Browse Lessons

The Race to Production-Grade Diffusion LLMs [Stefano Ermon] - 764

The Decentralized Future of Private AI [Illia Polosukhin] - 749

Inside Nano Banana 🍌 and the Future of Vision-Language Models [Oliver Wang] - 748

Is It Time to Rethink LLM Pre-Training? [Aditi Raghunathan] - 747

Building an Immune System for AI Generated Software [Animesh Koratana] - 746

Autoformalization and Verifiable Superintelligence [Christian Szegedy] - 745

Multimodal AI Models on Apple Silicon with MLX [Prince Canuma] - 744

Genie 3: A New Frontier for World Models [Jack Parker-Holder and Shlomi Fruchter] - 743

Closing the Loop Between AI Training and Inference [Lin Qiao] - 742

Context Engineering for Productive AI Agents [Filip Kozera] - 741

Infrastructure Scaling and Compound AI Systems [Jared Quincy Davis] - 740

Building Voice AI Agents That Don’t Suck [Kwindla Kramer] - 739

Distilling Transformers and Diffusion Models for Robust Edge Use Cases [Fatih Porikli] - 738

What are the four requirements of agent-to-agent collaboration? #aiagents #agntcy

Is collaboration the right path over competition among protocols like A2A, MCP, and ACP? #aiagents

Humans are probabilistic. Vijoy explains deterministic sandwich and why agents need semantic routing

Building the Internet of Agents [Vijoy Pandey] - 737

LLMs for Equities Feature Forecasting at Two Sigma [Ben Wellington] - 736

Zero-Shot Auto-Labeling: The End of Annotation for Computer Vision [Jason Corso] - 735

Grokking, Generalization Collapse, and Dynamics of Training Deep Neural Nets [Charles Martin] - 734

Google I/O 2025 Special Edition - 733

RAG Risks: Why Retrieval-Augmented LLMs Are Not Safer [Sebastian Gehrmann] - 732

From Prompts to Policies: How RL Builds Better AI Agents [Mahesh Sathiamoorthy] - 731

How OpenAI Builds AI Agents That Think and Act [Josh Tobin] - 730

CTIBench: How Good Are LLMs at Detecting Cyber Threats? [Nidhi Rastogi] - 729

Generative Benchmarking: Measuring AI Models Beyond Accuracy [Kelly Hong] - 728

Inside the “Neurons” of LLMs: Circuit Tracing Their Hidden Biology [Emmanuel Ameisen] - 727

Self-Reflecting LLMs: Reinforcement Learning That Boosts Reasoning [Maohao Shen] - 726

Inside Waymo’s New Foundation Model Powering Self-Driving Cars [Drago Anguelov] - 725

Dynamic Token Merging: 2× Faster Byte-Level LLMs [Julie Kallini] - 724

Latent Reasoning: Scaling Test-Time Compute for Smarter LLMs [Jonas Geiping] - 723

Visualization-of-Thought: Multimodal Models That Imagine While They Reason [Chengzu Li] - 722

s1: A High-Performance Reasoning Model Trained for Under $50 [Niklas Muennighoff] - 721

Is CUDA still a moat for NVIDIA? #aihardware #aichips #podcast

What if AI hardware innovation isn’t about the future but focusing on what won’t change?

The convergence of two market forces is driving more efficient and intelligent frontier models

Accelerating AI Training and Inference with AWS Trainium2 with Ron Diamant - 720

AI Trends 2025: AI Agents and Multi-Agent Systems with Victor Dibia - 718

Speculative Decoding and Efficient LLM Inference with Chris Lott - 717

Ensuring Privacy for Any LLM with Patricia Thaine - 716

AI Engineering Pitfalls with Chip Huyen - 715

Evolving MLOps Platforms for Generative AI and Agents with Abhijit Bose - 714

Why Agents Are Stupid & What We Can Do About It with Dan Jeffries - 713

Automated Reasoning to Prevent LLM Hallucination with Byron Cook - 712

AI at the Edge: Qualcomm AI Research at NeurIPS 2024 with Arash Behboodi - 711

AI for Network Management with Shirley Wu - 710

Why Your RAG System Is Broken, and How to Fix It with Jason Liu - 709

An Agentic Mixture of Experts for DevOps with Sunil Mallya - 708