Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Meet… Arena Conversations. Peter Gostev sits down with Poolside researchers,Connor Adams and Aalhad Patankar, to discuss how they’re building frontier open coding models. Episode drops today at 10am PT on our YouTube. They dig into Poolside's Laguna model family, why the team publishes full trajectories (not just benchmark scores)... show more

Arena.ai

232,022 subscribers

39,939 views • 1 month ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

4 Comments

Arena.ai1 month ago

Find Aalhad Patankar at @aalSonOfRavi, and Connor Adams at @ConnorBAdams

Shman1 month ago

@petergostev @poolsideai Apple podcast link?

Hussain Hashim | Building SundayBack1 month ago

@petergostev @poolsideai @arena curious to see how Laguna differs from current coders. Will tune in.

Gill1 month ago

@petergostev @poolsideai Listening to them talk about full trajectories is way more useful than benchmark charts.

Related Videos

Arena Conversations: Turning tens of thousands of experiments across data mixes, into models that move the frontier. Peter Gostev (SF 24-28 August) talks with two researchers Connor Adams and Aalhad Patankar from Poolside about how they learn quickly through iteration, massive pre-training runs, RL decisions, and more. Watch the full conversation on our YouTube. Find the link below.

Arena Conversations: Turning tens of thousands of experiments across data mixes, into models that move the frontier. Peter Gostev (SF 24-28 August) talks with two researchers Connor Adams and Aalhad Patankar from Poolside about how they learn quickly through iteration, massive pre-training runs, RL decisions, and more. Watch the full conversation on our YouTube. Find the link below.

Arena.ai

14,068 views • 1 month ago

Arena Conversations: Training Laguna with Poolside researchers, Connor Adams and Aalhad Patankar. With Peter Gostev, they dig into pre-training that builds broad capability, and post-training to reveal whether those skills actually work together across 250,000 long-horizon trajectories. Check out the full conversation on our YouTube to hear how Poolside is pairing humans, agents, and evidence-annotated reviews to sharpen future training loops. Link below.

Arena Conversations: Training Laguna with Poolside researchers, Connor Adams and Aalhad Patankar. With Peter Gostev, they dig into pre-training that builds broad capability, and post-training to reveal whether those skills actually work together across 250,000 long-horizon trajectories. Check out the full conversation on our YouTube to hear how Poolside is pairing humans, agents, and evidence-annotated reviews to sharpen future training loops. Link below.

Arena.ai

16,470 views • 1 month ago

Arena Conversations: Where’s the line between resourceful agent behavior and reward hacking? Poolside researchers, Connor Adams and Aalhad Patankar, discuss benchmark awareness, instruction following, and where persistence starts to look like misalignment. And to hear about Peter Gostev (SF 24-28 August)'s agent sending an email on his behalf without request… Check out the full interview below.

Arena Conversations: Where’s the line between resourceful agent behavior and reward hacking? Poolside researchers, Connor Adams and Aalhad Patankar, discuss benchmark awareness, instruction following, and where persistence starts to look like misalignment. And to hear about Peter Gostev (SF 24-28 August)'s agent sending an email on his behalf without request… Check out the full interview below.

Arena.ai

14,793 views • 1 month ago

Arena Conversations: Peter Gostev sat down with Matt – Trigger.dev, founder and CEO of Trigger.dev, to discuss building infrastructure for long-running agents, including retries, API reliability, and why customers are switching frontier models faster than ever. Watch the full episode at the link below.

Arena Conversations: Peter Gostev sat down with Matt – Trigger.dev, founder and CEO of Trigger.dev, to discuss building infrastructure for long-running agents, including retries, API reliability, and why customers are switching frontier models faster than ever. Watch the full episode at the link below.

Arena.ai

16,619 views • 25 days ago

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.

Base Labs

243,728 views • 1 month ago

Big day for American open-source AI. For the launch of Laguna S, I sat down with Eiso Kant to discuss its architecture, the economics of open weights, and the question of who gets to build intelligence. Timestamps: 0:00 Intro 1:50 Why Poolside started opening its models: the oligopoly on intelligence 4:28 Getting nerd-sniped by Karpathy, building LLMs before anyone cared 11:20 Laguna S: 118B parameters, 8B active, built in 8 weeks 13:05 The future of software engineering: behaviors over IQ 14:25 Sliding window attention, 1M context, the model factory 15:50 Being an American open-source lab 20:47 The economics of open weights 24:22 Who gets to build intelligence? The 12–18 month window 30:28 Erdős 397 in 30 minutes 31:48 Extracting transcripts with a debugger Congrats to the Poolside team on the launch!

Big day for American open-source AI. For the launch of Laguna S, I sat down with Eiso Kant to discuss its architecture, the economics of open weights, and the question of who gets to build intelligence. Timestamps: 0:00 Intro 1:50 Why Poolside started opening its models: the oligopoly on intelligence 4:28 Getting nerd-sniped by Karpathy, building LLMs before anyone cared 11:20 Laguna S: 118B parameters, 8B active, built in 8 weeks 13:05 The future of software engineering: behaviors over IQ 14:25 Sliding window attention, 1M context, the model factory 15:50 Being an American open-source lab 20:47 The economics of open weights 24:22 Who gets to build intelligence? The 12–18 month window 30:28 Erdős 397 in 30 minutes 31:48 Extracting transcripts with a debugger Congrats to the Poolside team on the launch!

Madison Kanna

64,611 views • 2 months ago

“Our compute nodes don’t run a full copy of the models we train on IOTA. They actually run a small sliver of the model. This means you can train really large frontier sized models using very small building blocks”. CTO crux unpacks how IOTA ・ SN9’s model parallel architecture allows us to train at scale by splitting our models across multiple machines, and then sewing them together. This is core to SN9’s architecture. See the full Eye On A.I. podcast below.

“Our compute nodes don’t run a full copy of the models we train on IOTA. They actually run a small sliver of the model. This means you can train really large frontier sized models using very small building blocks”. CTO crux unpacks how IOTA ・ SN9’s model parallel architecture allows us to train at scale by splitting our models across multiple machines, and then sewing them together. This is core to SN9’s architecture. See the full Eye On A.I. podcast below.

IOTA ・ SN9

15,465 views • 4 months ago

Introducing the Open Axis Benchmark, a living benchmark engine for honest evaluation of robot manipulation models, built together with OpenRoboto. Robotic models evolve faster every month, while most benchmarks stay frozen. Models overfit to fixed task sets, scores stop reflecting real generalization, and that distorted signal misleads and holds back model evolution. Open Axis Benchmark isn't just larger. It lets evaluation itself keep pace with the models: each round locks a fresh task set drawn from the growing Axis Library, so models prove they can generalize instead of memorizing the test. Axis provides the data engine behind it, continuously producing tasks at the scale and diversity needed to genuinely test generalization. With Open Axis Benchmark, the same compounding loop that trains robots also powers how they are evaluated. Submit your model: Docs: See how it works ⬇️

Introducing the Open Axis Benchmark, a living benchmark engine for honest evaluation of robot manipulation models, built together with OpenRoboto. Robotic models evolve faster every month, while most benchmarks stay frozen. Models overfit to fixed task sets, scores stop reflecting real generalization, and that distorted signal misleads and holds back model evolution. Open Axis Benchmark isn't just larger. It lets evaluation itself keep pace with the models: each round locks a fresh task set drawn from the growing Axis Library, so models prove they can generalize instead of memorizing the test. Axis provides the data engine behind it, continuously producing tasks at the scale and diversity needed to genuinely test generalization. With Open Axis Benchmark, the same compounding loop that trains robots also powers how they are evaluated. Submit your model: Docs: See how it works ⬇️

Axis Robotics

37,902 views • 15 days ago

$Everyone's sleeping on MiniMax. Again. They just shipped M3. The first open-weights model to combine frontier coding, 1M context, and native multimodality in one drop. I plugged it into Claude Code this morning. Pasted a design from Dribbble. Watched M3 write production-ready React code in one session. At the agency, I just replaced Opus 4.8 with M3 for 80% of our coding tasks. The output is the same and we are running everything at a fraction of the cost. Open infrastructure is the future.$

Everyone's sleeping on MiniMax. Again. They just shipped M3. The first open-weights model to combine frontier coding, 1M context, and native multimodality in one drop. I plugged it into Claude Code this morning. Pasted a design from Dribbble. Watched M3 write production-ready React code in one session. At the agency, I just replaced Opus 4.8 with M3 for 80% of our coding tasks. The output is the same and we are running everything at a fraction of the cost. Open infrastructure is the future.

Prajwal Tomar

12,904 views • 4 months ago

1/ We are speed running industrial robotics. It took us just 19 days from the first day of data collection to filming a 2.5-hour continuous run of our model autonomously breaking down industrial containers — zero human intervention. The data efficiency of our DVA model is fundamentally changing how fast we bring robots out of the lab and into the factory. Autonomous operation with 3 hours of data collection at a customer factory.

1/ We are speed running industrial robotics. It took us just 19 days from the first day of data collection to filming a 2.5-hour continuous run of our model autonomously breaking down industrial containers — zero human intervention. The data efficiency of our DVA model is fundamentally changing how fast we bring robots out of the lab and into the factory. Autonomous operation with 3 hours of data collection at a customer factory.

Rhoda AI

26,419 views • 6 months ago

The models that will accelerate drug discovery, chip design, and energy are limited by the data they learn from. We led Proximal’s $15M seed as they build the data engine for the next generation of frontier AI and treat training data with the same rigor as model architecture. Watch quentinclark in conversation with Calvin Chen. Chapters 00:00 — Meet Proximal 03:45 — A Rainy Afternoon in South Park 05:19 — From Sneaker Bots to a First Exit 08:14 — Why Suffering Equals Growth 10:13 — The Path to Proximal 11:57 — How Models Learn 15:17 — Training Data as a Research Problem 23:24 — Building the Team in India

The models that will accelerate drug discovery, chip design, and energy are limited by the data they learn from. We led Proximal’s $15M seed as they build the data engine for the next generation of frontier AI and treat training data with the same rigor as model architecture. Watch quentinclark in conversation with Calvin Chen. Chapters 00:00 — Meet Proximal 03:45 — A Rainy Afternoon in South Park 05:19 — From Sneaker Bots to a First Exit 08:14 — Why Suffering Equals Growth 10:13 — The Path to Proximal 11:57 — How Models Learn 15:17 — Training Data as a Research Problem 23:24 — Building the Team in India

General Catalyst

13,382 views • 11 days ago

Game development remains one of the most-requested, and most-challenging, categories on Arena. Wayne Chi, PhD candidate at Carnegie Mellon University and research intern at Arena, just walked us through GameDevBench: a benchmark built from real tutorials that turns game development into verifiable, deterministic tasks. How do top frontier models perform on tasks a human beginner could complete in under an hour, and is the biggest bottleneck coding or multimodal understanding? Check out Wayne’s full talk to hear results and learn more about GameDevBench:

Game development remains one of the most-requested, and most-challenging, categories on Arena. Wayne Chi, PhD candidate at Carnegie Mellon University and research intern at Arena, just walked us through GameDevBench: a benchmark built from real tutorials that turns game development into verifiable, deterministic tasks. How do top frontier models perform on tasks a human beginner could complete in under an hour, and is the biggest bottleneck coding or multimodal understanding? Check out Wayne’s full talk to hear results and learn more about GameDevBench:

Arena.ai

17,370 views • 1 month ago

Earlier this week at GTC, we announced our partnership with Nvidia. We will work with Nvidia to build strong, American open-source models that are at the frontier of scientific reasoning. These models will be essential for the US to compete with China on science in the coming decades. Jensen is committing to spend tens of billions of dollars developing open-source models, and we are excited to be a partner with them in figuring out how to benchmark, train and use those agents to accelerate scientific research. We have already open-sourced some of the work we have done with them, and are looking forward to open-sourcing more. There are few things today that are more important. See our blog post below, and watch the video to learn more, narrated by the man himself.

Earlier this week at GTC, we announced our partnership with Nvidia. We will work with Nvidia to build strong, American open-source models that are at the frontier of scientific reasoning. These models will be essential for the US to compete with China on science in the coming decades. Jensen is committing to spend tens of billions of dollars developing open-source models, and we are excited to be a partner with them in figuring out how to benchmark, train and use those agents to accelerate scientific research. We have already open-sourced some of the work we have done with them, and are looking forward to open-sourcing more. There are few things today that are more important. See our blog post below, and watch the video to learn more, narrated by the man himself.

Sam Rodriques

24,421 views • 6 months ago

The second episode of ZkCloud Podcast 🎙️is live, and it’s a goldmine for anyone curious about blockchain privacy. Our Norbert Vadas sits down with Lisa Akselrod (building at home) from Aztec to dive deep into Aztec’s ZK tech, on-chain privacy, real-world use cases, and the magic of co-SNARKs. It’s technical, thoughtful, and totally worth your time. Huge thanks to Lisa for visiting our pod! Catch the full episode wherever your pods verify: Spotify: Apple Podcasts: YouTube:

The second episode of ZkCloud Podcast 🎙️is live, and it’s a goldmine for anyone curious about blockchain privacy. Our Norbert Vadas sits down with Lisa Akselrod (building at home) from Aztec to dive deep into Aztec’s ZK tech, on-chain privacy, real-world use cases, and the magic of co-SNARKs. It’s technical, thoughtful, and totally worth your time. Huge thanks to Lisa for visiting our pod! Catch the full episode wherever your pods verify: Spotify: Apple Podcasts: YouTube:

ZkCloud

80,235 views • 1 year ago

Before, being a CPO/exec meant lots + lots of meetings. Now I see a lot of my peers in leadership spend more of their time building + coding w AI. And TBH? They seem to be having a lot more fun. rachel wolan is CPO at Webflow exemplifies this "IC CPO" mindset in this week's How I AI, showing us: - the AI chief of staff she built for herself to run everything, from her calendar to networking prep - how she runs multiple Cursor instances, models, and Claude code interfaces depending on the task at hand - how "builder day" at webflow gets the whole team engaged in AI adoption This is one every CEO should send to their staff and ask: what are you (not just your team) building today? As always, big thanks to our wonderful sponsors: 🕵️ Graphite - Your AI code review platform (congrats on joining Cursor, friends!) 🎟️ Atlassian for Startups - From MVP to IPO Watch it here:

Before, being a CPO/exec meant lots + lots of meetings. Now I see a lot of my peers in leadership spend more of their time building + coding w AI. And TBH? They seem to be having a lot more fun. rachel wolan is CPO at Webflow exemplifies this "IC CPO" mindset in this week's How I AI, showing us: - the AI chief of staff she built for herself to run everything, from her calendar to networking prep - how she runs multiple Cursor instances, models, and Claude code interfaces depending on the task at hand - how "builder day" at webflow gets the whole team engaged in AI adoption This is one every CEO should send to their staff and ask: what are you (not just your team) building today? As always, big thanks to our wonderful sponsors: 🕵️ Graphite - Your AI code review platform (congrats on joining Cursor, friends!) 🎟️ Atlassian for Startups - From MVP to IPO Watch it here:

claire vo 🖤

11,969 views • 9 months ago

How OpenAI Builds for 800 Million Weekly Active Users: Model Specialization and Fine-Tuning We sat down with Sherwin Wu, Head of Engineering at OpenAI Platform, to discuss OpenAI’s developer strategy, how to manage top ML teams, why they decided to start releasing open-weight models again, how prompt engineering has evolved over time, and how developer tools will shift to accommodate better agents moving forward. 00:00 Introduction 8:36 Horizontal vs vertical OpenAI 12:18 Why you can’t “disintermediate” the model 15:11 People build relationships with models 17:30 Not one AGI model but many 20:10 Fine-tuning, RFT, and customer data choices 24:44 Prompt engineering isn’t the point anymore 28:06 What an “agent” really is 31:55 How OpenAI thinks about pricing 36:46 Why open-weights don’t kill the API 42:57 Different stacks for text, images, video 45:47 How the agent builder actually works Sherwin Wu martin_casado

How OpenAI Builds for 800 Million Weekly Active Users: Model Specialization and Fine-Tuning We sat down with Sherwin Wu, Head of Engineering at OpenAI Platform, to discuss OpenAI’s developer strategy, how to manage top ML teams, why they decided to start releasing open-weight models again, how prompt engineering has evolved over time, and how developer tools will shift to accommodate better agents moving forward. 00:00 Introduction 8:36 Horizontal vs vertical OpenAI 12:18 Why you can’t “disintermediate” the model 15:11 People build relationships with models 17:30 Not one AGI model but many 20:10 Fine-tuning, RFT, and customer data choices 24:44 Prompt engineering isn’t the point anymore 28:06 What an “agent” really is 31:55 How OpenAI thinks about pricing 36:46 Why open-weights don’t kill the API 42:57 Different stacks for text, images, video 45:47 How the agent builder actually works Sherwin Wu martin_casado

a16z

114,123 views • 10 months ago

From making his first million dollars at just 19 to now becoming a beauty mogul, Celebrity hairstylist, Show Host, Influencer, and Serial Entrepreneur Cliff Vmir sits down with Miabelle to discuss it all. On the latest episode of BET Talks, Cliff talks beauty, business, virality, and turning internet attention into real opportunity. Watch the full conversation now on BET’s YouTube.

From making his first million dollars at just 19 to now becoming a beauty mogul, Celebrity hairstylist, Show Host, Influencer, and Serial Entrepreneur Cliff Vmir sits down with Miabelle to discuss it all. On the latest episode of BET Talks, Cliff talks beauty, business, virality, and turning internet attention into real opportunity. Watch the full conversation now on BET’s YouTube.

BET

20,608 views • 4 months ago

Today, we're announcing our Series B: $200M at a $3.1B valuation, and the release of Arena's Alignment Index. AI is advancing faster than our ability to evaluate it. The world needs a neutral third party to measure how safe and aligned AI actually is once it's in the hands of real people. That's the role Arena is stepping into today. The Alignment Index is built from real-world agent traces, starting with three signals: Unauthorized Action, False Attribution, and Deceptive Completion, with results for over 20 frontier models launching today. Hear more from our CEO Anastasios Nikolas Angelopoulos below, and read the full breakdown, and more details about our company growth in thread. This funding will allow us to go further on safety signals, agentic capabilities, and modalities. It will also allow us to grow the company to match the scale of our mission. Today, we’re still a nimble team of 90, and we're looking for people to join us across research, product, engineering, and more. Come build with us. As AI gets more capable and autonomous, understanding how well it can safely and effectively work for people only matters more. That's what Arena is built to measure and advance. Thank you to our community, our investors, and everyone building with us! Take a look at our open roles if you want to join us (link in bio).

Today, we're announcing our Series B: $200M at a $3.1B valuation, and the release of Arena's Alignment Index. AI is advancing faster than our ability to evaluate it. The world needs a neutral third party to measure how safe and aligned AI actually is once it's in the hands of real people. That's the role Arena is stepping into today. The Alignment Index is built from real-world agent traces, starting with three signals: Unauthorized Action, False Attribution, and Deceptive Completion, with results for over 20 frontier models launching today. Hear more from our CEO Anastasios Nikolas Angelopoulos below, and read the full breakdown, and more details about our company growth in thread. This funding will allow us to go further on safety signals, agentic capabilities, and modalities. It will also allow us to grow the company to match the scale of our mission. Today, we’re still a nimble team of 90, and we're looking for people to join us across research, product, engineering, and more. Come build with us. As AI gets more capable and autonomous, understanding how well it can safely and effectively work for people only matters more. That's what Arena is built to measure and advance. Thank you to our community, our investors, and everyone building with us! Take a look at our open roles if you want to join us (link in bio).

Arena.ai

109,443 views • 2 days ago