{"generated_at": "2026-07-14T03:16:57.378334Z", "slug": "training_data", "source_id": "src_training_data", "name": "Training Data", "episode_count": 10, "avg_signal": 81.5, "median_signal": 81.5, "top_signal": 87.3, "latest_episode_at": "2026-06-30T09:00:00Z", "earliest_episode_at": "2026-05-08T17:05:00Z", "category_mode": "ai", "cover_image_url": "https://megaphone.imgix.net/podcasts/fb9d2daa-719d-11f1-9e13-9f44da352371/image/e43393a9019870f9e2c5d83029da2159.jpg?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress", "rank_score": 166.703, "episodes": [{"episode_id": "ep_training_data_faf193cbceeb", "episode_title": "Why Hardware-Software Co-Design Is AI's Real 100x: Dylan Patel of SemiAnalysis", "podcast_name": "Training Data", "podcast_slug": "training_data", "source_id": "src_training_data", "category": "ai", "publish_date": "2026-06-30T09:00:00Z", "overall_score": 72.4, "score_breakdown": {"clarity": 74.6, "originality": 57.6, "hype_penalty": 18.0, "actionability": 100.0, "technical_depth": 100.0, "information_density": 100.0}, "podcast_cover_url": "https://megaphone.imgix.net/podcasts/fb9d2daa-719d-11f1-9e13-9f44da352371/image/e43393a9019870f9e2c5d83029da2159.jpg?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress", "source_link": "https://www.sequoiacap.com/", "audio_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI5467568199.mp3", "listen_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI5467568199.mp3", "verdict": "worth_your_time", "why_listen": "It goes beyond the title with direct discussion of like, know, it's, including: And then a big chunk is people who are formerly at hedge funds.", "summary": "I think it's really fun inside of Semi-Analysis because we have 90 people and like a big chunk of them are technologists, engineers across the whole supply chain. And you see these arguments like people are like, oh, well, that doesn't matter."}, {"episode_id": "ep_training_data_86347f814b99", "episode_title": "Memory and Continual Learning: Engram's Dan Biderman and Jessy Lin", "podcast_name": "Training Data", "podcast_slug": "training_data", "source_id": "src_training_data", "category": "ai", "publish_date": "2026-06-24T14:22:00Z", "overall_score": 78.8, "score_breakdown": {"clarity": 80.1, "originality": 53.8, "hype_penalty": 12.0, "actionability": 100.0, "technical_depth": 100.0, "information_density": 100.0}, "podcast_cover_url": "https://megaphone.imgix.net/podcasts/a982f792-6f55-11f1-b29a-1be1b9f3ee0a/image/3aedc280d2ffc8a464f57437c0c89836.jpg?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress", "source_link": "https://www.sequoiacap.com/", "audio_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI1349350448.mp3", "listen_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI1349350448.mp3", "verdict": "worth_your_time", "why_listen": "It goes beyond the title with direct discussion of like, know, think, including: How do we make the models learn that just as well as the models know the capital of France or how to write Python?.", "summary": "What about pre-training or even post-training makes it possible for the models to generalize in these magical emergent ways and controlling that process so that a company has a set of private data?. How do we make the models learn that just as well as the models know the capital of France or how to write Python?."}, {"episode_id": "ep_training_data_edc462b5848a", "episode_title": "Simulating Humans at Scale: Simile's Joon Sung Park", "podcast_name": "Training Data", "podcast_slug": "training_data", "source_id": "src_training_data", "category": "ai", "publish_date": "2026-06-16T09:00:00Z", "overall_score": 86.8, "score_breakdown": {"clarity": 63.9, "originality": 49.9, "hype_penalty": 0.0, "actionability": 100.0, "technical_depth": 100.0, "information_density": 100.0}, "podcast_cover_url": "https://megaphone.imgix.net/podcasts/e2c76d2e-68ea-11f1-b835-0fa4c8d306ac/image/b9265cdc1c170ca991d6d0ed8f94cdb0.jpg?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress", "source_link": "https://www.sequoiacap.com/", "audio_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI1964871992.mp3", "listen_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI1964871992.mp3", "verdict": "worth_your_time", "why_listen": "It goes beyond the title with direct discussion of actually, these, really, including: And when you read science fiction that covers societies that have progressed far enough in its technological maturity, you always see two pillars.", "summary": "I am somebody who is quite inspired by science fiction. You have some version of AGI, and you have some version of simulations that really help guide the society."}, {"episode_id": "ep_training_data_cd02b67e815a", "episode_title": "Google DeepMind's Logan Kilpatrick: Why the Model Eats the Harness", "podcast_name": "Training Data", "podcast_slug": "training_data", "source_id": "src_training_data", "category": "ai", "publish_date": "2026-06-11T09:00:00Z", "overall_score": 77.5, "score_breakdown": {"clarity": 61.5, "originality": 51.2, "hype_penalty": 9.0, "actionability": 100.0, "technical_depth": 100.0, "information_density": 100.0}, "podcast_cover_url": "https://megaphone.imgix.net/podcasts/c8ac5d90-6517-11f1-83d7-d3b49f07aa8f/image/3377c6b750c443581f8f20fb4b3b369c.jpg?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress", "source_link": "https://www.sequoiacap.com/", "audio_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI6013959399.mp3", "listen_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI6013959399.mp3", "verdict": "worth_your_time", "why_listen": "It goes beyond the title with direct discussion of like, think, it's, including: Yeah, I want this where we were talking off camera.", "summary": "So we could edit this set so it looks like we're. Like we should do that for the intro because I think it just like makes all this stuff more capable."}, {"episode_id": "ep_training_data_ab99e58effc4", "episode_title": "LIVE: Jensen Huang on Building the Dynamo of the Intelligence Age", "podcast_name": "Training Data", "podcast_slug": "training_data", "source_id": "src_training_data", "category": "ai", "publish_date": "2026-06-10T09:00:00Z", "overall_score": 79.5, "score_breakdown": {"clarity": 44.8, "originality": 47.7, "hype_penalty": 3.0, "actionability": 100.0, "technical_depth": 100.0, "information_density": 100.0}, "podcast_cover_url": "https://megaphone.imgix.net/podcasts/0fae4dc4-6456-11f1-9180-3f07b7c1bee8/image/4a16fd33b06708003827556209cd42b7.png?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress", "source_link": "https://www.sequoiacap.com/", "audio_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI2705410687.mp3", "listen_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI2705410687.mp3", "verdict": "worth_your_time", "why_listen": "It goes beyond the title with direct discussion of it's, that's, know, including: So we are in the middle of a massive AI revolution.", "summary": "Thank you so much, Jensen."}, {"episode_id": "ep_training_data_6ab310ec2353", "episode_title": "Knowing What Your Customers Want, All the Time: Listen Labs' Alfred Wahlforss", "podcast_name": "Training Data", "podcast_slug": "training_data", "source_id": "src_training_data", "category": "ai", "publish_date": "2026-06-02T09:00:00Z", "overall_score": 87.3, "score_breakdown": {"clarity": 66.9, "originality": 49.8, "hype_penalty": 0.0, "actionability": 100.0, "technical_depth": 100.0, "information_density": 100.0}, "podcast_cover_url": "https://megaphone.imgix.net/podcasts/245db97c-5de8-11f1-beb7-d7c1b176fe75/image/341de98b176149023f75a0cfc4e0bd07.jpg?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress", "source_link": "https://www.sequoiacap.com/", "audio_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI4652106703.mp3", "listen_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI4652106703.mp3", "verdict": "worth_your_time", "why_listen": "It goes beyond the title with direct discussion of like, it's, think, including: And it might be even something like sneakers.", "summary": "Our goal is to get to a billion people in our audience and then to be able to stratify and know what exactly is this person an expert on?. And it might be even something like sneakers."}, {"episode_id": "ep_training_data_3ef35ab768a1", "episode_title": "How Cursor Trained Composer on Fireworks: Distributed Infrastructure for High-Performance RL", "podcast_name": "Training Data", "podcast_slug": "training_data", "source_id": "src_training_data", "category": "ai", "publish_date": "2026-05-26T09:00:00Z", "overall_score": 86.8, "score_breakdown": {"clarity": 85.0, "originality": 94.0, "hype_penalty": 2.0, "actionability": 75.0, "technical_depth": 88.0, "information_density": 82.0}, "podcast_cover_url": "https://megaphone.imgix.net/podcasts/c85a76ee-3dd8-11f1-8934-37efe54dd294/image/89834425983d41c4f6fbb212a48603ac.png?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress", "source_link": "https://www.sequoiacap.com/", "audio_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI1086513324.mp3", "listen_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI1086513324.mp3", "verdict": "must_listen", "why_listen": "You'll learn how to build scalable, numerically stable reinforcement learning systems for agentic models using real-world simulation and delta-weight synchronization.", "summary": "Cursor trained its agentic coding model Composer using distributed reinforcement learning infrastructure that simulates real user environments at scale. The model, a 1 trillion parameter Mixture-of-Experts with 30B active parameters, undergoes mid-training on code tokens to learn library-specific behaviors and real-world context. To make RL efficient, they built a high-throughput system using asynchronous training, delta-based weight updates, and numerical stability controls to prevent divergence due to floating-point mismatches."}, {"episode_id": "ep_training_data_d8ae323550b7", "episode_title": "Rebuilding IT From the Ground Up for the AI Age: Serval's Jake Stauch", "podcast_name": "Training Data", "podcast_slug": "training_data", "source_id": "src_training_data", "category": "ai", "publish_date": "2026-05-19T09:00:00Z", "overall_score": 83.4, "score_breakdown": {"clarity": 90.0, "originality": 85.0, "hype_penalty": 2.0, "actionability": 75.0, "technical_depth": 82.0, "information_density": 75.0}, "podcast_cover_url": "https://megaphone.imgix.net/podcasts/11894f9c-52f7-11f1-a868-f30cc344c1ee/image/0ab3357de98e9ad6ad957c31c694a1a7.jpg?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress", "source_link": "https://www.sequoiacap.com/", "audio_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI4526751506.mp3", "listen_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI4526751506.mp3", "verdict": "must_listen", "why_listen": "You'll understand how to build durable enterprise AI products that survive rapid foundation model changes by focusing on workflow continuity, contextual agents, and economic efficiency.", "summary": "Serval is rebuilding IT for the AI age by automating enterprise workflows through a code-gen engine that turns natural language into executable automations with zero manual intervention. The company positions itself as an abstraction layer between foundation models and enterprise IT, maintaining moats through workflow context, guardrails, and unit economics rather than model ownership. As foundation models evolve rapidly, Serval focuses on stability, contextual awareness, and preventing agent sprawl while enabling non-technical users to build and maintain complex automations."}, {"episode_id": "ep_training_data_38cce8169049", "episode_title": "Suno's Mikey Shulman: Everyone Can Make Music Now", "podcast_name": "Training Data", "podcast_slug": "training_data", "source_id": "src_training_data", "category": "ai", "publish_date": "2026-05-13T18:55:00Z", "overall_score": 85.2, "score_breakdown": {"clarity": 90.0, "originality": 94.0, "hype_penalty": 2.0, "actionability": 75.0, "technical_depth": 82.0, "information_density": 75.0}, "podcast_cover_url": "https://megaphone.imgix.net/podcasts/beaede5a-4d82-11f1-9272-4f6372b94ca8/image/ba3ab676581811431f0fda5a047f7915.jpg?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress", "source_link": "https://www.sequoiacap.com/", "audio_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI5617471569.mp3", "listen_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI5617471569.mp3", "verdict": "must_listen", "why_listen": "Understand how AI is redefining creative participation by turning music into a social, user-driven medium rather than a top-down product.", "summary": "Suno enables anyone to create music using AI by modeling sound directly rather than separating vocals and instruments, collapsing the barrier between music consumers and creators. The platform has already produced chart-topping hits and record deals, proving that AI-generated music can achieve cultural and commercial relevance. Suno's success hinges on preference data and iterative model improvements, not just scale, and it aims to make music creation social again by embedding it in shared experiences."}, {"episode_id": "ep_training_data_7d5f1142cf72", "episode_title": "ElevenLabs' Mati Staniszewski: How Voice Becomes the Interface for Everything", "podcast_name": "Training Data", "podcast_slug": "training_data", "source_id": "src_training_data", "category": "ai", "publish_date": "2026-05-08T17:05:00Z", "overall_score": 76.8, "score_breakdown": {"clarity": 85.0, "originality": 85.0, "hype_penalty": 3.0, "actionability": 72.0, "technical_depth": 72.0, "information_density": 65.0}, "podcast_cover_url": "https://megaphone.imgix.net/podcasts/41e0e0de-4a72-11f1-8f25-0bf25b7d3e24/image/f7b5a9d461ffc6de3429681d50b18343.png?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress", "source_link": "https://www.sequoiacap.com/", "audio_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI5362828253.mp3", "listen_url": "https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI5362828253.mp3", "verdict": "must_listen", "why_listen": "Learn how a frontier AI company is building the infrastructure for voice-native agents and redefining human-computer interaction through emotional intelligence and full-stack control.", "summary": "Voice is evolving into the primary interface for human-computer interaction, with ElevenLabs building a full-stack voice engine that enables emotionally intelligent, context-aware voice agents. The company's defensible edge comes from vertical integration across speech-to-text, text-to-speech, and agent orchestration, combined with a talent model that embeds engineers in non-engineering teams. Real-world use cases span citizen services, education, and sales, where voice agents capture richer user intent than forms or buttons."}], "category_breakdown": [{"category": "ai", "count": 10}]}