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Beyond the Prototype: Using AI to Write High-Quality Code - Josh Albrecht, Imbue
In this case study-based keynote, Josh Albrecht, CTO of Imbue, examines the critical engineering challenges in building AI coding systems that create more than just prototypes. Drawing from Imbue's...
Software Development Agents: What Works and What Doesn't - Robert Brennan, AllHands/OpenHands
The adoption of AI into software development has been bumpy. While autocomplete tools like Copilot have gone mainstream, autonomous agents like Devin and OpenHands have generated both enthusiasm an...
Devin 2.0 and the Future of SWE - Scott Wu, Cognition
A talk on the future of software engineering with Scott Wu of Cognition AI, the makers of Devin. About Scott Wu Scott is the co-founder and CEO of Cognition AI. He previously competed in internati...
Your Coding Agent Just Got Cloned And Your Brain Isn't Ready - Rustin Banks, Google Jules
Will the future engineer code alongside a single coding agent, or will they spend their day orchestrating many agents? Traditional development rewards synchronous focus. This session dives into the...
Latent Space Paper Club: AIEWF Special Edition (Test of Time, DeepSeek R1/V3) — VIbhu Sapra
Recorded at the AI Engineer World's Fair in San Francisco. Stay up to date on our upcoming events and content by joining our newsletter here: https://www.ai.engineer/newsletter Timestamps: 00:00:...
Human seeded Evals — Samuel Colvin, Pydantic
In this talk I'll introduce the concept of Human-seeded Evals, explain the principle and demo them with Pydantic Logfire. ---related links--- https://x.com/samuel_colvin https://www.linkedin.com/...
Building AI Products That Actually Work — Ben Hylak (Raindrop), Sid Bendre (Oleve)
You've made the demo. How do you make the product? A lot of AI products don't actually work. Even worse, a lot of the techniques being advertised for making AI products better don't work either. We...
Rise of the AI Architect — Clay Bavor, Cofounder, Sierra w/ Alessio Fanelli
As the amount of consumer facing AI products grows, the most forward leaning enterprises have created a new role: the AI Architect. These leaders are responsible for helping define, manage, and evo...
AI That Pays: Lessons from Revenue Cycle — Nathan Wan, Ensemble Health
While much of the AI innovation in healthcare has centered on clinical and patient-facing applications, Revenue Cycle Management (RCM) remains an underexplored yet critical domain. Given the growin...
Structuring a modern AI team — Denys Linkov, Wisedocs
You've been given an AI mandate but don't have additional headcount, what next? Re-skilling, up-skilling and team augmentation become essential to delivering on a new mandate. In this talk we'll co...
The Rise of Open Models in the Enterprise — Amir Haghighat, Baseten
This year kicked off with the DeepSeek-R1 news cycle breaking out of our AI Engineering bubble into the mainstream tech and business world. Leaders at the highest levels of the largest enterprises ...
Mentoring the Machine — Eric Hou, Augment Code
You’d never let a swarm of fresh interns ship to prod on day one—same deal with AI agents. Mentoring the Machine dives into how acting like a tech lead (not just a user) turns those bots into real ...
Building Applications with AI Agents — Michael Albada, Microsoft
Generative AI has dramatically shortened the distance between ideas and implementation, enabling faster prototyping and deployment than ever before. But while language models can streamline individ...
AX is the only Experience that Matters - Ivan Burazin, Daytona
If you’re building devtools for humans, you’re building for the past. Already a quarter of Y Combinator’s latest batch used AI to write 95% or more of their code. AI agents are scaling at an expo...
How to build Enterprise Aware Agents - Chau Tran, Glean
While LLMs demonstrated impressive reasoning capabilities, their out-of-the-box reasoning is akin to hiring a brilliant but brand-new employee who doesn’t have the enterprise context of “how things...
Monetizing AI — Alvaro Morales, Orb
As AI continues to transform industries, companies are faced with the critical challenge of effectively monetizing AI-driven products in a way that captures value, ensures customer adoption, and sc...
Does AI Actually Boost Developer Productivity? (100k Devs Study) - Yegor Denisov-Blanch, Stanford
Forget vendor hype: Is AI actually boosting developer productivity, or just shifting bottlenecks? Stop guessing. Our study at Stanford cuts through the noise, analyzing real-world productivity dat...
How agents will unlock the $500B promise of AI - Donald Hruska, Retool
AI agents are on the cusp of revolutionizing work as we know it. The number of use cases software can tackle is set to explode as AI handles tasks requiring real judgment. But to cross the gap betw...
How Intuit uses LLMs to explain taxes to millions of taxpayers - Jaspreet Singh, Intuit
I will talk about how Intuit uses LLMs to explain tax situations to Turbotax users. Users want explanations of their tax situations - this drives confidence in the product. Over the course of last...
3 ingredients for building reliable enterprise agents - Harrison Chase, LangChain/LangGraph
It's easy to build a prototype of an agent, but hard to put an agent in production - especially in an enterprise setting. In this section, will talk about three ingredients for building reliable ag...
From Hype to Habit: How We’re Building an AI-First SaaS Company—While Still Shipping the Roadmap
What does it really take to move a modern SaaS company from AI experimentation to becoming truly AI-first? At Sprout Social, we’re in the midst of that transformation—rearchitecting strategy, syst...
Machines of Buying and Selling Grace - Adam Behrens, New Generation
How to go beyond browser automation to truly agentic commerce, where AI can buy, sell and negotiate on behalf of users and merchants. About Adam Behrens Adam Behrens is the co-founder and CEO of N...
How to Build Planning Agents without losing control - Yogendra Miraje, Factset
LLMs are getting smarter—but Agents are still unpredictable, unreliable, and hard to control. In this talk, I’ll share practical lessons from building real-world plan-and-execute agents —covering ...
Building Agents (the hard parts!) - Rita Kozlov, Cloudflare
AI workloads are rapidly shifting from AI being used for augmentation (co-pilots), to AI becoming responsible for full, end-to-end automation (agents). But building effective agents, and even more ...
POC to PROD: Hard Lessons from 200+ Enterprise GenAI Deployments - Randall Hunt, Caylent
The transition from experimental GenAI demonstrations to robust, production-grade systems involves significant technical and organizational complexities. Humans provide a ceiling on the true ROI of...
From Copilot to Colleague: Trustworthy Agents for High-Stakes - Joel Hron, CTO Thomson Reuters
This keynote will explore what it takes to move from basic generative assistants to fully agentic AI—systems that don’t just suggest but plan, act, and adapt—all within the structured, high-trust e...
How to Hire AI Engineers when EVERYONE is cheating with AI — Beth Glenfield, DevDay
AI broke recruitment - how to think about hiring for AI-enabled engineers in the era of AI cheating agents and AI customised resumes. Recorded at the AI Engineer World's Fair in San Francisco. Sta...
Stateful environments for vertical agents — Josh Purtell, Synth Labs
Hey All - gave a talk on building stateful environments for vertical agents at AI tinkerers and ppl really liked it, happy to do again. Here's the repo - general code that endows environments like ...
Books reimagined: AI to create new experiences for things you know — Lukasz Gandecki, TheBrain.pro
[last round of Attendee-Led 10min lightning talks] I will showcase how I got tired of waiting for an AI assisted/no spoiler book reading experience and built my own. Check 30s video at https://yout...
AI powered entomology: Lessons from millions of AI code reviews — Tomas Reimers, Graphite
This talk will explore insights from millions of automated code reviews, revealing trends in bugs, vulnerabilities, and code health that Graphite’s AI code review agent have uncovered. This talk wi...
Do You Trust Your AI’s Inferences? — Sahil Yadav, Hariharan Ganesan, Telemetrak
Enterprise AI adoption is accelerating, but with it comes a hard question: Do we trust the model’s decisions? In this 18-minute talk, I’ll explore the invisible risks behind automated decision-maki...
How to run Evals at Scale: Thinking beyond Accuracy or Similarity — Muktesh Mishra, Adobe
https://www.linkedin.com/in/mukteshkrmishra/
Continuous Profiling for GPUs — Matthias Loibl, Polar Signals
Continuous Profiling for GPUs extends our industry-leading continuous profiling platform to provide deep, always-on visibility into your GPU workloads. Now you can see exactly how your GPUs are be...
Top Ten Challenges to Reach AGI — Stephen Chin, Andreas Kollegger
an opener to the GraphRAG track!
Practical GraphRAG: Making LLMs smarter with Knowledge Graphs — Michael, Jesus, and Stephen, Neo4j
RAG has become one standard architecture component for GenAI applications to address hallucinations and integrate factual knowledge. While vector search over text is common, knowledge graphs repres...
Knowledge Graphs in Litigation Agents — Tom Smoker, WhyHow
Structured Representations are pretty important in the law, where the relationships between clauses, documents, entities, and multiple parties matter. Structured Representation means Structured Con...
When Vectors Break Down: Graph-Based RAG for Dense Enterprise Knowledge - Sam Julien, Writer
Enterprise knowledge bases are filled with "dense mapping," thousands of documents where similar terms appear repeatedly, causing traditional vector retrieval to return the wrong version or irrelev...
HybridRAG: A Fusion of Graph and Vector Retrieval - Mitesh Patel, NVIDIA
Interpreting complex information from unstructured text data poses significant challenges to Large Language Models (LLM), with difficulties often arising from specialized terminology and the multif...
tldraw.computer - Steve Ruiz, tldraw
Learn about tldraw's latest experiments with AI on an infinite canvas. In 2024, we created tldraw computer, a loose visual programming environment where arrows and LLMs powered every step of a grap...
Excalidraw: AI and Human Whiteboarding Partnership - Christopher Chedeau
Covid sent everybody home and created the space of virtual whiteboards. At first the experience reused the physical constraints but soon it became better than a physical whiteboard thanks to using ...
The Bitter Layout or: How I Learned to Love the Model Picker — Maximillian Piras, Yutori
Are conversational interfaces the future or, as many designers have suggested, a lazy solution that is bottlenecking AI-HCI? Despite well-documented usability issues, the design of many AI applicat...
UX Design Principles for Semi Autonomous Multi Agent Systems — Victor Dibia, Microsoft
Autonomous or semi-autonomous multi-agent systems (MAS) involve exponentially complex configurations (system config, agent configs, task management and delegation, etc.). These present unique inter...
Agentic GraphRAG: AI’s Logical Edge — Stephen Chin, Neo4j
AI models are getting tasked to do increasingly complex and industry specific tasks where different retrieval approaches provide distinct advantages in accuracy, explainability, and cost to execute...
CIAM for AI: Authn/Authz for Agents — Michael Grinich, CEO of WorkOS
AI agents are changing the way modern SaaS products operate. Whether automating workflows, integrating with APIs, or acting on behalf of users, AI-driven assistants and autonomous systems are becom...
Good design hasn’t changed with AI — John Pham, SF Compute
Bad designs are still bad. AI doesn’t make it good. The novelty of AI makes the bad things tolerable, for a short time. Building great designs and experiences with AI have the same first principles...
Building Effective Voice Agents — Toki Sherbakov + Anoop Kotha, OpenAI
How to build production voice applications and learnings from working with customers along the way! https://x.com/tokisherbakov https://www.linkedin.com/in/akotha7/
What every AI engineer needs to know about GPUs — Charles Frye, Modal
Every programmer needs to know a few things about hardware, like processors, memory, and disks. Due to AI systems' extreme demand for mathematical processing power, AI engineers need to know a few ...
Robots as professional Chefs - Nikhil Abraham, CloudChef
How we converted a bimanual robot into a professional chef that works in novel kitchens and learn new recipes from a single demonstration About Nikhil Abraham Nikhil is the CEO of CloudChef - reim...
![[Full Workshop] Reinforcement Learning, Kernels, Reasoning, Quantization & Agents — Daniel Han](https://i.ytimg.com/vi/OkEGJ5G3foU/mqdefault.jpg)
[Full Workshop] Reinforcement Learning, Kernels, Reasoning, Quantization & Agents — Daniel Han
Why is Reinforcement Learning (RL) suddenly everywhere, and is it truly effective? Have LLMs hit a plateau in terms of intelligence and capabilities, or is RL the breakthrough they need? In this w...
A Taxonomy for Next-gen Reasoning — Nathan Lambert, Allen Institute (AI2) & Interconnects.ai
Current AI models are extremely skilled, which was seen as the step change in evaluation scores across the industry in the first half of 2025, but often fail when presented with even medium time-ho...