Sora

Inside xAI: Building Grok Imagine in 3 Months, Videogen vs World Models, and Video Agents— Ethan He

Jun 1, 2026 · 1:44:43

Ethan He, former xAI and NVIDIA Cosmos researcher, explains how xAI built its first image and video models (Grok Imagine 0.9) from zero to one in three months, attributing rapid iteration to small teams with minimal meetings and strong infra that enabled fixing tiny data and training bugs for biggest quality gains. He argues that most improvements in video generation now come from language models and agents rather than diffusion technology, predicting that by end of 2025 video agents will produce production-grade content for ads. He defines world models as real-time, interactive, long-horizon videos, and details challenges like temporal compression, context management, and the high cost of storing and moving video data (e.g., tens of petabytes for a billion videos). Ethan also shares why he left xAI to focus on language model research, believing the next frontier is models that manage their own context length, similar to solutions already being explored in video generation.

Moonlake: Interactive, Multimodal World Models — with Chris Manning and Fan-yun Sun

Apr 2, 2026 · 1:06:48

Moonlake AI founders Chris Manning and Fan-yun Sun argue that interactive, multimodal world models require structured symbolic reasoning over pure scale, enabling indefinite multiplayer gameplay and causal consistency that video generation models like Genie and Sora cannot achieve. Their approach uses code engines and physics simulators as cognitive tools, producing reasoning traces that handle geometry, physics, and logic, while a separate diffusion model (Reverie) handles pixel fidelity. They aim to replace traditional rendering and empower creators by allowing human intent to be injected at a symbolic layer. Manning contrasts this with Yann LeCun's JEPA, emphasizing language and abstraction over pixel-level prediction. Moonlake is hiring engineers at the intersection of code generation, computer vision, and graphics.

After LLMs: Spatial Intelligence and World Models — Fei-Fei Li & Justin Johnson, World Labs

Nov 25, 2025 · 1:00:39

World Labs co-founders Fei-Fei Li and Justin Johnson argue that spatial intelligence is the next frontier beyond LLMs, with their product Marble generating editable 3D worlds from text or images using Gaussian splats. They claim language is a lossy channel for describing the rich 3D/4D world, while spatial intelligence handles everything from picking up a mug to inferring DNA's double helix. Marble already serves gaming, VFX, and film, with precise camera control and real-time rendering on phones and VR headsets. They discuss scaling compute from AlexNet to today's million-fold increase, why world models can 'soak up' modern GPU clusters better than language alone, and the gap between pattern-fitting and causal understanding in physics. Fei-Fei worries more about under-resourced academia than open vs. closed models, advocating for national AI compute clouds and open benchmarks like the BEHAVIOR dataset. Justin notes transformers are natively set models, not sequence models, opening architectural possibilities for new spatial data.

DevDay 2025: Apps SDK, Agent Kit, MCP, Codex and why Prompting is More Important than Ever

Oct 7, 2025 · 44:52

Sherwin Wu and Christina Huang from OpenAI's platform team discuss the new AgentKit suite and Apps SDK, arguing that visual agent builders and prompt optimization remain critical for production AI. They detail AgentKit's components—Agent SDK, visual Builder, Evals, and Tool Registry—and how Apps SDK inverts the website-chatbot paradigm by embedding apps inside ChatGPT. OpenAI adopted Anthropic's MCP protocol for tool connectivity, and the team dogfoods these tools for their own customer support at openai.com. They explain that automated prompt optimization and evaluation pipelines are essential for scaling agents, and share how Codex is used internally for code generation and PR reviews. The episode also covers the Service Health Dashboard for real-time API SLO tracking, reflecting OpenAI's reliability investments.

A Technical History of Generative Media

Sep 8, 2025 · 1:04:44

Fal.ai founders Gorkem and Batuhan detail their pivot from dbt pipelines to generative media inference, which now serves 2M developers and 350 models, crossed $100M ARR, and raised a $125M Series C. Key inflection points included Stable Diffusion 1.5 (company pivot), SDXL (first $1M revenue), Flux (jump from $2M to $10M monthly revenue), and Veo 3 (text-to-video with perfect lip-sync). They built a proprietary inference engine with 100+ custom kernels and a serverless GPU stack managing 10,000+ H100 equivalents across 6 cloud providers, typically delivering 1.5-10x speedups over stock PyTorch. Video models now drive 50% of revenue, up from 18% in February, fueled by open-source models like Hunyuan and partnerships with closed labs such as Play.ht and Google DeepMind. They argue advertising is the killer application for generative media and that image/video RL, specialized data pipelines, and cheaper conversational video models are underexplored startup opportunities.

Scaling Test Time Compute to Multi-Agent Civilizations — Noam Brown, OpenAI

Jun 19, 2025 · 1:17:47

Noam Brown, OpenAI researcher behind Cicero and reasoning models, joins hosts Alessio and Puix to discuss how test-time compute scaling drives multi-agent AI and the limits of the system 1/2 analogy. He explains that his Diplomacy bot Cicero reached top 10% of humans in 2022, and later he won the 2025 world championship. Brown argues that reasoning models like o3 succeed in unverifiable domains (e.g., Deep Research) and that harnesses and routers will be washed away by scale. He reveals OpenAI's multi-agent team is pursuing a principled approach, not heuristic, and envisions AI civilizations collaborating. He also shares his coding stack (Codex, Windsurf) and predicts test-time compute will hit cost and wall-clock bottlenecks.

Best of 2024 in Vision [LS Live @ NeurIPS]

Dec 22, 2024 · 55:46

Isaac Robinson and Peter Robicheaux from Roboflow and Vik Korrapati from Moondream recap the biggest shifts in computer vision in 2024, naming Sora as the year's most significant paper despite being a blog post, and highlighting its replication efforts (OpenSora) that use MAGViT V2 and diffusion transformers. They detail how SAM 2 extends the SAM approach to video with a memory bank and real-time performance, and how DETR-based detectors (RT-DETR, LW-DETR, DeFine) now surpass YOLOs on COCO by 4.6 AP at the same latency. Peter explains the CLIP-blind phenomenon: MMVP shows leading VLMs (ChatGPT, Gemini, LLaVA) fail at fine-grained tasks like reading clock hands, but models like PaliGemma 2 and AimV2 improve by incorporating pixel-level understanding and autoregressive image reconstruction, with AimV2 approaching 60.2 mAP on COCO. Vik introduces Moondream's 0.5B parameter pruned VLM and demonstrates a grounded chain-of-thought approach that dramatically improves sample efficiency for analog gauge reading, reducing the need for millions of synthetic examples.

The Ultimate Guide to Prompting - with Sander Schulhoff from LearnPrompting.org

Sep 20, 2024 · 1:07:09

Sander Schulhoff, author of The Prompt Report and founder of LearnPrompting, presents a comprehensive taxonomy of 58 prompting techniques and argues that prompt engineering is a skill everyone should have, not a specialized role. He categorizes techniques by problem-solving strategy (zero-shot, few-shot, thought generation, decomposition, ensembling, self-criticism) and debunks role prompting for accuracy-based tasks, showing an 'idiot' prompt outperformed a 'genius' prompt on MMLU. Schulhoff shares his experience running Hack-a-Prompt, which collected 600,000 malicious prompts and won best paper at EMNLP, and differentiates between prompt injection and jailbreaking. He advocates for automatic prompt engineering with DSPy, which beat his manual effort in 10 minutes, and previews Hack-a-Prompt 2.0, aiming for a $500,000 prize pool to generate real-world harms for safety tuning.