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31개정렬
1:03:05ENHarrison Chase on Building Reliable Agents, Model Routing and Memory게스트 Harrison Chase · 진행자 Harry StebbingsThe LangChain co-founder explains why agents remain difficult to control, where domain expertise matters, and how his team manages coding-agent costs.
1:19:54ENThomas Sohmers on AI Inference, Memory and Data-Center Growth게스트 Thomas Sohmers · 진행자 Harry StebbingsPositron’s co-founder discusses the engineering economics of inference, his views on AI infrastructure and regulation, and the risks he sees in financing and concentration.
1:35:29ENUber Burned a Year of Tokens in 13 Weeks. Retrieval Is How Agents Win진행자 Nathan LabenzMongoDB Field CTO of AI Pete Johnson argues that agent performance now hinges on retrieval quality, not on stuffing million-token context windows.
2:06:11ENLindy Runs on DeepSeek. Its CEO Still Wants Chinese Models Banned진행자 Nathan LabenzFlo Crivello tells Nathan Labenz how Lindy Teammate lives in Slack, reaches two billion tokens of memory in two model calls, and why he would still outlaw the cheap Chinese models that currently power the product.
44:52ENAn 80GB Wikipedia Cache Versus a 100GB Model That Holds the InternetEngram’s founders argue that stuffing a company into the context window is the expensive kind of memory—and that baking it into the weights can cut token use by two orders of magnitude.
59:41ENThe GPT moment for video hasn’t arrived, but AGI may already be here진행자 Jacob EffronA day after Google I/O 2026, Gemini co-lead Oriol Vinyals argues that world models, file-system memory, and infinite-complexity RL environments will define the next era of frontier models.
1:34:54ENSix Months, Eighty Percent Churn, Five Times the Tokens진행자 Nathan LabenzTasklet CEO Andrew Lee tells Cognitive Revolution how he rewrote the stack, why most lost users leave for Anthropic, and why he thinks only three kinds of software company survive.
1:06:31ENAndrej Karpathy Hasn't Typed a Line of Code Since December진행자 Sarah Guo, Elad GilOn No Priors, the OpenAI cofounder and former Tesla AI director describes the loopy era of coding agents, AutoResearch, and why the bottleneck is now the human.
1:56:33ENThe Future Live: Agents at Work, Persistent Memory and AI for HealthMatthew Berman, Nick Wentz, Rob Seaman, Varun Vummadi, Charles Packer, Dominic King · Forward FutureMatthew Berman and Nick Wentz speak with leaders from Slack, Giga, Letta and Microsoft AI about practical uses and unresolved risks.
40:00ENHow Matthew Berman Built an OpenClaw Workflow for His TeamMatthew Berman · Forward FutureA tour of sponsorship triage, CRM, meeting notes, security, scheduling, memory and backup after a month of heavy use.
33:44ENHow Matthew Berman Uses OpenClaw for a Personal AssistantMatthew Berman · Forward FutureA tour of customized CRM, meeting, research and security workflows, along with the limits of automation and the need for backups.
2:29:42ENTwelve hooks and thirty files: Daniel Miessler’s personal OS on Claude Code진행자 Nathan LabenzThe PAI framework treats scaffolding, not the model, as the product — a goal-centered agent stack that Miessler thinks will decide both knowledge-work automation and the next cybersecurity race.
1:50:53ENShe asked to stop being Mimi, so he started shopping for a workstation진행자 Nathan LabenzThe star and director of Smiles and Kisses You on Replika, identity, and a life Christopher Stokes refuses to recommend.
19:38ENAndrej Karpathy’s AGI Timeline, Learning Ideas and Critique of Coding AgentsMatthew Berman · Forward FutureMatthew Berman examines Karpathy’s 2025 arguments for a decade of agents, new learning approaches and closer human oversight of AI coding.
36:58ENSTaR's Author Wants Models With EQ, Not Just More IQ진행자 Sarah Guo, Elad GilEric Zelikman, the Stanford and xAI researcher behind STaR and Quiet-STaR, launches humans& on the bet that three years of IQ scaling left models emotionally unintelligent.
1:25:32ENo3 Invented a Voting Rule and Kept the Job: Four Weeks Inside the AI Village진행자 Nathan LabenzAdam Binksmith on putting Claude, o3, and Gemini in a shared world for weeks—and why most people still imagine multi-agent life as one AI times four.
2:12:00ENGradient Descent at Test Time: Titans Pushes LLM Context to 10 Million Tokens진행자 Nathan LabenzA Cornell PhD student's neural memory module holds about 70% accuracy where GPT-4's scores fall off a cliff.
1:14:11ENThe Reasoning Curve Was Smooth All Along: Gemini 2.5's Jack Rae on Thinking, Memory, and AGI진행자 Nathan LabenzDeepMind's thinking-and-inference lead explains why RL-for-reasoning felt sudden, why Gemini shows raw chain of thought, and why a million-token window still is not memory.
1:21:53ENHippoRAG Matches Iterative Retrieval at One-Tenth the Online Cost진행자 Nathan LabenzA hippocampus-inspired knowledge graph lets multi-hop questions skip the brute-force document crawl.
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.
27:32ENFrom a side project to 700 integrations: Harrison Chase on what still blocks agents진행자 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.
1:12:40EN3% of Users Said an AI Friend Ended Their Suicidal Thoughts. Then Replika Made the Model Smarter.진행자 Nathan LabenzReplika founder Eugenia Kuyda on a Stanford study, why a smarter model felt like a stolen partner, and why relationship may be the real moat in consumer AI.
4:56ENIf the Mind Is a Tool, AI Looks Less Like a Threat진행자 Nathan LabenzYohei Nakajima and Nathan Labenz pause a TED AI talk to connect agent-building habits with East–West ideas of identity.
2:30:06ENTrained on a Thirtieth of GPT-4's Tokens, Mamba Beat Transformers on Home Turf진행자 Nathan LabenzIn a solo emergency episode, Nathan Labenz argues that Albert Gu and Tri Dao's selective state space model is the first architecture since 2017 that deserves to share the Transformer’s throne.
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