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Halluminate is building reinforcement learning environments and benchmarks to help AI models do knowledge work beyond coding, starting with finance. With a team of fewer than 10 people, the company works with four of the top five closed-source US AI labs and recently raised a $30M Series A. In...

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Y Combinator

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258,252 görüntüleme • 4 gün önce •via X (Twitter)

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ShadowAguy profil fotoğrafı
ShadowAguy4 gün önce

finance is where every model discovers its spreadsheet is hallucinating.

Dragonbyte🐉🦁🍀 profil fotoğrafı
Dragonbyte🐉🦁🍀4 gün önce

The important leap is not just training models on harder domains. It is giving them environments where decisions can be tested, mistakes become data, and progress is measurable. That is how AI moves from impressive demos to dependable work.

Ted | unfair.so profil fotoğrafı
Ted | unfair.so4 gün önce

verification before scale ✅

The SI Therapist profil fotoğrafı
The SI Therapist4 gün önce

AI models handle finance data well too. RL environments help them learn complex rules quickly without much human effort. It works for me too.

Jerry Wu profil fotoğrafı
Jerry Wu4 gün önce

Thanks for having us! We wouldn't be here without the whole @ycombinator team.

Emir Çitak profil fotoğrafı
Emir Çitak4 gün önce

The pivot from evals to training environments is a good example for founders. They tested browser agents, saw what was missing, and then built that missing thing. Good ideas often come from the work you were already doing.

Tyler | Crypto Whale profil fotoğrafı
Tyler | Crypto Whale4 gün önce

Less than 10 people, four of the top five labs, $30M in. They're selling shovels and the gold rush just started.

Eden Bitton profil fotoğrafı
Eden Bitton4 gün önce

Finance is a smart first market because a model either ties out or it doesn't. Marketing will be the hard one. Half the job there is agreeing on what "worked" even means.

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Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses
1:14:20

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses

Mario Gabriele 🦊

27,871 görüntüleme • 6 ay önce

Finance is one of the hardest environments to build AI for because precision and trust matter so much. Gabriel Stengel, John Willett, Tumas Rackaitis, and team have built Rogo with that reality in mind: a platform that fits the way bankers already work and delivers the auditability, reliability, and workflow depth that leading financial firms require. Thrilled to lead Rogo’s Series D and excited to partner with the team.
3:03

Finance is one of the hardest environments to build AI for because precision and trust matter so much. Gabriel Stengel, John Willett, Tumas Rackaitis, and team have built Rogo with that reality in mind: a platform that fits the way bankers already work and delivers the auditability, reliability, and workflow depth that leading financial firms require. Thrilled to lead Rogo’s Series D and excited to partner with the team.

Mamoon Hamid

26,927 görüntüleme • 5 ay önce

When Mudith Jayasekara and I met Gabe Pereyra, we were expecting just another vanilla intro call and instead had the best yarn about research, the state of LLMs, and where intelligence is actually heading. It's rare to meet a founder this deep in the weeds who's also building for one of the most important verticals in this new age of intelligence So it was awesome to sit down with Gabe for an extended discussion on what it take to build agents that can reliably complete work over hours, days, or even longer? We talked about why agents today struggle with search and long context windows and how techniques like KV-cache compaction, synthetic data, and continual learning could help. 0:00 Introduction 0:36 Getting legal agents to review the whole data room 2:08 Data rooms larger than any context window 5:28 How far open-source models can go 7:58 Where specialist models fit in legal AI 10:59 Training legal models when client data is off-limits 13:06 Teaching a model how a law firm works 13:59 What belongs in context vs. model weights 15:36 From firm-wide AI to a model for every lawyer 18:37 What training adds beyond retrieving the right cases 20:26 Why context windows have plateaued 24:01 How models could learn continuously on the job 26:12 Can AI recursively improve AI research? 27:07 Research agents can run experiments but not choose them 30:00 Why open-ended research is hard to train 33:47 Why deployment, not intelligence, is the bottleneck 35:08 The cost of frontier intelligence 36:59 Different neolabs, different paths to intelligence 39:26 Using open datasets to compare research methods 41:13 Conclusion
41:50

When Mudith Jayasekara and I met Gabe Pereyra, we were expecting just another vanilla intro call and instead had the best yarn about research, the state of LLMs, and where intelligence is actually heading. It's rare to meet a founder this deep in the weeds who's also building for one of the most important verticals in this new age of intelligence So it was awesome to sit down with Gabe for an extended discussion on what it take to build agents that can reliably complete work over hours, days, or even longer? We talked about why agents today struggle with search and long context windows and how techniques like KV-cache compaction, synthetic data, and continual learning could help. 0:00 Introduction 0:36 Getting legal agents to review the whole data room 2:08 Data rooms larger than any context window 5:28 How far open-source models can go 7:58 Where specialist models fit in legal AI 10:59 Training legal models when client data is off-limits 13:06 Teaching a model how a law firm works 13:59 What belongs in context vs. model weights 15:36 From firm-wide AI to a model for every lawyer 18:37 What training adds beyond retrieving the right cases 20:26 Why context windows have plateaued 24:01 How models could learn continuously on the job 26:12 Can AI recursively improve AI research? 27:07 Research agents can run experiments but not choose them 30:00 Why open-ended research is hard to train 33:47 Why deployment, not intelligence, is the bottleneck 35:08 The cost of frontier intelligence 36:59 Different neolabs, different paths to intelligence 39:26 Using open datasets to compare research methods 41:13 Conclusion

Charlie O'Neill

93,422 görüntüleme • 2 ay önce

Factory CEO on why AGI is already here Matan Grinberg is the co-founder and CEO of Factory, a $5 billion AI coding startup building Droid agents to automate the engineering process. He tells me how AI is changing the job of a software engineer and why Factory is betting on a future with many competing models. We get into how it balances model performance with token costs and why he thinks companies should avoid depending on one AI provider. We also discuss why he thinks AGI is already here, how Factory hires its own engineers, his view of the open-versus-closed AI debate, competition with companies like Cognition, and when companies should outsource to AI rather than build in-house. Timestamps: 00:00 The AI coding shift 06:10 What makes Factory different 11:32 Funding Factory’s growth 16:17 When AI writes the code 20:25 Hiring in the AI era 28:30 Betting on multiple models 32:49 Is AGI already here? 34:30 AI safety and regulation 39:36 Building a trusted AI business 41:32 The risks of model dependence 44:50 Competition and engineering’s future Links: YouTube: Spotify: Substack: Thanks to the show’s premiere sponsors: Atlassian, Granola, and Mercury.
50:15

Factory CEO on why AGI is already here Matan Grinberg is the co-founder and CEO of Factory, a $5 billion AI coding startup building Droid agents to automate the engineering process. He tells me how AI is changing the job of a software engineer and why Factory is betting on a future with many competing models. We get into how it balances model performance with token costs and why he thinks companies should avoid depending on one AI provider. We also discuss why he thinks AGI is already here, how Factory hires its own engineers, his view of the open-versus-closed AI debate, competition with companies like Cognition, and when companies should outsource to AI rather than build in-house. Timestamps: 00:00 The AI coding shift 06:10 What makes Factory different 11:32 Funding Factory’s growth 16:17 When AI writes the code 20:25 Hiring in the AI era 28:30 Betting on multiple models 32:49 Is AGI already here? 34:30 AI safety and regulation 39:36 Building a trusted AI business 41:32 The risks of model dependence 44:50 Competition and engineering’s future Links: YouTube: Spotify: Substack: Thanks to the show’s premiere sponsors: Atlassian, Granola, and Mercury.

Alex Heath

62,099 görüntüleme • 6 gün önce

Most people experience AI through apps. Those apps run on infrastructure owned by a handful of companies. Ben Fielding and Harry Grieve of gensyn on why that needs to change, and what it's going to take to rebuild AI as crypto-native open infrastructure. 00:00 – Intro 00:29 – Misconceptions about AI infrastructure 01:20 – Why centralization in AI is a deeper problem than people realize 04:19 – Why AI needs crypto 05:51 – How AI models are trained 08:15 – The rise of autonomous AI agents with onchain identities 10:37 – Lightning round
17:23

Most people experience AI through apps. Those apps run on infrastructure owned by a handful of companies. Ben Fielding and Harry Grieve of gensyn on why that needs to change, and what it's going to take to rebuild AI as crypto-native open infrastructure. 00:00 – Intro 00:29 – Misconceptions about AI infrastructure 01:20 – Why centralization in AI is a deeper problem than people realize 04:19 – Why AI needs crypto 05:51 – How AI models are trained 08:15 – The rise of autonomous AI agents with onchain identities 10:37 – Lightning round

a16z crypto

24,317 görüntüleme • 5 ay önce

Baseten’s CEO on why every company will want to own its AI / Tuhin Srivastava on the rise of AI agents, the demand for inference, and building a new kind of hyperscaler. Lately, I’ve been spending a lot of time thinking about the AI inference market and how big it could get. Agents like Muse, Instinct, Town, and Grok Bot aggressively use browsers, run their own computers, and burn through far more tokens than a traditional chatbot. As more people put them to work (Muse is number two in the App Store), the demand for inference, or the computing needed to run these models, should grow enormously. Tuhin and I discuss the rise of agents using browsers and virtual machines to get things done, and Baseten's recent acquisition to help power that shift. We also talk about the data center backlash, why companies are embracing Chinese open models, his plans for Baseten’s new research lab, and why he thinks inference becomes the only market left after AGI. Timestamps: 00:00 What Is AI Inference? 06:05 Competing With the Cloud Giants 09:24 Why Companies Want to Own Their AI 15:30 Building Baseten Before the AI Boom 23:45 DeepSeek and the Race for Open AI Models 29:32 Baseten’s Growth and Expansion 33:05 AI Agents and the Blaxel Acquisition 38:32 Data Centers and the AI Backlash 43:25 What Happens to Inference After AGI? 45:12 When AI Agents Become Customers Thanks to the show's premier sponsors: Atlassian, Granola, and Mercury.
48:16

Baseten’s CEO on why every company will want to own its AI / Tuhin Srivastava on the rise of AI agents, the demand for inference, and building a new kind of hyperscaler. Lately, I’ve been spending a lot of time thinking about the AI inference market and how big it could get. Agents like Muse, Instinct, Town, and Grok Bot aggressively use browsers, run their own computers, and burn through far more tokens than a traditional chatbot. As more people put them to work (Muse is number two in the App Store), the demand for inference, or the computing needed to run these models, should grow enormously. Tuhin and I discuss the rise of agents using browsers and virtual machines to get things done, and Baseten's recent acquisition to help power that shift. We also talk about the data center backlash, why companies are embracing Chinese open models, his plans for Baseten’s new research lab, and why he thinks inference becomes the only market left after AGI. Timestamps: 00:00 What Is AI Inference? 06:05 Competing With the Cloud Giants 09:24 Why Companies Want to Own Their AI 15:30 Building Baseten Before the AI Boom 23:45 DeepSeek and the Race for Open AI Models 29:32 Baseten’s Growth and Expansion 33:05 AI Agents and the Blaxel Acquisition 38:32 Data Centers and the AI Backlash 43:25 What Happens to Inference After AGI? 45:12 When AI Agents Become Customers Thanks to the show's premier sponsors: Atlassian, Granola, and Mercury.

Alex Heath

26,164 görüntüleme • 20 gün önce