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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...
How to Train Your Agent: Building Reliable Agents with RL — Kyle Corbitt, OpenPipe
Have you ever launched an awesome agentic demo, only to realize no amount of prompting will make it reliable enough to deploy in production? Agent reliability is a famously difficult problem to sol...
OpenThoughts: Data Recipes for Reasoning Models — Ryan Marten, Bespoke Labs
Peel back the curtain on state of the art model post-training through the story of OpenThinker, a SOTA small reasoning model (outperforming DeepSeek distill), built in the open. Learn about the dat...
Google Photos Magic Editor: GenAI Under the Hood of a Billion-User App - Kelvin Ma, Google Photos
Go behind the scenes of Google Photos' Magic Editor. Explore the engineering feats required to integrate complex CV and cutting-edge generative AI models into a seamless mobile experience. We'll di...
Dream Machine: Scaling to 1m users in 4 days — Keegan McCallum, Luma AI
Talking about Luma AI, our mission, and how our ML infrastructure enables SOTA multimodal model development About Keegan McCallum I'm Keegan McCallum, the Head of ML infrastructure at Luma AI. I ...
ComfyUI Full Workshop — first workshop from ComfyAnonymous himself!
Quick introduction to ComfyUI and what's new followed by a QA session. Recorded at the AI Engineer World's Fair in San Francisco. Stay up to date on our upcoming events and content by joining our ...
Design like Karpathy is watching — Zeke Sikelianos, Replicate
Legendary AI engineer and educator Andrej Karpathy recently blogged about his experiences building, deploying, and monetizing a vibe-coded web app called MenuGen. Let's dig into the challenges he f...
On Curiosity — Sharif Shameem, Lexica
Creating and sharing demos is the easiest way to influence the future. It gets people to think about what's possible. A good tech demo doesn't have to be fully fleshed out. It doesn't even have to ...
Real world MCPs in GitHub Copilot Agent Mode — Jon Peck, Microsoft
As developers, we don't spend most of our time vibe-coding prototypes. More often, we're adding features, squashing bugs, and building tests for existing apps across a wide variety of services and ...
The rise of the agentic economy on the shoulders of MCP — Jan Curn, Apify
Thanks to MCP and all the MCP server directories, agents can now autonomously discover new tools and other agents. This lays down the foundation for the future agentic economy, where businesses wil...
MCP is all you need — Samuel Colvin, Pydantic
Everyone is talking about agents, and right after that, they’re talking about agent-to-agent communications. Not surprisingly, various nascent, competing protocols are popping up to handle it. But...
Full Spec MCP: Hidden Capabilities of the MCP spec — Harald Kirschner, Microsoft/VSCode
The true power of Model Context Protocol emerges when clients and servers collaborate across the full spectrum of the specification. This talk presents practical examples of how VS Code's comprehen...
Shipping an Enterprise Voice AI Agent in 100 Days - Peter Bar, Intercom Fin
What does it take to go from blank page to live enterprise voice agent in 100 days? That’s the challenge we took on with Fin Voice at Intercom. Enterprise customer service demands high-quality, re...
The State of Generative Media - Gorkem Yurtseven, FAL
Generative AI is reshaping the creative landscape, enabling the production of images, audio, and video with unprecedented speed and sophistication. This session offers an in-depth exploration of th...
Teaching Gemini to Speak YouTube: Adapting LLMs for Video Recommendations to 2B+DAU - Devansh Tandon
YouTube recommendations drive the majority of video watch time for billions of daily users. Traditionally powered by large embedding models (LEMs), we're undertaking a fundamental shift: rebuilding...
Transforming search and discovery using LLMs — Tejaswi & Vinesh, Instacart
Learn how Instacart uses cutting-edge LLMs to redefine search and product discovery. - Explore innovative solutions overcoming traditional search engine limitations for grocery shopping. - Discove...
Netflix's Big Bet: One model to rule recommendations: Yesu Feng, Netflix
Discuss the foundation model strategy for personalization at Netflix based on this post https://netflixtechblog.com/foundation-model-for-personalized-recommendation-1a0bd8e02d39 and recent developm...
360Brew: LLM-based Personalized Ranking and Recommendation - Hamed and Maziar, LinkedIn AI
We will give a talk about our journey of building a foundation model for solving ranking and recommendation tasks About Hamed Firooz Principal AI Scientist at LinkedIn Core AI. With 15 years in la...
What We Learned from Using LLMs in Pinterest — Mukuntha Narayanan, Han Wang, Pinterest
Pinterest Search integrates Large Language Models (LLMs) to enhance relevance scoring by combining search queries with rich multimodal content, including visual captions, link-based text, and user ...
ARC AGI-3: Interactive Reasoning Benchmarks for Measuring AGI — Greg Kamradt, ARC Prize Foundation
ARC Prize Foundation is building the North Star for AGI—rigorous, open benchmarks that track reasoning progress in modern AI. We'll show why static AGI evaluations are useful, but fall short when c...
RL for Autonomous Coding — Aakanksha Chowdhery, Reflection.ai
The models and techniques to build fully autonomous coding agents - not just coding copilots - are already here. In this talk, former Google DeepMind staff research scientist, now CEO of Reflection...
Recsys Keynote: Improving Recommendation Systems & Search in the Age of LLMs - Eugene Yan, Amazon
Recommendation systems and search have long adopted advances in language modeling, from early adoption of Word2vec for embedding-based retrieval to the transformative impact of GRUs, Transformers, ...
Benchmarks Are Memes: How What We Measure Shapes AI—and Us - Alex Duffy, Every.to
Benchmarks shape more than just AI models—they shape our future. The things we choose to measure become self-fulfilling prophecies, guiding AI toward specific abilities and, ultimately, defining hu...
Small AI Teams with Huge Impact — Vik Paruchuri, Datalab
We scaled Datalab 5x this year - to 7-figure ARR, with customers that include tier 1 AI labs. We train custom models for document intelligence (OCR, layout), with popular repos surya and marker. I...
Rethinking Team Building: how a 30-person Startup serves 50 Million Users — Grant Lee, Gamma
The central thesis of this talk is that in the rapidly evolving age of AI, startups and tech companies should reject the traditional "blitzscaling" model of hyper-growth and specialized roles. Inst...
Building a 10 person unicorn - Max Brodeur-Urbas, Gumloop
An overview of how Gumloop is scaling automation across companies like Instacart, Webflow and Shopify with less than 10 people. About Max Brodeur-Urbas ex-microsoft engineer, started Gumloop in my...
Using OSS models to build AI apps with millions of users — Hassan El Mghari
In this talk, Hassan will go over how he builds open source AI apps that get millions of users like roomGPT.io 2.9 million users, restorePhotos.io 1.1 million users, Blinkshot.io 1 million visitors...
Bolt.new: How we scaled $0-20m ARR in 60 days, with 15 people — Eric Simons, Bolt
Tiny Teams are the future of how startups are built, and it all comes down to team culture, decision making, tooling choices, and endless grit. In this talk, Eric will share the high octane insigh...