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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.
49:50ENUnconstrained agents captivated Twitter. Custom ones are what ship.LangChain founder Harrison Chase tells Sequoia’s Training Data what an agent actually is, why AutoGPT stalled, and which cognitive architectures reach production.
1:00ENAndrew Ng on Four Agentic AI Design PatternsMatthew Berman, Andrew Ng · Forward FutureA short excerpt identifies reflection, tools, planning and multi-agent collaboration as approaches to building AI agents.
29:28ENDevin Will Not Replace Engineers. It Will Multiply Them, Says Cognition’s Scott WuHost Sarah Guo, Elad GilThe IOI champion behind Devin explains why an AI software engineer that you can watch over the shoulder creates more human engineers, not fewer.
16:22ENHarrison Chase on Planning, Interface Design and Memory for AI AgentsMatthew Berman, Harrison Chase · Forward FutureThe LangChain CEO outlines open questions for agent builders, with commentary from Matthew Berman on oversight, workflows and memory.
13:59ENSWE-agent Tutorial: Navigating Repositories and Attempting GitHub FixesMatthew Berman, Carlos · Forward FutureMatthew Berman sets up a research coding agent, hits an installation problem and a cost limit, then shows an author’s successful SymPy benchmark example.
27:32ENFrom a side project to 700 integrations: Harrison Chase on what still blocks agentsHost Sarah Guo, Elad GilThe LangChain CEO walks through a two-year churn of patterns, and why memory, planning, and few-shot data still matter more than swapping models.
42:14ENMeta’s $197 Billion Day Was a Partial Answer to AI’s $200 Billion QuestionHost Sarah Guo, Elad GilSarah Guo and Elad Gil walk through Gemini, Mistral, NVIDIA’s upgrade cycle, Klarna’s support bot, and why the real AI spend still sits with the hyperscalers.
55:29ENBuilding a CrewAI Newsletter Team: Scraping, Summarizing and DebuggingMatthew Berman, Joe · Forward FutureMatthew Berman builds a two-agent newsletter prototype, works through input and scraping errors, and shows what still needs to be fixed.
4:56ENIf the Mind Is a Tool, AI Looks Less Like a ThreatHost Nathan LabenzYohei Nakajima and Nathan Labenz pause a TED AI talk to connect agent-building habits with East–West ideas of identity.
1:45:21EN600,000 Attack Prompts, and Nearly 40% Still Pwned GPT-4Host Nathan LabenzSander Schulhoff walks Nathan Labenz through the prompting habits that still work, the role-play myth that does not, and the global HackAPrompt contest that mapped how language models get hijacked.
15:11ENBuilding a Transcript-to-Article Team in AutoGen StudioMatthew Berman · Forward FutureMatthew Berman configures a retrieval skill, two agents and a group workflow, then troubleshoots missing credentials and output.
1:43:07ENAn Agent Posted Five Tweets — and 5,000 People Didn't NoticeHost Nathan LabenzMultiOn founder Div Garg walks Cognitive Revolution through what web agents can actually do in early 2024, what they cost, and why guardrails have to sit outside the model.
46:36ENAfter a decade of code search, Sourcegraph’s CTO says the coding-AI fight is outside the modelHost Sarah Guo, Elad GilBeyang Liu on Cody, why keyword search still does most of RAG, and why CS fundamentals get more valuable as AI compresses the middleware.
1:50:57ENTwenty-Eight Days After GPT-4, an AI Designed and Ran a Chemistry ExperimentHost Nathan LabenzGabe Gomes and his Carnegie Mellon students built Coscientist, a language-model agent that plans, codes, and executes real experiments on a remote cloud lab.
1:06:39ENOpenAI's First DevRel Writes 90% of His Code with ChatGPT — and Says Web Agents Still Need YearsHost Jacob EffronLogan Kilpatrick on picking GPT-3.5 versus GPT-4, why plugins stalled, and why the public internet is not ready for autonomous agents.
1:22:59ENIf Your Agent Is Right 80% of the Time, Ten Steps Leave You With 10%Host Nathan LabenzImbue CTO Josh Albrecht tells Nathan Labenz how a 27-person, pre-product research company is putting $200 million and 10,000 H100s toward agents that can actually finish a long job.
32:48ENFourteen People, $200 Million of Compute, and the Reliability Bottleneck for AgentsHost Sarah Guo, Elad GilImbue co-founders Kanjun Qiu and Josh Albrecht on why agents fail, why language models cannot learn general algorithms, and why they are spending a giant round on GPUs rather than headcount.
1:14:48ENDevDay Probably Created Thousands of Startups, Says OpenAI's Logan KilpatrickHost Nathan LabenzThe day after OpenAI's first developer conference, Dev Relations lead Logan Kilpatrick tells Nathan Labenz why thin wrappers were never companies, why agents were deliberately not shipped fully autonomous, and what $2 million custom models are for.
1:25:11EN105 lines of code, a million impressions, and a VC who refused to found the companyHost Nathan LabenzBabyAGI creator Yohei Nakajima tells Nathan Labenz how a weekend prototype set off the agent wave — and why he went back to writing checks.
1:32:41ENThere are maybe 5,000 people who can train frontier models. The rest need AI engineersHost Nathan LabenzSwyx of Latent Space tells Nathan Labenz how a new job is forming, why OpenAI will Sherlock its developers, and what still does not count as an agent.
55:13ENTwo Days to a Prototype: How a16z Turned a Stanford Paper Into AI TownHost Nathan LabenzA companion named Alice, a weekend Phaser hack, and Martin Casado's argument that models will never replace formal languages.
1:39:53ENUnder 100 People, 24 Million Users: Replit's Bet on Artificial Developer IntelligenceHost Nathan LabenzMichele Catasta, Replit's new VP of AI, lays out a generate-execute-learn loop he will not call AGI, a 2x-to-1000x productivity ladder, and a keystroke-level data advantage.
39:45ENPalantir's 13th Employee: The Model Is a Stochastic Genie. Ontology Is the Stake That Tethers ItHost Sarah Guo, Elad GilCTO Shyam Sankar on 550 microservices, air-gapped submarines, and why Palantir's ontology is what large language models were waiting for.
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