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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.
21:00ENJim Fan's Foundation Agent Vision: Skills, Robot Bodies, and SimulationMatthew Berman, Jim Fan · Forward FutureMatthew Berman examines research on Minecraft agents, varied robot bodies, and simulation as steps toward more general embodied AI.
1:43:07ENAn Agent Posted Five Tweets — and 5,000 People Didn't NoticePresenta: 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.
1:50:57ENTwenty-Eight Days After GPT-4, an AI Designed and Ran a Chemistry ExperimentPresenta: 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.
29:58ENRabbit R1’s 2024 Launch Demo: An AI Assistant Built Around ActionsMatthew Berman, Jesse Lyu · Forward FutureMatthew Berman reviews Rabbit’s launch presentation, from voice-controlled services and experimental teach mode to its proposed $199 device.
1:22:59ENIf Your Agent Is Right 80% of the Time, Ten Steps Leave You With 10%Presenta: 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 AgentsPresenta: 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.
1:36:39ENOpenAI’s $5 Million Bet: A 60-Person Team Wants GPT-4V to Give Cars Common SensePresenta: Nathan LabenzGhost Autonomy cofounder John Hayes tells Cognitive Revolution why a multimodal model on the driving path could reset self-driving after two decades of DARPA-era stacks.
1:03:46ENWhen Code Costs a Dime, You Throw the App AwayPresenta: Nathan LabenzLindy founder Flo Crivello tells Nathan Labenz how an AI employee learns APIs from documentation, why GPT-4 made quality the only metric that matters, and why even he wants aviation-style rules for AI.
1:25:11EN105 lines of code, a million impressions, and a VC who refused to found the companyPresenta: 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.
4:42ENConnecting AutoGen to a Local Model With LM StudioMatthew Berman · Forward FutureMatthew Berman demonstrates an early local-model setup and explains where agent behavior still needs work.
42:25ENThe Transformer Co-Author Who Wants to Give AI a WalletPresenta: Sarah Guo, Elad GilIllia Polosukhin, co-author of Attention Is All You Need and co-founder of NEAR, argues that a blockchain account turns a language-model agent into an economic actor that can hire people and other AIs.
55:13ENTwo Days to a Prototype: How a16z Turned a Stanford Paper Into AI TownPresenta: Nathan LabenzA companion named Alice, a weekend Phaser hack, and Martin Casado's argument that models will never replace formal languages.
1:40:44ENGive the Model Time, Memory, and Tools: Nathan Labenz’s 2023 Field Guide to Using GPT-4Presenta: Nathan LabenzPart two of the AI Scouting Report walks from H100 clusters and RLHF through chain-of-thought, retrieval, agents, and mixture-of-experts.
59:41ENHumans Are Single-Threaded. MULTI·ON Wants 100 Agents Running AnywayPresenta: Nathan LabenzStanford PhD dropout Div Garg put a personal agent in Chrome so it can reuse your logins, ask for help when it stalls, and stop before it hits Buy.
1:40:07ENThirty thousand voltage updates a second: inside Skydio’s autonomy stackPresenta: Nathan LabenzHayk Martiros walks Nathan Labenz through a two-pound drone that sees in every direction, runs more than ten neural nets on board, and will not be weaponized.
1:30:29ENGPT-4 Wins Ten Times More Bill Fights: DoNotPay's Robot-Lawyer WarPresenta: Nathan LabenzGPT-3 thanked Comcast for twenty dollars off. GPT-4 said that was not enough — and the robot lawyer called it an arms race.
51:55ENNine orders of magnitude later, he shipped a companion, not an oraclePresenta: Sarah Guo, Elad GilDeepMind co-founder Mustafa Suleyman tells No Priors why Inflection’s first product is a personal AI that sits in your corner, and why the missing piece is a router, not a bigger model.
58:26ENSeventy Percent of the Inbox Is a Pitch: Matt Welsh on the First Wave of AI AgentsPresenta: Nathan LabenzThe Fixie.ai founder maps copilot versus delegation, the missing appendages into our systems, and why agents will need guardrails, memory hygiene, and probably an earpiece.
1:29:31EN130,000 Teleop Demos: Google's GPT Playbook for RobotsPresenta: Nathan LabenzKeerthana Gopalakrishnan of Google Robotics tells Nathan Labenz how RT-1, SayCan, and PaLM-E turn language-model scaling into kitchen-ready mobile manipulators.
1:25:46ENDon't Make the Tools Agentic: Flo Crivello on What Production Agents Actually NeedPresenta: Nathan LabenzThe Lindy CEO tells Nathan Labenz that most working agents are intelligent workflows with human-designed control flow — and that scaffolding may still matter even if AGI arrives.
4:57ENHow Microsoft Research Used ChatGPT to Guide Robots and DronesMatthew Berman · Forward FutureA 2023 research demonstration combines natural-language instructions, robot APIs and human review to generate code for physical and simulated tasks.
1:00:36ENForty games of Diplomacy, and nobody spotted the bot: Noam Brown’s 100,000x lessonPresenta: Sarah Guo, Elad GilMeta’s Noam Brown tells Sarah Guo and Elad Gil how Cicero negotiated in English for 40 games on a tiny web community — and why thinking at decision time beat three years of scaling.
2:29:21ENThe Poker Bot That Never Tried to Read a SoulInvitado: Noam Brown · Presenta: Lex FridmanNoam Brown tells Lex Fridman how a Nash-equilibrium bot crushed heads-up no-limit Hold'em, how Pluribus did it six-handed, and why Diplomacy required an AI that could earn human trust.
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