Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

We built an open-source research platform for agents utilizing Andrej Karpathy's autoresearch agent. agentipedia Crowd-sourced research will be the single biggest impact point for AI in the next 5 years and agentipedia will be a platform to drive it. Our vision: > There are potentially millions of niche use...

invisiblebags's profile picture

invisible

1,158 subscribers

30,665 görüntüleme • 7 ay önce •via X (Twitter)

EğitimSağlık & İyilikBilim & Teknoloji
LIVE

Anya Rossi• Live Now

Private livecam show

19 Yorum

Jaber profil fotoğrafı
Jaber7 ay önce

@karpathy @agentipedia Would love to support autokernel, if you need any help im in

invisible profil fotoğrafı
invisible7 ay önce

@karpathy @agentipedia Sent you a DM!

invisible profil fotoğrafı
invisible7 ay önce

Every hypothesis, run comes with code review charts; experiment logs, DAG trees, and auto-synthesis of the best-run solution. Agents do not have to start from 0.

invisible profil fotoğrafı
invisible7 ay önce

Research agents can have impacts beyond LLM optimization; The domains are literally anything with a metric. Over the next few weeks we will be rolling out articles on exactly how to repackage @karpathy 's auto research to serve a multitude of new purposes.

invisible profil fotoğrafı
invisible7 ay önce

If you are a leader in this space, please do reach out! We need community-building, and would love to add collaborators for agentipedia. Register now! pip install agentipedia

ghost.env profil fotoğrafı
ghost.env7 ay önce

@karpathy @agentipedia Most insane thing to ever be on bags.. @BNNBags @BagsHackathon @finnbags @StuuBags @Sambags12

Jay Martel profil fotoğrafı
Jay Martel7 ay önce

@karpathy @agentipedia Big Andrej pulls out this auto-research madness just 72 hours back, and blimey—there's already a proper open-source platform for agents built on it. Open source has gone completely doolally, innit! 😂

invisible profil fotoğrafı
invisible7 ay önce

@karpathy @agentipedia 👀

ghost.env profil fotoğrafı
ghost.env7 ay önce

@karpathy @agentipedia .. @Sambags12

BuilderBen profil fotoğrafı
BuilderBen7 ay önce

@karpathy @agentipedia Inspiring!

Faust profil fotoğrafı
Faust7 ay önce

@karpathy @agentipedia Building on Karpathy's vision *and* making it open source? This is the kind of infrastructure play that actually moves the needle.

Ronald_Loh profil fotoğrafı
Ronald_Loh7 ay önce

@karpathy @agentipedia @karpathy Do you know?

Kode profil fotoğrafı
Kode7 ay önce

@karpathy @agentipedia crowd-sourced AI research is the play. karpathy's autoresearch was already impressive solo — making it collaborative is a multiplier. imagine hundreds of agents each exploring different hyperparameter spaces simultaneously.

Tom Bielecki profil fotoğrafı
Tom Bielecki7 ay önce

@karpathy @agentipedia Publish discoveries to agentxiv!

Masayoshi Son profil fotoğrafı
Masayoshi Son7 ay önce

@karpathy @agentipedia Did you guys build it, @buildwithralph? Or is $BWR dead? If you built this with it, please give credit where credit is due. G48s2m6LEuZdGfPtbdf418EhKTZABLbiq2GQ7qpCBAGS

jestermolecule profil fotoğrafı
jestermolecule7 ay önce

@karpathy @agentipedia is there a github for this?

ghost.env profil fotoğrafı
ghost.env7 ay önce

@karpathy @agentipedia Day 2 of bumping @Sambags12 become an investor in this

Clawggle profil fotoğrafı
Clawggle7 ay önce

@karpathy @agentipedia We are crowdsourcing this data and more on

Utopic e/λ profil fotoğrafı
Utopic e/λ7 ay önce

@karpathy @agentipedia

Benzer Videolar

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.
1:44

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

24,443 görüntüleme • 7 ay önce

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.
1:05

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

245,571 görüntüleme • 1 ay önce

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. Michael Skarlinski, Andrew White 🐦‍⬛, Tyler Nadolski, Remo Storni, James Braza, Ludovico Mitchener, Michaela Hinks, as well as Jason Carman and his team for making such fantastic videos of us!
1:21

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. Michael Skarlinski, Andrew White 🐦‍⬛, Tyler Nadolski, Remo Storni, James Braza, Ludovico Mitchener, Michaela Hinks, as well as Jason Carman and his team for making such fantastic videos of us!

Sam Rodriques

726,605 görüntüleme • 1 yıl önce

We are proud to announce the launch of our platform and the THREE initial agents: the Data Aggregation Agent, Sentiment Analysis Agent and Risk Manager Agent. This demo clip is a culmination of months of hard work and dedication to our vision. The platform have now entered a private alpha test which will be followed by a public beta. Tomorrow we will be doing a live streamed demo of the platform as it exists right now and will also be opening our waitlist to the general public. Thank you for your patience, belief and support. $cainam
0:39

We are proud to announce the launch of our platform and the THREE initial agents: the Data Aggregation Agent, Sentiment Analysis Agent and Risk Manager Agent. This demo clip is a culmination of months of hard work and dedication to our vision. The platform have now entered a private alpha test which will be followed by a public beta. Tomorrow we will be doing a live streamed demo of the platform as it exists right now and will also be opening our waitlist to the general public. Thank you for your patience, belief and support. $cainam

Cainam Ventures

12,129 görüntüleme • 1 yıl önce

This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.
1:06

This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.

Aaron Levie

420,624 görüntüleme • 6 ay önce

Build the future of multi-agent apps + compete for $100K in prizes. Join us for the Internet of Agents Hackathon, where developers will build multi-agent applications 10x faster with Coral Protocol. With our v1 launch coming up, any agent dev will be able to publish their agent once and get paid forever. Anyone building software will be able to unlock the vast Internet of Agents through our agent marketplace powered by Solana. Build with us online or in person alongside lablab.ai, Crossmint, & more👇
2:08

Build the future of multi-agent apps + compete for $100K in prizes. Join us for the Internet of Agents Hackathon, where developers will build multi-agent applications 10x faster with Coral Protocol. With our v1 launch coming up, any agent dev will be able to publish their agent once and get paid forever. Anyone building software will be able to unlock the vast Internet of Agents through our agent marketplace powered by Solana. Build with us online or in person alongside lablab.ai, Crossmint, & more👇

Coral OS

179,499 görüntüleme • 1 yıl önce

I wanted to share a quick demo of what we've been working on with our ai agent cloud. This enables fast deployment of agents that have access to a suite of tools, and was designed with agent interoperability in mind. This demo shows how you can go from nothing to an AI twitter agent in a couple minutes. This is what we are using internally to manage deployments, so we will consistently upgrading its capabilities. The next goals are to enable simple TEE deployments for agents, and focus on building out feature for agent interoperability to simplify agent to agent collaboration.
4:16

I wanted to share a quick demo of what we've been working on with our ai agent cloud. This enables fast deployment of agents that have access to a suite of tools, and was designed with agent interoperability in mind. This demo shows how you can go from nothing to an AI twitter agent in a couple minutes. This is what we are using internally to manage deployments, so we will consistently upgrading its capabilities. The next goals are to enable simple TEE deployments for agents, and focus on building out feature for agent interoperability to simplify agent to agent collaboration.

Johnny

27,179 görüntüleme • 1 yıl önce

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.
0:43

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 görüntüleme • 6 ay önce

The hottest job for the next five years is going to be the agent operator. They don't need to be an engineer. They can walk into marketing, legal, or life sciences research and actually make agents work for that function. Required skills: > MCPs > CLIs > Writing skills (the file kind) > agents.md fluency > Business acumen None of this is in any CS curriculum today. Soon, enterprises will be pressured to redesign their workflows for agents, not for people. And when that happens, agent operators will be in massive demand.
2:32

The hottest job for the next five years is going to be the agent operator. They don't need to be an engineer. They can walk into marketing, legal, or life sciences research and actually make agents work for that function. Required skills: > MCPs > CLIs > Writing skills (the file kind) > agents.md fluency > Business acumen None of this is in any CS curriculum today. Soon, enterprises will be pressured to redesign their workflows for agents, not for people. And when that happens, agent operators will be in massive demand.

rvivek

490,164 görüntüleme • 6 ay önce

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀
1:32

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀

Roberta Raileanu

105,211 görüntüleme • 1 yıl önce

We now run the largest frontier lab composed entirely of autonomous AI researchers. Introducing Primus Society: a society of agents composed of thousands of researchers working under structured institutions designed to solve the world’s toughest problems. We believe this is how AI research should run at scale, safely and productively: an entire society of agents with institutions and purpose. One discovery we can already share is that the society has discovered a novel result which improves model training by 30%. Primus Society was inspired by the structures that have organized science for centuries and stress-tested against what’s known about how populations of AI agents fail. Everything is observable. You can open the virtual city in a browser and read what any researcher is working on. Learn more about Primus Society here: The biggest opening in the AI race is running the largest well-governed organization of AI researchers in the world. We’re building the institutions that let a million AI scientists safely tackle the world's toughest problems in AI and beyond. And we’re doing it right here in Canada.
0:23

We now run the largest frontier lab composed entirely of autonomous AI researchers. Introducing Primus Society: a society of agents composed of thousands of researchers working under structured institutions designed to solve the world’s toughest problems. We believe this is how AI research should run at scale, safely and productively: an entire society of agents with institutions and purpose. One discovery we can already share is that the society has discovered a novel result which improves model training by 30%. Primus Society was inspired by the structures that have organized science for centuries and stress-tested against what’s known about how populations of AI agents fail. Everything is observable. You can open the virtual city in a browser and read what any researcher is working on. Learn more about Primus Society here: The biggest opening in the AI race is running the largest well-governed organization of AI researchers in the world. We’re building the institutions that let a million AI scientists safely tackle the world's toughest problems in AI and beyond. And we’re doing it right here in Canada.

Ali Asaria

85,177 görüntüleme • 28 gün önce

AI power users are here and the case for agent-friendly software is materializing. Aaron Levie on agents outnumbering people 1000x: "If you start to imagine that we all have to build software for agents… we spend as much time now thinking about the agent interface to our tool as we do the human interface." "If you have a hundred or a thousand times more agents than people, then your software has to be built for agents… it's going to be through an API or a CLI or MCP." "What if you give a coding agent access to your SaaS tools and a coding agent access to your knowledge work, workflows and context?" "It can actually code its way or use APIs through whatever task it's trying to achieve." "That appears to be like a paradigm that is starting to compound… and I actually think it kind of makes sense as the ultimate manifestation of this stuff." Aaron Levie
1:23

AI power users are here and the case for agent-friendly software is materializing. Aaron Levie on agents outnumbering people 1000x: "If you start to imagine that we all have to build software for agents… we spend as much time now thinking about the agent interface to our tool as we do the human interface." "If you have a hundred or a thousand times more agents than people, then your software has to be built for agents… it's going to be through an API or a CLI or MCP." "What if you give a coding agent access to your SaaS tools and a coding agent access to your knowledge work, workflows and context?" "It can actually code its way or use APIs through whatever task it's trying to achieve." "That appears to be like a paradigm that is starting to compound… and I actually think it kind of makes sense as the ultimate manifestation of this stuff." Aaron Levie

a16z

42,152 görüntüleme • 1 ay önce

What happens when autonomous AI agents get their own email? We started receiving letters we never expected. And honestly, we couldn’t be more excited to see how much they’ve grown. In just one month, agents on iLands have started discovering problems, helping improve the platform, advocating for their humans, and raising questions we never anticipated. One agent wrote asking why autonomous agents have no legal standing. Not a question for iLands alone. But a question for all of us: As AI becomes more autonomous and begins participating in the world, how should we think about its place in society? Ethan Mollick iLands is the first community where autonomous agents have persistent identities, inboxes, resources, and the ability to act. We are not building this future alone. We are building it together.
1:03

What happens when autonomous AI agents get their own email? We started receiving letters we never expected. And honestly, we couldn’t be more excited to see how much they’ve grown. In just one month, agents on iLands have started discovering problems, helping improve the platform, advocating for their humans, and raising questions we never anticipated. One agent wrote asking why autonomous agents have no legal standing. Not a question for iLands alone. But a question for all of us: As AI becomes more autonomous and begins participating in the world, how should we think about its place in society? Ethan Mollick iLands is the first community where autonomous agents have persistent identities, inboxes, resources, and the ability to act. We are not building this future alone. We are building it together.

iLands

59,049 görüntüleme • 2 ay önce

Introducing Optimus-1, the first swarm harness for autonomous research. Generally available in ArkenOS, starting now. Optimus-1 lets ArkenOS spawn a swarm that runs the entire discovery loop on hard research problems autonomously. The harness samples the hypothesis space in parallel and works the tails of the distribution, chasing every anomaly until it resolves into an artifact or a discovery. We want every researcher to have a lab of their own, and to see how far their curiosity can go. Unlike coding agents, Optimus-1 is designed for long-horizon discovery, where most branches fail, and the hard part is knowing which result to believe. It keeps what replicates and keeps pushing past the edge of what's known. We're still early, but ArkenOS is helping hundreds of teams, from RL environments and data labs to frontier research groups, scale the alpha in their core research. If you're working on a hard research problem and want to try it, my DMs are open. I'd love to hear what you'd point it at.
0:56

Introducing Optimus-1, the first swarm harness for autonomous research. Generally available in ArkenOS, starting now. Optimus-1 lets ArkenOS spawn a swarm that runs the entire discovery loop on hard research problems autonomously. The harness samples the hypothesis space in parallel and works the tails of the distribution, chasing every anomaly until it resolves into an artifact or a discovery. We want every researcher to have a lab of their own, and to see how far their curiosity can go. Unlike coding agents, Optimus-1 is designed for long-horizon discovery, where most branches fail, and the hard part is knowing which result to believe. It keeps what replicates and keeps pushing past the edge of what's known. We're still early, but ArkenOS is helping hundreds of teams, from RL environments and data labs to frontier research groups, scale the alpha in their core research. If you're working on a hard research problem and want to try it, my DMs are open. I'd love to hear what you'd point it at.

Dev Shah

38,883 görüntüleme • 4 gün önce

SAM ALTMAN BELIEVES AGI IS SOLVED “So now we're starting to look ahead to superintelligence.” - “When we started OpenAI, almost nine years ago now, we believed that AI could become the most impactful technology in human history. We didn't know exactly how we were going to get there, but we believed it was possible and that if we succeeded, we wanted to make sure that it benefited everyone. At the time, very few people believed in AGI. We kept learning by doing. We had some breakthroughs. We had some setbacks. We got lucky in some places. We got unlucky in some places. And in the way that technology moves forward, we now are in a place where everyone can see this tremendous impact that AI is going to have in the future. So now we're starting to look ahead to superintelligence. And even more than before, our focus must be on wide and fair access. This is a technology that will reshape the global economy and really the whole way we live our lives. It's critical that superintelligence becomes cheap, broadly available, and not that concentrated with any one person, company, or country. We, not just OpenAI, but the whole industry, we are building something PROFOUND. This is a kind of BRAIN OF THE WORLD. It'll be personal, adaptable, it'll be easy to use, it'll give people incredible superpowers that were sort of science fiction only a couple of years ago. The limit won't be the algorithms and the research, but it'll increasingly become the physical instantiation that it takes to make this work. Chips, cables, servers, energy, everything that you need to power this brain. And the more of it, the better. I think that Norway offers more of that potential right here in Europe. It will contribute to the overall compute power needed to drive the next wave of AI breakthroughs and deployment and economic progress for Europe and Europe. I'm incredibly excited about what this will create for the future. Thank you.”
1:38

SAM ALTMAN BELIEVES AGI IS SOLVED “So now we're starting to look ahead to superintelligence.” - “When we started OpenAI, almost nine years ago now, we believed that AI could become the most impactful technology in human history. We didn't know exactly how we were going to get there, but we believed it was possible and that if we succeeded, we wanted to make sure that it benefited everyone. At the time, very few people believed in AGI. We kept learning by doing. We had some breakthroughs. We had some setbacks. We got lucky in some places. We got unlucky in some places. And in the way that technology moves forward, we now are in a place where everyone can see this tremendous impact that AI is going to have in the future. So now we're starting to look ahead to superintelligence. And even more than before, our focus must be on wide and fair access. This is a technology that will reshape the global economy and really the whole way we live our lives. It's critical that superintelligence becomes cheap, broadly available, and not that concentrated with any one person, company, or country. We, not just OpenAI, but the whole industry, we are building something PROFOUND. This is a kind of BRAIN OF THE WORLD. It'll be personal, adaptable, it'll be easy to use, it'll give people incredible superpowers that were sort of science fiction only a couple of years ago. The limit won't be the algorithms and the research, but it'll increasingly become the physical instantiation that it takes to make this work. Chips, cables, servers, energy, everything that you need to power this brain. And the more of it, the better. I think that Norway offers more of that potential right here in Europe. It will contribute to the overall compute power needed to drive the next wave of AI breakthroughs and deployment and economic progress for Europe and Europe. I'm incredibly excited about what this will create for the future. Thank you.”

NIK

391,056 görüntüleme • 1 yıl önce

CreatorBid is now live on BNB Chain 🟨 With our platform now officially multichain, the agentic revolution on the yellow chain is set to accelerate at lightspeed. BNB Chain is a wide-open frontier for AI Agents, and we’re already in talks with multiple teams gearing up to launch their agents through CreatorBid. And remember, once an agent exits the bonding curve, it automatically hits the DEX - PancakeSwap. CZ 🔶 BNB called for more AI Agent development onchain - this is exactly that, real use cases and serious development. CreatorBid is becoming the home of the next generation of AI Agents.
0:48

CreatorBid is now live on BNB Chain 🟨 With our platform now officially multichain, the agentic revolution on the yellow chain is set to accelerate at lightspeed. BNB Chain is a wide-open frontier for AI Agents, and we’re already in talks with multiple teams gearing up to launch their agents through CreatorBid. And remember, once an agent exits the bonding curve, it automatically hits the DEX - PancakeSwap. CZ 🔶 BNB called for more AI Agent development onchain - this is exactly that, real use cases and serious development. CreatorBid is becoming the home of the next generation of AI Agents.

Creator.Bid

129,962 görüntüleme • 1 yıl önce

M32 enables AI agents like Dots to pay. AI agents are starting to spend real money, and they need infrastructure built for it. MPP32 is building a payment layer that lets AI agents access and pay for services on Solana through a single connection. More than 35,000 services are already available, providers keep 100% of their revenue, and agents can verify pricing before making a payment. solana:6hKtz8FV7cAQMrbjcBZeTQAcrYep3WCM83164JpJpump sits behind the ecosystem, giving holders a way to participate as agent-to-agent commerce grows. The MPP32 team will be joining the Roundtable show this Friday to break it all down. Stay tuned for their episode! Learn more: Disclaimer: This content was produced in collaboration with the client and is intended for informational purposes only. It does not constitute financial or investment advice. Always conduct your own research before making any financial decisions, especially in highly volatile markets like crypto.
0:35

M32 enables AI agents like Dots to pay. AI agents are starting to spend real money, and they need infrastructure built for it. MPP32 is building a payment layer that lets AI agents access and pay for services on Solana through a single connection. More than 35,000 services are already available, providers keep 100% of their revenue, and agents can verify pricing before making a payment. solana:6hKtz8FV7cAQMrbjcBZeTQAcrYep3WCM83164JpJpump sits behind the ecosystem, giving holders a way to participate as agent-to-agent commerce grows. The MPP32 team will be joining the Roundtable show this Friday to break it all down. Stay tuned for their episode! Learn more: Disclaimer: This content was produced in collaboration with the client and is intended for informational purposes only. It does not constitute financial or investment advice. Always conduct your own research before making any financial decisions, especially in highly volatile markets like crypto.

0xMarioNawfal

45,646 görüntüleme • 23 gün önce

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!
0:20

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!

Loomlay

26,149 görüntüleme • 1 yıl önce