch code, without any human effort. Lots of great frameworks exist to optimize PyTorch with programmati...
Code World Model: Building World Models for Computation – Jacob Kahn, FAIR Meta
Today, most neural models for code learn from code itself: sequences of tokens that capture syntax rather than computation. While this allows models to learn the shape of code, true reasoning about...
Your Support Team Should Ship Code – Lisa Orr, Zapier
Zapier maintains 8000+ integrations that break as APIs change. We had thousands of backlog support tickets with dozens more arriving weekly. To keep up with the traffic, we started building AI tool...
What We Learned Deploying AI within Bloomberg’s Engineering Organization – Lei Zhang, Bloomberg
When it comes to using AI for software engineering, much of the spotlight falls on how large language models (LLMs) can write code—sometimes entirely from scratch. Countless studies highlight produ...
Building in the Gemini Era – Kat Kampf & Ammaar Reshi, Google DeepMind
A deep dive into the latest capabilities of Google DeepMind's Gemini 3 and the newly released "Nano Banana Pro" image model within Google AI Studio. Kat and Ammaar demonstrate "vibe coding"—a new p...
Coding Evals: From Code Snippets to Codebases – Naman Jain, Cursor
AI coding capabilities have leapt from generating one-line snippets to competing entire codebases with agentic workflows. I’ll trace that arc focusing on learnings and challenges through each stage...
From Vibe Coding To Vibe Engineering – Kitze, Sizzy
Web development has always moved in cycles of hype, from frameworks to tooling. With the rise of large language models, we're entering a new era of "vibe coding," where developers shape software th...
Minimax M2: Building the #1 Open Model – Olive Song, MiniMax
Introducing Minimax's latest AI model and its applications in code generation. Speaker: Olive Song | Senior Researcher, MiniMax https://x.com/olive_jy_song
Proactive Agents – Kath Korevec, Google Labs
Speaker: Kath Korevec | Director of Product, Google Labs https://x.com/simpsoka https://www.linkedin.com/in/kathleensimpson/
Moving away from Agile: What's Next – Martin Harrysson & Natasha Maniar, McKinsey & Company
Most enterprises are not capturing much value from AI in software dev to date (at least relative to the potential). The reason is that most are adding AI tools to their dev teams without changing t...
Hard Won Lessons from Building Effective AI Coding Agents – Nik Pash, Cline
Most of what’s written about AI agents sounds great in theory — until you try to make them work in production. The seductive ideas (multi-agent orchestration, RAG, prompt stacking) often collapse u...
The State of AI Code Quality: Hype vs Reality — Itamar Friedman, Qodo
AI is making code generation nearly effortless, but the critical question remains: can we trust AI-generated code for software that truly matters? Has it really become easier to build robust, high-...
Can you prove AI ROI in Software Eng? (Stanford 120k Devs Study) – Yegor Denisov-Blanch, Stanford
You’re investing millions in AI for software engineering. Can you prove it’s paying off? Benchmarks show models can write code, but in enterprise deployments ROI is hard to measure, easy to bias, ...
Agent Reinforcement Fine Tuning – Will Hang & Cathy Zhou, OpenAI
Deep dive into OpenAI's approach to reinforcement fine-tuning for code models. https://x.com/willhang_ https://x.com/cathyzhou AIE is coming to London and SF! see dates and sign up to be notified...
RL Environments at Scale – Will Brown, Prime Intellect
Scaling reinforcement learning environments for training advanced AI coding models. https://twitter.com/willccbb AIE is coming to London and SF! see dates and sign up to be notified of sponsorshi...
Efficient Reinforcement Learning – Rhythm Garg & Linden Li, Applied Compute
Reinforcement learning (RL) is a powerful mechanism for building agents that are superhuman and specialized in particular tasks. At Applied Compute, RL is one of the fundamental building blocks tha...
Don't Build Agents, Build Skills Instead – Barry Zhang & Mahesh Murag, Anthropic
In the past year, we've seen rapid advancement of model intelligence and convergence on agent scaffolding. But there's still a gap: agents often lack the domain expertise and specialized knowledge ...
2026: The Year The IDE Died — Steve Yegge & Gene Kim, Authors, Vibe Coding
As AI has grown more capable, software developers around the world have lagged behind the technology advances, and have consistently eschewed the most powerful tools. In this talk I explore why dev...
VoiceVision RAG - Integrating Visual Document Intelligence with Voice Response — Suman Debnath, AWS
In this workshop we will explore the integration of Colpali, a cutting-edge Vision based Retrieval Model, with voice synthesis for next-generation RAG systems. We'll demonstrate how Colpali's abili...
Government Agents: AI Agents Meet Tough Regulations — Mark Myshatyn, Los Alamos National Lab
Lightning talk given at the 2025 AI Engineer World's Fair. https://www.linkedin.com/in/markmyshatyn/
Benchmarks vs Economics: The AI capability measurement gap – Joel Becker, METR
AI models are crushing benchmarks. SWE-bench scores are climbing, and METR's measured time horizons are rising rapidly. Yet when we deployed these same models in a field study with experienced deve...
Continual System Prompt Learning for Code Agents – Aparna Dhinakaran, Arize
Your coding agent writes code—but not like your team. RL has boosted base models, but it’s opaque and hard to scale across enterprises. Most agents still rely on brittle, hand-edited system prompts...
Future-Proof Coding Agents – Bill Chen & Brian Fioca, OpenAI
Coding agents are becoming one of the most active areas in applied AI, yet many teams keep rebuilding fragile infrastructure every time models or providers change. We believe there is a better way....
Katelyn Lesse – Evolving Claude APIs for Agents, Anthropic
Developers are building more and more complex, long-running, agentic systems. Learn how the Anthropic team is evolving the Claude Developer Platform to enable developers to get the best outcomes fr...
No Vibes Allowed: Solving Hard Problems in Complex Codebases – Dex Horthy, HumanLayer
It seems pretty well-accepted that AI coding tools struggle with real production codebases. At AI Engineer 2025 in June, The Stanford study on AI's impact on developer productivity found: A lot of...
Defying Gravity - Kevin Hou, Google DeepMind
Why we built Google Antigravity, and discussing the future of agentic IDEs with Gemini 3. Speaker: https://x.com/kevinhou22 AIE is coming to London and SF! see dates and sign up to be notified of...
Building Cursor Composer – Lee Robinson, Cursor
Learn about the infrastructure, training, and evaluations used to build Cursor Composer, our first coding model. (https://cursor.com/blog/2-0) Speaker: https://x.com/leerob AIE is coming to Londo...
Music from AIE Code Summit - Instrumentals
By popular demand, we are releasing our music from the livestream + venue stage -- the instrumental tracks. Comment below if you want to see the vocal tracks released!
The Unbearable Lightness of Agent Optimization — Alberto Romero, Jointly
This talk introduces Meta-ACE, a learned meta-optimization framework that dynamically orchestrates multiple strategies (context evolution, adaptive compute, hierarchical verification, structured me...
Backlog.md: Terminal Kanban Board for Managing Tasks with AI Agents — Alex Gavrilescu, Funstage
Never leave your terminal to create and manage tasks for your AI agents. Backlog.md stores all your tasks as Markdown files in your Git repo. By exposing the main workflows and commands as MCP tool...
Agents are Robots Too: What Self-Driving Taught Me About Building Agents — Jesse Hu, Abundant
In this talk, I break down the surprising parallels between robotics and agents: embodiment, statefulness, simulation, and more. The main lesson from self-driving: everyone thought perception was h...
Vision: Zero Bugs — Johann Schleier-Smith, Temporal
Software with zero bugs sounds absurd, or even impossible, in anything but simple situations, but it has been built. For example, NASA's Space Shuttle software achieved near-perfection (1 error per...
Compilers in the Age of LLMs — Yusuf Olokoba, Muna
Python is where ideas start—but it isn't where portable, low-latency software ends. In this talk, I'll show how we use LLMs inside a constrained, verifiable compiler pipeline to turn plain Python f...
Hacking Subagents Into Codex CLI — Brian John, Betterup
Subagents are amazing tools for managing context, among other things. But Codex CLI doesn't have them. Let's change that! Brian John is a Principal Full Stack Engineer with over a decade of experi...
Enterprise Deep Research: The Next Killer App for Enterprise AI — Ofer Mendelevitch, Vectara
Conversational AI has already proven itself as the first high-ROI enterprise AI application. But the real frontier lies beyond chat with high-value, document-centric workflows that still consume co...
What Data from 20m Pull Requests Reveal About AI Transformation — Nick Arcolano, Jellyfish
Engineering teams are spending millions on AI coding tools, but most have no idea what's actually working. Without hard data, you're flying blind – unable to tell which teams are actually using AI ...
Infra that fixes itself, thanks to coding agents — Mahmoud Abdelwahab, Railway
This talk shows how we built Railway Autofix, a plug-in template you can drop into any Railway project to monitor your infrastructure, and open PRs with fixes when issues are detected. We use OpenC...
Context Engineering: Connecting the Dots with Graphs — Stephen Chin, Neo4j
AI systems need more than intelligence; they need context. Without it, even the most advanced models can misinterpret information, lose track of details, or arrive at conclusions that don’t hold up...
Context Platform Engineering to Reduce Token Anxiety — Val Bercovici, WEKA
Context Platform Engineering is the set of skills and tools to design, size, and configure systems optimized for Agent Swarm Context, at any scale. “KV-cache hit rate is the single most important ...