AI Engineer

How to run Evals at Scale: Thinking beyond Accuracy or Similarity — Muktesh Mishra, Adobe

Continuous Profiling for GPUs — Matthias Loibl, Polar Signals

Top Ten Challenges to Reach AGI — Stephen Chin, Andreas Kollegger

Practical GraphRAG: Making LLMs smarter with Knowledge Graphs — Michael, Jesus, and Stephen, Neo4j

Knowledge Graphs in Litigation Agents — Tom Smoker, WhyHow

When Vectors Break Down: Graph-Based RAG for Dense Enterprise Knowledge - Sam Julien, Writer

Stop Using RAG as Memory — Daniel Chalef, Zep

HybridRAG: A Fusion of Graph and Vector Retrieval - Mitesh Patel, NVIDIA

tldraw.computer - Steve Ruiz, tldraw

Excalidraw: AI and Human Whiteboarding Partnership - Christopher Chedeau

The Bitter Layout or: How I Learned to Love the Model Picker — Maximillian Piras, Yutori

CIAM for AI: Authn/Authz for Agents — Michael Grinich, CEO of WorkOS

Good design hasn’t changed with AI — John Pham, SF Compute

What every AI engineer needs to know about GPUs — Charles Frye, Modal

Robots as professional Chefs - Nikhil Abraham, CloudChef

[Full Workshop] Reinforcement Learning, Kernels, Reasoning, Quantization & Agents — Daniel Han

Daniel Han delivers a presentation on deep reinforcement learning (RL), covering topics such as kernels, agents, and quantization, and then transitions into an extensive Q&A session. He discusses the history of large language models, the open-source drought, and the jumps in performance achieved through supervised fine...

A Taxonomy for Next-gen Reasoning — Nathan Lambert, Allen Institute (AI2) & Interconnects.ai

How to Train Your Agent: Building Reliable Agents with RL — Kyle Corbitt, OpenPipe

OpenThoughts: Data Recipes for Reasoning Models — Ryan Marten, Bespoke Labs

Google Photos Magic Editor: GenAI Under the Hood of a Billion-User App - Kelvin Ma, Google Photos