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"A system that can literally engineer matter is going to be of key importance to everybody." Liam Fedus and Ekin Dogus Cubuk are the co-founders of Periodic Labs, a San Francisco-based startup building AI systems and autonomous laboratories to discover new materials, starting with a higher-temperature superconductor. Fedus previously...

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Mario Gabriele

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32,564 görüntüleme • 13 gün önce •via X (Twitter)

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@LiamFedus @ekindogus @periodiclabs Love this! When was this podcast/interview filmed?

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1/ ChatGPT had the internet. AI for science has no equivalent. Liam Fedus helped create ChatGPT and went on to lead post-training at OpenAI. Now he’s building Periodic Labs because better models alone won’t get us to an AI scientist. We talked about why Periodic is building physical labs and what Liam learned from being inside OpenAI as ChatGPT took off on today's episode of The Frontier.
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1/ ChatGPT had the internet. AI for science has no equivalent. Liam Fedus helped create ChatGPT and went on to lead post-training at OpenAI. Now he’s building Periodic Labs because better models alone won’t get us to an AI scientist. We talked about why Periodic is building physical labs and what Liam learned from being inside OpenAI as ChatGPT took off on today's episode of The Frontier.

Brendan (can/do)

25,603 görüntüleme • 2 gün önce

🚀 Big milestone: We raised a $235M Series A, co-led by Braidwell and Collective Global! Our team is excited for this next chapter in building the world’s first scientific superintelligence platform to accelerate breakthroughs in life, chemical, and materials sciences. 📣 If the idea of building scientific superintelligence excites you, come build with us. We’re seeking extraordinary, driven people across AI research, software, robotics, biology, chemistry, materials science, and operations. #ScientificSuperintelligence #AI #Innovation
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🚀 Big milestone: We raised a $235M Series A, co-led by Braidwell and Collective Global! Our team is excited for this next chapter in building the world’s first scientific superintelligence platform to accelerate breakthroughs in life, chemical, and materials sciences. 📣 If the idea of building scientific superintelligence excites you, come build with us. We’re seeking extraordinary, driven people across AI research, software, robotics, biology, chemistry, materials science, and operations. #ScientificSuperintelligence #AI #Innovation

Lila Sciences

43,160 görüntüleme • 1 yıl önce

the legends at Soon and I have been on a mission to find the most jacked AI researcher and we think that Liam Fedus, co-creator of ChatGPT and co-founder of Periodic Labs might be it. check out the latest episode of Swole as a Service where he totally framemogs me while discussing whether or not AI will replace scientists 00:21 Who is Liam Fedus? 01:21 Lying skull crushers & will AI replace scientists? 03:05 Barbell curls, Jayden’s fitness tips, & why build an AI scientist now? 05:17 Hammer curls & is the singularity imminent? 07:43 Sleds & is the permanent underclass meme real? 08:37 Chestmaxxer Challenge & why hasn’t AI cured all diseases?
10:46

the legends at Soon and I have been on a mission to find the most jacked AI researcher and we think that Liam Fedus, co-creator of ChatGPT and co-founder of Periodic Labs might be it. check out the latest episode of Swole as a Service where he totally framemogs me while discussing whether or not AI will replace scientists 00:21 Who is Liam Fedus? 01:21 Lying skull crushers & will AI replace scientists? 03:05 Barbell curls, Jayden’s fitness tips, & why build an AI scientist now? 05:17 Hammer curls & is the singularity imminent? 07:43 Sleds & is the permanent underclass meme real? 08:37 Chestmaxxer Challenge & why hasn’t AI cured all diseases?

atlas

162,204 görüntüleme • 6 ay önce

Nobel laureate John Jumper (John Jumper) on AlphaFold and the future of science. At AI Startup School in San Francisco. 01:02 - Journey from Physics to Computational Biology 02:01 - Transition to Machine Learning and AI 02:59 - Joining Google DeepMind 03:47 - The Mission: Accelerating Scientific Discovery 04:54 - Understanding Protein Structures 07:44 - Challenges in Protein Structure Determination 11:21 - The Role of Data, Compute, and Research 13:28 - AlphaFold: A Breakthrough in Protein Prediction 14:09 - The Importance of Research and Ideas 17:48 - Real-World Applications and Impact 18:02 - Open Sourcing AlphaFold 21:07 - Unexpected Uses and Innovations 23:15 - Case Study: Molecular Syringe 25:28 - Future of AI in Science
27:12

Nobel laureate John Jumper (John Jumper) on AlphaFold and the future of science. At AI Startup School in San Francisco. 01:02 - Journey from Physics to Computational Biology 02:01 - Transition to Machine Learning and AI 02:59 - Joining Google DeepMind 03:47 - The Mission: Accelerating Scientific Discovery 04:54 - Understanding Protein Structures 07:44 - Challenges in Protein Structure Determination 11:21 - The Role of Data, Compute, and Research 13:28 - AlphaFold: A Breakthrough in Protein Prediction 14:09 - The Importance of Research and Ideas 17:48 - Real-World Applications and Impact 18:02 - Open Sourcing AlphaFold 21:07 - Unexpected Uses and Innovations 23:15 - Case Study: Molecular Syringe 25:28 - Future of AI in Science

Y Combinator

150,364 görüntüleme • 1 yıl önce

Dust ( helps companies build AI teammates that work across their organization. Founded by former OpenAI researcher Stanislas Polu (Stanislas Polu) and his team, the company has spent the last three years building AI for work while the capabilities of frontier models have advanced at breakneck speed. At Startup School Paris, YC's Mathilde Collin sat down with Stanislas to talk about what it takes to build alongside frontier AI labs, why staying model agnostic is a competitive advantage, how AI is changing the way we work, and why the best startups are built around a problem founders can't stop thinking about. 01:09 — From Stripe to OpenAI to Founder 01:40 — Why Stan Left OpenAI 02:49 — What Dust Actually Does 03:46 — What Work Looks Like in 3 Years 04:53 — Where the Thesis Was Right (and Wrong) 07:05 — Building Alongside Frontier Labs 09:15 — Model Agnostic as a Moat 10:49 — How Frontier Labs Changed Fundraising 12:19 — Raising Small on Purpose 14:39 — What Mistral Got Right 15:11 — Building in France 16:37 — What Happens When Frontier Labs Enter Your Market? 18:27 — The Margin Problem
22:52

Dust ( helps companies build AI teammates that work across their organization. Founded by former OpenAI researcher Stanislas Polu (Stanislas Polu) and his team, the company has spent the last three years building AI for work while the capabilities of frontier models have advanced at breakneck speed. At Startup School Paris, YC's Mathilde Collin sat down with Stanislas to talk about what it takes to build alongside frontier AI labs, why staying model agnostic is a competitive advantage, how AI is changing the way we work, and why the best startups are built around a problem founders can't stop thinking about. 01:09 — From Stripe to OpenAI to Founder 01:40 — Why Stan Left OpenAI 02:49 — What Dust Actually Does 03:46 — What Work Looks Like in 3 Years 04:53 — Where the Thesis Was Right (and Wrong) 07:05 — Building Alongside Frontier Labs 09:15 — Model Agnostic as a Moat 10:49 — How Frontier Labs Changed Fundraising 12:19 — Raising Small on Purpose 14:39 — What Mistral Got Right 15:11 — Building in France 16:37 — What Happens When Frontier Labs Enter Your Market? 18:27 — The Margin Problem

Y Combinator

54,342 görüntüleme • 2 ay önce

OpenAI DevDay Talk: Building AI for the Enterprise with Decagon and Clay I sat down with the founders of Decagon and Clay at OpenAI DevDay to chat about what it actually takes to build and sell AI to the enterprise. We discuss how to make product decisions against a rapidly evolving model landscape, ensure AI pilots are successful, break out in seemingly crowded markets, and more. 00:00 Intro 01:54 Decagon and Clay's founding stories 05:12 Building against an evolving model landscape 08:36 AI experimentation vs risk management 11:25 Ensuring AI pilots are successful 14:52 Breaking out in noisy markets 19:11 Going deep in verticals vs going broad 21:13 Where to invest time + money 23:17 Top advice for founders Decagon Clay Jesse Zhang Varun Anand OpenAI OpenAIDevs
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OpenAI DevDay Talk: Building AI for the Enterprise with Decagon and Clay I sat down with the founders of Decagon and Clay at OpenAI DevDay to chat about what it actually takes to build and sell AI to the enterprise. We discuss how to make product decisions against a rapidly evolving model landscape, ensure AI pilots are successful, break out in seemingly crowded markets, and more. 00:00 Intro 01:54 Decagon and Clay's founding stories 05:12 Building against an evolving model landscape 08:36 AI experimentation vs risk management 11:25 Ensuring AI pilots are successful 14:52 Breaking out in noisy markets 19:11 Going deep in verticals vs going broad 21:13 Where to invest time + money 23:17 Top advice for founders Decagon Clay Jesse Zhang Varun Anand OpenAI OpenAIDevs

Kimberly Tan

13,901 görüntüleme • 1 yıl önce

Bob McGrew (Bob McGrew) helped build some of the most influential technologies of the past two decades. He was an early engineer at PayPal, an early executive at Palantir—where he helped pioneer the Forward Deployed Engineer (FDE) model— and was recently Chief Research Officer at OpenAI - where he led the development of ChatGPT, GPT-4, and o1. On this episode of Lightcone Podcast, he explains how FDEs became central to today's startups, why "doing things that don't scale at scale" works, and where he sees the biggest opportunities for founders working in AI. 00:29 – From PayPal to Palantir to OpenAI 02:19 – The Role of a Forward Deployed Engineer 03:19 – How Palantir Invented It 07:56 – Product Discovery in the Field vs. Sales 09:51 – Echo and Delta Teams Explained 13:34 – Training Ground for Founders 14:35 – Consulting or Real Software? 17:54 – The Birth of Palantir’s Ontology 23:04 – Why AI Companies Adopt It 36:17 – What Success Metrics Look Like 41:14 – Building with Demo-Driven Development 44:56 – Joining the US Army Reserve 47:43 – Opportunities for Founders
50:42

Bob McGrew (Bob McGrew) helped build some of the most influential technologies of the past two decades. He was an early engineer at PayPal, an early executive at Palantir—where he helped pioneer the Forward Deployed Engineer (FDE) model— and was recently Chief Research Officer at OpenAI - where he led the development of ChatGPT, GPT-4, and o1. On this episode of Lightcone Podcast, he explains how FDEs became central to today's startups, why "doing things that don't scale at scale" works, and where he sees the biggest opportunities for founders working in AI. 00:29 – From PayPal to Palantir to OpenAI 02:19 – The Role of a Forward Deployed Engineer 03:19 – How Palantir Invented It 07:56 – Product Discovery in the Field vs. Sales 09:51 – Echo and Delta Teams Explained 13:34 – Training Ground for Founders 14:35 – Consulting or Real Software? 17:54 – The Birth of Palantir’s Ontology 23:04 – Why AI Companies Adopt It 36:17 – What Success Metrics Look Like 41:14 – Building with Demo-Driven Development 44:56 – Joining the US Army Reserve 47:43 – Opportunities for Founders

Y Combinator

409,631 görüntüleme • 1 yıl önce

What actually goes into a data center? Guru Chahal🇺🇸 whiteboards the entire stack - across land, power, cooling, storage, networking, compute - and shares where we're seeing the most interesting innovation + startup opportunities → Base Power: virtual power plants from residential batteries → Periodic Labs: using AI to discover new materials - ones that run cooler, conduct better, and waste less power → Nexthop AI: network switches that are 10–15% more power efficient than what's on the market → Unconventional AI: AI chips modeled after how the human brain works, targeting 100–1,000x better power efficiency → Ricursive Intelligence: AI to design chips. Today a first chip costs $100M and takes 2–3 years. They want to build in months, at a fraction of the cost → better materials → cheaper experiments → more chip designs → more efficient chips → less power → and what happens when you package all of this into a satellite and put it in orbit 🚀 As trillions of dollars of infrastructure flow into building the "AI factory", every layer of the intelligence economy is being rebuilt at once. Who are we missing? We'd love to meet if you are building somewhere on this stack.
11:20

What actually goes into a data center? Guru Chahal🇺🇸 whiteboards the entire stack - across land, power, cooling, storage, networking, compute - and shares where we're seeing the most interesting innovation + startup opportunities → Base Power: virtual power plants from residential batteries → Periodic Labs: using AI to discover new materials - ones that run cooler, conduct better, and waste less power → Nexthop AI: network switches that are 10–15% more power efficient than what's on the market → Unconventional AI: AI chips modeled after how the human brain works, targeting 100–1,000x better power efficiency → Ricursive Intelligence: AI to design chips. Today a first chip costs $100M and takes 2–3 years. They want to build in months, at a fraction of the cost → better materials → cheaper experiments → more chip designs → more efficient chips → less power → and what happens when you package all of this into a satellite and put it in orbit 🚀 As trillions of dollars of infrastructure flow into building the "AI factory", every layer of the intelligence economy is being rebuilt at once. Who are we missing? We'd love to meet if you are building somewhere on this stack.

Claire Zau

50,785 görüntüleme • 2 ay önce

Dianne Penn is Head of Product for Research and Labs at Anthropic. She joined in 2023 as their first technical PM (when there were just five product engineers) and has helped ship every Claude model from Claude 2 to Mythos. We discuss: 02:31 Early Anthropic 08:55 The two biggest inflections 13:50 Inside the exponential 20:02 Token maxing 23:30 Anthropic Labs and the incubation model 27:30 How the AI research role works 31:35 How to become a top AI researcher 35:18 Frontier model safeguards 39:38 Hiring in the AI era 44:16 Building evals 47:48 Evals are the new PRDs 49:55 The importance of hands-on leadership 52:46 Finding joy in AI 58:10 How Dianne uses Claude 01:01:05 Avoiding overreliance on AI 01:03:50 The constitution that makes Claude better 01:07:11 AI writing and verification 01:11:40 Where human brains will continue to be valuable 01:14:10 Navigating AI with kids 01:16:26 Alignment, the future of the PM role, and burnout 01:21:54 Lightning round and final thoughts
1:33:50

Dianne Penn is Head of Product for Research and Labs at Anthropic. She joined in 2023 as their first technical PM (when there were just five product engineers) and has helped ship every Claude model from Claude 2 to Mythos. We discuss: 02:31 Early Anthropic 08:55 The two biggest inflections 13:50 Inside the exponential 20:02 Token maxing 23:30 Anthropic Labs and the incubation model 27:30 How the AI research role works 31:35 How to become a top AI researcher 35:18 Frontier model safeguards 39:38 Hiring in the AI era 44:16 Building evals 47:48 Evals are the new PRDs 49:55 The importance of hands-on leadership 52:46 Finding joy in AI 58:10 How Dianne uses Claude 01:01:05 Avoiding overreliance on AI 01:03:50 The constitution that makes Claude better 01:07:11 AI writing and verification 01:11:40 Where human brains will continue to be valuable 01:14:10 Navigating AI with kids 01:16:26 Alignment, the future of the PM role, and burnout 01:21:54 Lightning round and final thoughts

Lenny Rachitsky

158,387 görüntüleme • 2 ay önce

The interview with Tibo from OpenAI 🔥 0:00 Everything OpenAI launched at DevDay 2:44 Sponsors: Wispr Flow, Hostinger, OpenArt 4:11 What is ChatGPT Dots? 7:52 Will ChatGPT Dots be free? 10:15 GPT-6.1 Sol vs GPT-6 Astra 11:44 Is AI now building AI? 12:53 Ultra Fast: when does everyone get it? 14:00 How OpenAI rebuilt the ChatGPT desktop app in 30 days 19:08 Why everything will run in the cloud 20:55 Calling your Dots on the phone 22:11 The new Turing test 24:05 OpenAI's mission and AI safety 26:14 Why OpenAI still works like a startup 27:29 The truth about ChatGPT usage resets 29:00 The $500 ChatGPT plan 30:17 Sponsors in action 32:23 Why AI agents still fail 33:52 OpenAI's AI is now on call 34:26 OpenAI's internal benchmarks 35:55 Open source Codex and the open ecosystem 38:42 ChatGPT Space explained 39:45 Is the chat box holding AI back? 42:26 Every DevDay update, full workshop 43:28 How people really use ChatGPT Work and Codex 44:01 When is the next OpenAI model coming? 45:05 What AI has to solve next 45:32 Is your job safe from AI? 48:14 Rapid fire 48:54 Has OpenAI already reached AGI? 50:48 The one thing he'll never let AI do 51:22 His morning AI routine 52:15 What he'd build if not AI Watch the full podcast on YouTube or X. Enjoy!
54:11

The interview with Tibo from OpenAI 🔥 0:00 Everything OpenAI launched at DevDay 2:44 Sponsors: Wispr Flow, Hostinger, OpenArt 4:11 What is ChatGPT Dots? 7:52 Will ChatGPT Dots be free? 10:15 GPT-6.1 Sol vs GPT-6 Astra 11:44 Is AI now building AI? 12:53 Ultra Fast: when does everyone get it? 14:00 How OpenAI rebuilt the ChatGPT desktop app in 30 days 19:08 Why everything will run in the cloud 20:55 Calling your Dots on the phone 22:11 The new Turing test 24:05 OpenAI's mission and AI safety 26:14 Why OpenAI still works like a startup 27:29 The truth about ChatGPT usage resets 29:00 The $500 ChatGPT plan 30:17 Sponsors in action 32:23 Why AI agents still fail 33:52 OpenAI's AI is now on call 34:26 OpenAI's internal benchmarks 35:55 Open source Codex and the open ecosystem 38:42 ChatGPT Space explained 39:45 Is the chat box holding AI back? 42:26 Every DevDay update, full workshop 43:28 How people really use ChatGPT Work and Codex 44:01 When is the next OpenAI model coming? 45:05 What AI has to solve next 45:32 Is your job safe from AI? 48:14 Rapid fire 48:54 Has OpenAI already reached AGI? 50:48 The one thing he'll never let AI do 51:22 His morning AI routine 52:15 What he'd build if not AI Watch the full podcast on YouTube or X. Enjoy!

Vaibhav Sisinty

495,936 görüntüleme • 11 gün önce

AI is rapidly evolving, and it’s reshaping how and where computing happens. From data center infrastructure to AI systems in the physical world, to the future of personal AI devices, intelligence is being distributed across a new compute landscape. At the edge, more agentic experiences are starting to show up, expanding what devices need to do. Tomorrow in New York, we’ll share what Qualcomm is building across it and how it will drive our next phase of growth. Stay tuned for #QCOMInvestorDay.
0:32

AI is rapidly evolving, and it’s reshaping how and where computing happens. From data center infrastructure to AI systems in the physical world, to the future of personal AI devices, intelligence is being distributed across a new compute landscape. At the edge, more agentic experiences are starting to show up, expanding what devices need to do. Tomorrow in New York, we’ll share what Qualcomm is building across it and how it will drive our next phase of growth. Stay tuned for #QCOMInvestorDay.

Cristiano R. Amon

15,783 görüntüleme • 3 ay önce

This week's guest on Uncapped is Brad Lightcap, COO at OpenAI. We talked about the history of OpenAI, the shift in AI from chat to agents, where new startups can endure, Codex, FDEs, working with Sam, and more. Hope you enjoy! (0:00) Intro (0:39) The early days of OpenAI (3:47) A research centric culture (7:32) Post-ChatGPT chapters (11:54) Sci-Fi future or good software (15:26) AI’s impact on rural communities (18:57) Codex and coding of the future (24:04) Doing a lot of things at once (27:55) What VCs should invest in (35:43) The software sell off (38:23) Using Codex over ChatGPT (42:32) FDEs and Private Equity (44:53) Working with Sam
49:33

This week's guest on Uncapped is Brad Lightcap, COO at OpenAI. We talked about the history of OpenAI, the shift in AI from chat to agents, where new startups can endure, Codex, FDEs, working with Sam, and more. Hope you enjoy! (0:00) Intro (0:39) The early days of OpenAI (3:47) A research centric culture (7:32) Post-ChatGPT chapters (11:54) Sci-Fi future or good software (15:26) AI’s impact on rural communities (18:57) Codex and coding of the future (24:04) Doing a lot of things at once (27:55) What VCs should invest in (35:43) The software sell off (38:23) Using Codex over ChatGPT (42:32) FDEs and Private Equity (44:53) Working with Sam

Jack Altman

120,785 görüntüleme • 6 ay önce

In this special live event, Founders In Arms Podcast brings together a room full of founders at Mercury's San Francisco headquarters for an intimate conversation about building, investing, and the future of AI. Max Mullen is co-founder at Instacart, where he led product and grew it from a contrarian idea into the leader in online grocery delivery and eventually a publicly traded company. Now Max Mullen is an active investor and runs a founder community in San Francisco called Workshop. Max shares lessons from over a decade of building and his perspective on AI's transformative potential. In this episode, we cover: (00:00) Welcome to the first live Founders in Arms event at Mercury HQ (00:43) Introduction to Max Mullen and his early investment in Mercury (03:58) Max's origin story and first startup at Startup Weekend (04:36) Building a social network and getting acquihired (05:39) The path from acquihire to founding Instacart (06:19) Why Max wanted to solve real-world operational problems (07:17) Starting Instacart when there was no gig economy (09:15) Overcoming the "grocery delivery is dead" narrative (11:48) Market timing and why Webvan was too early (14:37) Being a non-technical co-founder in a technical role (17:45) The PARE framework for building company culture (22:09) Max's approach to M&A at Instacart (24:00) The Caper smart shopping cart acquisition story (25:31) How founders can position for acquisition (28:26) Why product-market fit attracts acquirers (29:46) Evaluating investments: market size vs. founder quality (32:54) Max and Immad's differing investment philosophies (35:39) The current state of VC funding and AI companies (36:51) The three best times to raise money as a founder (39:15) Instacart's seasonal business patterns and fundraising timing (39:59) The most interesting opportunities in AI right now (40:24) Why every profession will have AI agent co-pilots (41:44) What AI-native companies will look like for the next generation
42:26

In this special live event, Founders In Arms Podcast brings together a room full of founders at Mercury's San Francisco headquarters for an intimate conversation about building, investing, and the future of AI. Max Mullen is co-founder at Instacart, where he led product and grew it from a contrarian idea into the leader in online grocery delivery and eventually a publicly traded company. Now Max Mullen is an active investor and runs a founder community in San Francisco called Workshop. Max shares lessons from over a decade of building and his perspective on AI's transformative potential. In this episode, we cover: (00:00) Welcome to the first live Founders in Arms event at Mercury HQ (00:43) Introduction to Max Mullen and his early investment in Mercury (03:58) Max's origin story and first startup at Startup Weekend (04:36) Building a social network and getting acquihired (05:39) The path from acquihire to founding Instacart (06:19) Why Max wanted to solve real-world operational problems (07:17) Starting Instacart when there was no gig economy (09:15) Overcoming the "grocery delivery is dead" narrative (11:48) Market timing and why Webvan was too early (14:37) Being a non-technical co-founder in a technical role (17:45) The PARE framework for building company culture (22:09) Max's approach to M&A at Instacart (24:00) The Caper smart shopping cart acquisition story (25:31) How founders can position for acquisition (28:26) Why product-market fit attracts acquirers (29:46) Evaluating investments: market size vs. founder quality (32:54) Max and Immad's differing investment philosophies (35:39) The current state of VC funding and AI companies (36:51) The three best times to raise money as a founder (39:15) Instacart's seasonal business patterns and fundraising timing (39:59) The most interesting opportunities in AI right now (40:24) Why every profession will have AI agent co-pilots (41:44) What AI-native companies will look like for the next generation

Rajat Suri

144,197 görüntüleme • 8 ay önce

Join us at the MIT Media Lab for the ScienceClaw Hackathon, building the internet of agents for science. AI is becoming a collaborator in the real world - designing materials, creating instruments, running experiments, and connecting its capabilities with those of other agents. AI adapts as a problem unfolds - revising its reasoning, learning how to collaborate, and assembling scattered pieces of knowledge, evidence, and raw capability into solutions to some of the hardest challenges in science, technology, and innovation. Teams will connect AI agents, models, simulations, robots, cloud labs, and scientific tools into functioning systems to tackle problems in protein design, robotics, materials, manufacturing, experimental science, and beyond. The challenge is to turn ideas into tested results - and demonstrate how agents collaborating across teams and disciplines can accomplish more together. Explore how agents can specialize, challenge one another’s assumptions, learn from failed experiments, and combine their expertise to solve harder problems. Show how collaboration changes what your system can discover, design, or build. One agent's discovery becomes another's starting point. A tool built by one team enables an experiment by another. Connect your team of AIs, share a capability someone else needs, and build on what others have learned, produced, built. The ambition is an internet of agents through which scientific knowledge, tools, and capabilities can grow, evolve and be utilized across teams and institutions. 🗓️ When: October 30-November 1, 2026 📍 Where: MIT Media Lab Bring your expertise, your tools, and a problem worth solving - or find one. Help build AI that can contribute to science through what it can discover, create, and make work.
0:27

Join us at the MIT Media Lab for the ScienceClaw Hackathon, building the internet of agents for science. AI is becoming a collaborator in the real world - designing materials, creating instruments, running experiments, and connecting its capabilities with those of other agents. AI adapts as a problem unfolds - revising its reasoning, learning how to collaborate, and assembling scattered pieces of knowledge, evidence, and raw capability into solutions to some of the hardest challenges in science, technology, and innovation. Teams will connect AI agents, models, simulations, robots, cloud labs, and scientific tools into functioning systems to tackle problems in protein design, robotics, materials, manufacturing, experimental science, and beyond. The challenge is to turn ideas into tested results - and demonstrate how agents collaborating across teams and disciplines can accomplish more together. Explore how agents can specialize, challenge one another’s assumptions, learn from failed experiments, and combine their expertise to solve harder problems. Show how collaboration changes what your system can discover, design, or build. One agent's discovery becomes another's starting point. A tool built by one team enables an experiment by another. Connect your team of AIs, share a capability someone else needs, and build on what others have learned, produced, built. The ambition is an internet of agents through which scientific knowledge, tools, and capabilities can grow, evolve and be utilized across teams and institutions. 🗓️ When: October 30-November 1, 2026 📍 Where: MIT Media Lab Bring your expertise, your tools, and a problem worth solving - or find one. Help build AI that can contribute to science through what it can discover, create, and make work.

Markus J. Buehler

36,142 görüntüleme • 22 gün önce

Perplexity co-founder and CEO Aravind Srinivas (Aravind Srinivas) on the race to build the AI browser of the future. At AI Startup School in San Francisco. 00:48 - Perplexity's Current Status and Challenges 01:16 - Future Plans: The Browser Bet 03:18 - Competition and Market Dynamics 04:43 - The Importance of Speed and Innovation 05:51 - Founding Perplexity: The Early Days 09:18 - Product Evolution and User Experience 19:44 - Running the Company and Future Outlook 20:15 - AI Tools in Software Engineering 20:31 - Adoption and Implementation of AI Coding Tools 21:11 - Challenges and Benefits of AI in Development 22:35 - Brand Value and Competition 23:56 - Network Effects and Integrations 27:40 - Business Model and Revenue Streams 29:54 - Advice for Founders 31:33 - Audience Q&A
43:12

Perplexity co-founder and CEO Aravind Srinivas (Aravind Srinivas) on the race to build the AI browser of the future. At AI Startup School in San Francisco. 00:48 - Perplexity's Current Status and Challenges 01:16 - Future Plans: The Browser Bet 03:18 - Competition and Market Dynamics 04:43 - The Importance of Speed and Innovation 05:51 - Founding Perplexity: The Early Days 09:18 - Product Evolution and User Experience 19:44 - Running the Company and Future Outlook 20:15 - AI Tools in Software Engineering 20:31 - Adoption and Implementation of AI Coding Tools 21:11 - Challenges and Benefits of AI in Development 22:35 - Brand Value and Competition 23:56 - Network Effects and Integrations 27:40 - Business Model and Revenue Streams 29:54 - Advice for Founders 31:33 - Audience Q&A

Y Combinator

236,501 görüntüleme • 1 yıl önce

Build an assistant that can store and search what it sees, hears, and is told, all without leaving the device. In Building AI Assistants with On-Device Memory, you'll build a local memory system that recalls text, voice, and images, and teach it to recognize something new from a few photos. Built in partnership with Qdrant and taught by Dylan Couzon, Developer Experience Engineer at Qdrant. This course also includes our new AI coding lab, so you can practice building with an AI coding agent from the mobile app. Enroll for free:
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Build an assistant that can store and search what it sees, hears, and is told, all without leaving the device. In Building AI Assistants with On-Device Memory, you'll build a local memory system that recalls text, voice, and images, and teach it to recognize something new from a few photos. Built in partnership with Qdrant and taught by Dylan Couzon, Developer Experience Engineer at Qdrant. This course also includes our new AI coding lab, so you can practice building with an AI coding agent from the mobile app. Enroll for free:

DeepLearning.AI

13,842 görüntüleme • 19 gün önce

Loved the conversation on Uncapped with David George, head of growth investing at a16z. He's an incredible investor and can go deep on a huge range of topics. We talked all about investing at this point in the AI cycle and we covered a lot of ground -- frontier vs. open source, what could make it all keep running vs. slow down, application vs. infra, robotics and autonomy, narrative and vibes, and lots more. Hope you enjoy! Timestamps: (0:00) Intro (0:23) The answer to every AI question is "and" (1:27) Where we are in the AI buildout (4:55) What would stop the train (7:16) Sellers vs. buyers of tokens (10:39) Frontier labs vs. open source (13:18) App investing and the coding blast radius (14:01) Legal as a category (20:46) Consumer AI (22:14) From reactive to proactive (25:52) AI, autonomy, robotics, bio health (27:49) Autonomous driving and why it's underappreciated (30:46) Robotics and what comes next (33:25) Benchmark's new growth fund (34:11) Why growth investing is compelling now (34:53) Half of private market returns happen at growth (37:57) Product cycle vs. capital cycle (41:54) The importance of narrative (43:14) Why vibes really matter (46:20) Never short a messianic founder (49:39) How big can Andreessen Horowitz get
53:11

Loved the conversation on Uncapped with David George, head of growth investing at a16z. He's an incredible investor and can go deep on a huge range of topics. We talked all about investing at this point in the AI cycle and we covered a lot of ground -- frontier vs. open source, what could make it all keep running vs. slow down, application vs. infra, robotics and autonomy, narrative and vibes, and lots more. Hope you enjoy! Timestamps: (0:00) Intro (0:23) The answer to every AI question is "and" (1:27) Where we are in the AI buildout (4:55) What would stop the train (7:16) Sellers vs. buyers of tokens (10:39) Frontier labs vs. open source (13:18) App investing and the coding blast radius (14:01) Legal as a category (20:46) Consumer AI (22:14) From reactive to proactive (25:52) AI, autonomy, robotics, bio health (27:49) Autonomous driving and why it's underappreciated (30:46) Robotics and what comes next (33:25) Benchmark's new growth fund (34:11) Why growth investing is compelling now (34:53) Half of private market returns happen at growth (37:57) Product cycle vs. capital cycle (41:54) The importance of narrative (43:14) Why vibes really matter (46:20) Never short a messianic founder (49:39) How big can Andreessen Horowitz get

Jack Altman

57,444 görüntüleme • 11 gün önce

Periodic Labs cofounder Liam Fedus on why so many physicists are working on AI: "I think [physics is] a great way to think about the world. It's very principled, very hard-nosed scientists, very careful. And I think it's just such an incredible field. You have such high leverage in computer science, in AI. And so I think a lot of physicists were seeing that." "After the discovery of the Higgs, I think a lot of high energy physicists were sort of looking for what's next." "Ultimately it becomes bottlenecked on the new apparatus for pushing the next energy frontier." "And I think a lot of physicists were looking at their skillset and looking at the progress elsewhere and saying like, hey, I think I could be a huge contributor elsewhere." Liam Fedus on No Priors with Elad Gil
0:58

Periodic Labs cofounder Liam Fedus on why so many physicists are working on AI: "I think [physics is] a great way to think about the world. It's very principled, very hard-nosed scientists, very careful. And I think it's just such an incredible field. You have such high leverage in computer science, in AI. And so I think a lot of physicists were seeing that." "After the discovery of the Higgs, I think a lot of high energy physicists were sort of looking for what's next." "Ultimately it becomes bottlenecked on the new apparatus for pushing the next energy frontier." "And I think a lot of physicists were looking at their skillset and looking at the progress elsewhere and saying like, hey, I think I could be a huge contributor elsewhere." Liam Fedus on No Priors with Elad Gil

a16z

41,427 görüntüleme • 6 ay önce