Results
39 articlesSort
1:49:41ENA Team of Fifteen Matched Meta’s Post-Training on Meta’s Own BaseHost Nathan LabenzNathan Lambert walks through Tulu 3: the three-stage recipe, the 1 percent versus 14 percent lesson, and why open post-training now lives or dies on data and evals.
2:24:18ENMMLU Stays at Chance Until Late Training. Loss Is Only an Alarm.Host Nathan LabenzZyphra’s training lead walks through Zamba 2’s hybrid architecture, the evals that actually matter, and the trade secrets small labs still have to rediscover.
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.
1:42:17ENThe Cheapest Inference Provider Is the Most DangerousHost Nathan LabenzFireworks AI cofounders Lin Qiao and Dmytro Ivchenko explain why subsidized GPU bills vanish, how LoRA serving can cut cost a hundredfold, and why OpenAI is the rival that actually matters.
2:16:27ENSuperhuman Go Has a Blind Spot. Balaji Bets on a War of the GodsHost Nathan LabenzBalaji Srinivasan tells Nathan Labenz why adversarial inputs, missing actuators, and cryptography make a single runaway AI god less likely than a contest among many that still need priests.
1:14:36ENZulu Bypasses GPT-4, and a Hundred Examples Undo the GuardrailsHost Nathan LabenzNathan Labenz reads seven papers — four hopeful, three not — on whether anyone can actually keep a language model under control.
52:48ENOpenAI Cut Fine-Tuning 90 Percent, and Founders Crossed Llama 2 Off the ListHost Nathan LabenzNathan Labenz and Erik Torenberg walk through GPT-3.5 Turbo fine-tuning, why self-hosting still loses on total cost, and what is actually working in code, school, and medicine.
59:28ENFrom $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced ProductHost Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.
1:20:49ENA Wafer-Scale Chip So AI Stops Waiting on AmdahlHost Nathan LabenzAndrew Feldman, CEO of Cerebras, tells Nathan Labenz why one 2.5-trillion-transistor die, sparse linear algebra, and a 16-machine supercomputer are a better shape than a GPU cluster.
1:21:20ENFreeze the eyes and the brain. Train only the optic nerve.Host Nathan LabenzJunnan Li and Dongxu Li tell Nathan Labenz how BLIP cleaned noisy web captions, and how BLIP-2 connected a frozen vision model to a frozen language model in under ten days on one A100.
40:00ENThe Winds of Change: How Mistral Plans to Win the Open-Model EraHost Jacob EffronArthur Mensch explains Mistral's open-source strategy, enterprise distribution, model research, and views on AI regulation and sovereignty.
1:12:15ENThe Open-Model Vibe Check: China, Containment, and the Data MoatHost Jacob EffronJacob Effron and guests debate Chinese open-weight models, distillation, containment policy, the OpenAI–Hugging Face incident, and Cursor's data advantage.
50:23ENOpenAI’s Platform Bet: Where AI Value Accrues, Why Open Source Trails, and When Superintelligence ArrivesHost Jacob EffronPeter Welinder explains OpenAI’s platform strategy, the case for proprietary frontier models over open source, the enterprise hallucination problem, and his speculative timeline for AGI and superintelligence.
1:17:31ENImage AI Is Still at the GPT-2 Stage: Suhail Doshi on Building a Unified Vision ModelHost Nathan LabenzPlayground AI's founder explains why image generation has barely begun to deliver real utility, and why his team is betting 2024 on editing and a single model that can create, edit and understand pixels.









