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1:33:07ZH → EnglishLi Guangmi on a Year in Global LLMs: Scaling Law, the AGI Bet, and the US–China LandscapeGuest 李广密 · Host 张小珺Drawing on his front-row view from Silicon Valley, Shixiang Capital founder Li Guangmi looks back at the competition among OpenAI, Anthropic, Google, and Chinese model companies. He argues that model capability and cost together will shape the next wave of applications, while today’s enormous investments are bets on whether intelligence can keep scaling and how AI will reshape the economy and industry over the long term.
49:41ZH → EnglishFu Sheng: Large Models Are a Productivity Revolution, and Robots Have a New Window of OpportunityGuest 傅盛 · Host 张小珺Fu Sheng sees the heart of large models in their capacity for logic and reasoning. Their impact, he says, is closer to the steam engine than to another round of ordinary internet evolution. He also reflects on open models, service robots, and how entrepreneurs can face difficult periods.
2:04:15ENA parrot cannot paraphrase — and Claude cannot be talked into harmHost Nathan LabenzRobert Wright of the Nonzero podcast interviews Nathan Labenz on whether large language models understand anything, what Anthropic’s 10 million features actually show, and why open weights collide with U.S.–China policy.
15:24ENTesting the Anonymous sus-column-r Model, Later Identified as Grok-2Matthew Berman · Forward FutureMatthew Berman tests a mysterious 2024 Arena model on coding and reasoning; its verbose answers prompted speculation about OpenAI that the later identification contradicted.
14:32ENOpenAI Departures, Developer Tools and Synthetic Media: An August 2024 AI RoundupMatthew Berman · Forward FutureMatthew Berman reviews leadership changes, training-data disputes, structured API output, Qwen2-Math and emerging deepfake tools.
16:06ENQ* and Strawberry: What Was Known and Speculated in August 2024Matthew Berman · Forward FutureMatthew Berman reviews contemporary rumors and research concepts around reasoning, search and self-play, while the underlying project remained undisclosed.
46:09ENOriol Vinyals: The Days of Burning 90% of Compute on Pretraining Are NumberedHost Sarah Guo, Elad GilGoogle DeepMind’s VP of research on Gemini, long context, the coming shift from pretraining to RL, and why AGI may never have a finish line.
13:40ENTesting Llama 3.1 8B: Benchmark Gains and Mixed Practical ResultsMatthew Berman · Forward FutureMatthew Berman tests coding, reasoning and other prompts on the small model and finds several failures despite stronger reported benchmarks.
10:02ENTesting Llama 3.1 405B on Coding, Arithmetic and ReasoningMatthew Berman · Forward FutureMatthew Berman tries Meta's 2024 open-weight model on an informal set of prompts and demonstrates a sponsored dataset workflow.
15:50ENLlama 3.1: Meta’s 405B Model and the Open-Weight EcosystemMatthew Berman · Forward FutureMatthew Berman reviews Meta’s July 2024 release, the smaller models, reported benchmarks, synthetic data and the proposed Llama Stack.
0:58ENNVIDIA NIM: Containerized AI Inference MicroservicesMatthew Berman · Forward FutureA sponsored overview of NVIDIA’s approach to packaging model inference for local and cloud applications.
18:57ENAI News in July 2024: Small Models, Voice Tools and OpenAI SecurityMatthew Berman, Andrej Karpathy · Forward FutureMatthew Berman reviews AI product announcements, model evaluation and security reporting, and shares his predictions about the future of computing.
1:42:16EN267 Papers in Seven Months: Albert Gu Explains the State Space Model RevolutionHost Nathan LabenzThe Mamba co-author walks through five years of recurrent-model work, why selective compression beats a growing KV cache, and what Mamba 2 trades away to train faster.
13:30ENAI News in July 2024: Smart Glasses, Robot Skin and Model RoutingMatthew Berman, Mark Zuckerberg · Forward FutureMatthew Berman surveys early plans for AI glasses, robotics demonstrations, generative video and RouteLLM's model-routing results.
34:09ENFive Independent Teams Found the Same 10-to-1 Mix That Beats TransformersHost Sarah Guo, Elad GilThe authors of S4 and Mamba argue that mostly state-space layers plus a dash of attention is the architecture that actually works—and they are shipping it first as real-time speech.
1:54:01ENA Tiny Network Grew a Salt Crystal the Training Data Never ShowedHost Nathan LabenzTim Duignan trained tens of thousands of parameters on salt water. The model invented a crystal — and a continuum solvent theory he had spent a PhD failing to write by hand.
58:18ENDavid Luan on Model Scaling, AI Agents and Competition Across the Chip StackGuest David Luan · Host Harry StebbingsThe Adept co-founder explains his views on compute, vertical integration, enterprise agents and why human interaction should shape AI product design.
12:55ENTesting Mixture-of-Agents on Coding, Arithmetic and ReasoningMatthew Berman · Forward FutureMatthew Berman tries a multi-model setup on his informal LLM rubric and finds strong individual answers alongside coding and counting failures.
1:37:07ENA Text-Only AI Outdiagnosed GPs. Google Still Won't Ship It.Host Nathan LabenzGoogle researchers Vivek Natarajan and Khaled Saab walk Nathan Labenz through five medical-AI papers, from radiology reports to an AI doctor that still has to earn trust.
16:11ENAI News in June 2024: SSI, Tree Search, Meta, Claude and ARC PrizeMatthew Berman · Forward FutureMatthew Berman reviews new AI research, product launches and a competition, with context on historical benchmarks and uncertain claims.
16:49ENHow Apple's On-Device and Private Cloud Models WorkMatthew Berman · Forward FutureMatthew Berman examines Apple's 2024 foundation-model overview, from training data and quantization to task adapters and human evaluations.
13:18ENTesting Local AI on a Dual RTX 6000 WorkstationMatthew Berman · Forward FutureMatthew Berman tests language models, concurrent inference, and image generation on a Dell Precision tower supplied by Dell and NVIDIA.
27:37ENLeo Pekelis on Extending Llama 3 to a Million-Token ContextMatthew Berman, Leo Pekelis · Forward FutureGradient’s chief scientist explains long-context training, coding use cases, inference costs and why retrieval benchmarks tell only part of the story.
1:06:04ENAlexandr Wang on AI Data, Model Development and Building ScaleGuest Alexandr Wang · Host Harry StebbingsScale’s founder discusses his data-bottleneck thesis, enterprise adoption, AI security and the challenges of growing a team.
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