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16:49ENHow Apple's On-Device and Private Cloud Models WorkMatthew Berman · Forward FutureMatthew Berman examines Apple's 2024 foundation-model overview, from training data and quantization to task adapters and human evaluations.
58:16ENVal Scholz on Revolut’s Referral Growth, Hiring and ScalingInvitado: Val Scholz · Presenta: Harry StebbingsThe former Revolut growth leader explains onboarding, referrals, experimentation and the organizational challenges of rapid growth.
13:18ENTesting Local AI on a Dual RTX 6000 WorkstationMatthew Berman · Forward FutureMatthew Berman tests language models, concurrent inference, and image generation on a Dell Precision tower supplied by Dell and NVIDIA.
27:26ENA 40-Person Company With 40,000 Paying Customers Wants to Replace the Camera With AIPresenta: Sarah Guo, Elad GilHeyGen CEO Joshua Xu tells Sarah Guo and Elad Gil how avatars, localization, and a component-wise video stack are meant to knock the camera out of content production.
3:02:16ENThe journey begins after the answer: Perplexity CEO Aravind Srinivas on search and curiosityInvitado: Aravind Srinivas · Presenta: Lex FridmanIn 2022, Perplexity's first hire asked about health insurance — and four PhDs could not get a clear answer from Google.
1:02:17ENMichael Eisenberg on AI Investing, Liquidity and Venture DisciplineInvitado: Michael Eisenberg · Presenta: Harry StebbingsThe Aleph investor discusses AI business models, national security, venture exits and the risks of mistaking a technology boom for durable returns.
1:45:11ENAI already beats doctors at empathy. Catastrophe is still not the defaultPresenta: Nathan LabenzNathan Labenz tells Sarah Hastings Woodhouse why he is an adoption accelerationist and a hyperscaling pauser, and lists eight reasons he will not join the hardcore doomers.
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.
16:06ENAI News: Video Generation, Apple Intelligence, Robotics and DeepSeek Coder V2Matthew Berman · Forward FutureMatthew Berman reviews a week of launches and research, from Luma and Runway to NVIDIA, robotics and coding models.
0:57ENASM and the Equipment Behind Semiconductor ManufacturingMatthew Berman · Forward FutureIn an ASM-sponsored short, Matthew Berman highlights the machinery and people involved in making the chips used in everyday devices.
1:24:03ENDanny Rimer on Missed Deals, Investment Discipline and Building Index VenturesInvitado: Danny Rimer · Presenta: Harry StebbingsThe Index partner reflects on Spotify, Snap, Airbnb and Facebook, explains how the firm makes decisions, and weighs patience against the need to realize returns.
27:37ENLeo Pekelis on Extending Llama 3 to a Million-Token ContextMatthew Berman, Leo Pekelis · Forward FutureGradient’s chief scientist explains long-context training, coding use cases, inference costs and why retrieval benchmarks tell only part of the story.
1:25:10ENA Tenth of Google's Index, 99.9% Recall: How 20 People Built Brave SearchPresenta: Nathan LabenzJosep M. Pujol walks Cognitive Revolution through Brave's privacy-first index, hybrid retrieval stack, and the Search API Nathan Labenz now wants his own product team to use.
1:06:40ENJanie Lee on Product Judgment, Hiring and Enterprise ExpansionInvitado: Janie Lee · Presenta: Harry StebbingsThe Loom product leader discusses customer problems, clear writing, product reviews and the shift from self-serve adoption to enterprise sales.
2:51:58ENSara Walker: Life Is Huge in Time, Not in SpaceInvitado: Sara Walker · Presenta: Lex FridmanThe astrobiologist tells Lex Fridman why textbook definitions of life fail, how assembly theory measures complexity, and why aliens might disappear into their own causal structure.
11:28ENOCaml Local Types: Regional Parameters and Loop RegionsRichard Eisenberg · Jane StreetA demonstration explains why some local parameters can cross a function boundary and how loop regions reclaim allocations.
1:01:30ENLeopold Aschenbrenner’s 2024 Forecast for AGI, Superintelligence and SecurityMatthew Berman · Forward FutureMatthew Berman examines Situational Awareness, its extrapolation to automated AI research, and the industrial, geopolitical and alignment challenges it envisions.
52:45ENA $150 million model, $5 million six months later: Benchmark’s AI investment mathPresenta: Nathan LabenzEric Vishria and Quilter’s Sergiy Nesterenko explain why PCB layout is not a Copilot problem, why human board data would be the wrong training set, and which AI companies look like sugar highs.
1:06:04ENAlexandr Wang on AI Data, Model Development and Building ScaleInvitado: Alexandr Wang · Presenta: Harry StebbingsScale’s founder discusses his data-bottleneck thesis, enterprise adoption, AI security and the challenges of growing a team.
35:24ENScott Belsky: Models Are Commoditizing. Controls, Data, and Taste Are the MoatPresenta: Jacob EffronAdobe’s chief product officer tells Unsupervised Learning why Project Neo leaking into Firefly, a three-second Premiere extend, and Superhuman-priced software all point to the same shift.
26:00ENWhy a Zapier founder thinks AGI research has been stalled for four yearsPresenta: Sarah Guo, Elad GilMike Knoop and François Chollet put more than a million dollars on beating ARC-AGI, the eval they say actually measures general intelligence.
35:27ENApple Intelligence at WWDC 2024: Siri, Privacy and ChatGPTMatthew Berman · Forward FutureMatthew Berman examines Apple’s 2024 AI announcements, emphasizing cross-app actions, on-device processing, Private Cloud Compute and the ChatGPT integration.
1:25:42ENReid Hoffman on AI Models, Inflection, Governance and Technology’s Social ImpactInvitado: Reid Hoffman · Presenta: Harry StebbingsHoffman discusses the Microsoft–Inflection arrangement, AI competition and regulation, and his political views in a time-specific 2024 interview.
4:01:26ENAndrej Karpathy: Reproducing GPT-2 from ScratchAndrej KarpathyBuild the 124-million-parameter GPT-2, accelerate its training, and evaluate the resulting model with validation loss and HellaSwag.
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