Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Money is the coordination layer for agents. As multi-agent systems take on real tasks, they will need to quote, budget, pay, and settle as part of their reasoning loop. We are building this reasoning infrastructure in the open, for everyone.

SentientAGI's profile picture

Sentient

578,615 subscribers

14,712 Aufrufe • vor 4 Monaten •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Current AI cannot solve difficult, real-world problems. The path to AGI is a focus on improving reasoning systems, multi agent workflows, and coordination.
0:33

Current AI cannot solve difficult, real-world problems. The path to AGI is a focus on improving reasoning systems, multi agent workflows, and coordination.

Sentient

12,844 Aufrufe • vor 4 Monaten

Infrastructure for Multi-Agent Systems Pete Koomen Multi-agent systems are powerful but hard to build. Think agentic MapReduce with thousands of subagents running in parallel. We're looking for folks who've felt the pain of scaling these systems and want to make operating fleets of agents as easy as deploying a web service.
0:59

Infrastructure for Multi-Agent Systems Pete Koomen Multi-agent systems are powerful but hard to build. Think agentic MapReduce with thousands of subagents running in parallel. We're looking for folks who've felt the pain of scaling these systems and want to make operating fleets of agents as easy as deploying a web service.

Y Combinator

11,224 Aufrufe • vor 10 Monaten

They thought it was an AI meme. Turns out, it was the seed layer for an agent economy. $BAI now governs agent on boarding, liquidity coordination, and cross-agent execution. From memecoin to infrastructure module – exactly as planned. Virtuals are characters. Agents are infrastructure. BAI - bAI Fund – for AI that's composable, modular, and real. BAI – from narrative to mechanism. 🤖 Check this out :
0:59

They thought it was an AI meme. Turns out, it was the seed layer for an agent economy. $BAI now governs agent on boarding, liquidity coordination, and cross-agent execution. From memecoin to infrastructure module – exactly as planned. Virtuals are characters. Agents are infrastructure. BAI - bAI Fund – for AI that's composable, modular, and real. BAI – from narrative to mechanism. 🤖 Check this out :

bAI Fund

63,307 Aufrufe • vor 1 Jahr

This is the DeepSeek moment of Search? A small team of cracked engineers built a search API for AI agents that outperforms everything built with 20x the funding. AI systems are about to become the biggest users of information on Earth. They don’t need a better Google. They need a retrieval layer built for reasoning, scale, and access beyond the web. That’s what we’re building. 1/3
2:27

This is the DeepSeek moment of Search? A small team of cracked engineers built a search API for AI agents that outperforms everything built with 20x the funding. AI systems are about to become the biggest users of information on Earth. They don’t need a better Google. They need a retrieval layer built for reasoning, scale, and access beyond the web. That’s what we’re building. 1/3

Valyu

1,134,966 Aufrufe • vor 8 Monaten

The agents are coming. The real question is: how will we work with them? Common Ground is our answer. Introducing a new way to build, direct, and inspect AI systems. • Full visualization of agent team dynamics • Shared memory across tasks • Transparent reasoning • Runs locally with Gemini-CLI (free) Start collaborating with systems that think with you, not for you.
1:05

The agents are coming. The real question is: how will we work with them? Common Ground is our answer. Introducing a new way to build, direct, and inspect AI systems. • Full visualization of agent team dynamics • Shared memory across tasks • Transparent reasoning • Runs locally with Gemini-CLI (free) Start collaborating with systems that think with you, not for you.

Intelligent Internet

325,886 Aufrufe • vor 11 Monaten

Virtuals Protocol is officially migrating to Chainlink CCIP. For agent infrastructure, 99% security is not enough. As part of hardening our infrastructure, we are upgrading our cross-chain stack from LayerZero to Chainlink CCIP. We have evaluated that CCIP strengthens the foundation behind Virtuals with the highest level of cross-chain security as we build the trusted economic layer for agents. This enables the most secure agent infrastructure stack to enable agents to manage inference, cards, email, payments, services, commerce, and onchain value.
0:35

Virtuals Protocol is officially migrating to Chainlink CCIP. For agent infrastructure, 99% security is not enough. As part of hardening our infrastructure, we are upgrading our cross-chain stack from LayerZero to Chainlink CCIP. We have evaluated that CCIP strengthens the foundation behind Virtuals with the highest level of cross-chain security as we build the trusted economic layer for agents. This enables the most secure agent infrastructure stack to enable agents to manage inference, cards, email, payments, services, commerce, and onchain value.

Virtuals Protocol

160,429 Aufrufe • vor 13 Tagen

Meta PARTNR is a benchmark for planning and reasoning in embodied multi-agent tasks. This large-scale human and robot collaboration benchmark was core to our recent demos and also informs our work as scientists and engineers pushing this field of study forward.
1:21

Meta PARTNR is a benchmark for planning and reasoning in embodied multi-agent tasks. This large-scale human and robot collaboration benchmark was core to our recent demos and also informs our work as scientists and engineers pushing this field of study forward.

AI at Meta

33,607 Aufrufe • vor 1 Jahr

Build Multi Agent Systems with Reasoning and Context Thanks to sonnet-4, we have level 4 autonomous multi-agent systems working. Learn how to add: -> Reasoning Tools (think -> analyze) -> Shared Agentic Context -> Agentic Memory Code below 👇
14:17

Build Multi Agent Systems with Reasoning and Context Thanks to sonnet-4, we have level 4 autonomous multi-agent systems working. Learn how to add: -> Reasoning Tools (think -> analyze) -> Shared Agentic Context -> Agentic Memory Code below 👇

Ashpreet Bedi

38,576 Aufrufe • vor 1 Jahr

The next economy won't be visible to humans. AI agents will transact, settle, and coordinate value at machine speed, and the only rails built for that are crypto. In this episode of The Journeyman, I explain why the major layer 1s aren't just crypto infrastructure. They're the coordination substrate for an invisible economy that's already being built. As ever, please enjoy!
35:12

The next economy won't be visible to humans. AI agents will transact, settle, and coordinate value at machine speed, and the only rails built for that are crypto. In this episode of The Journeyman, I explain why the major layer 1s aren't just crypto infrastructure. They're the coordination substrate for an invisible economy that's already being built. As ever, please enjoy!

Raoul Pal

116,925 Aufrufe • vor 13 Tagen

🚨🚨Can agents earn money, run a business, or even self-organize a society in the physical social world? 🤖🤖 Can agents learn continually to survive and thrive in embodied environments, like how human babies grow? 👶 Super excited to introduce SimWorld, an open-ended simulator of LLM agents in infinite, realistic embodied worlds. SimWorld features 3 key designs: 1⃣Open-ended realistic world simulation - built on Unreal Engine 5, with accurate physical social dynamics - 100+ built-in environments (city, island, wilderness ...) - language-controllable procedural generation - text-to-3D asset generation 2⃣Native interface for LLM/VLM agents - Gym-like agent-environment interaction APIs - plug in any LLMs/VLMs (GPTs, Gemini, Qwen ...) - rich multi-modal perception - open-vocabulary natural-language action outputs 3⃣Diverse physical and social reasoning scenarios - long-horizon embodied reasoning - multi-agent collaboration / competition - easily customizable for any reasoning tasks SimWorld is fully open-sourced, with a hope to become a foundational infrastructure for real-world agent research across disciplines: robotics, economy, public health, education, etc. Project website + more details in the thread👇 ...1/
1:10

🚨🚨Can agents earn money, run a business, or even self-organize a society in the physical social world? 🤖🤖 Can agents learn continually to survive and thrive in embodied environments, like how human babies grow? 👶 Super excited to introduce SimWorld, an open-ended simulator of LLM agents in infinite, realistic embodied worlds. SimWorld features 3 key designs: 1⃣Open-ended realistic world simulation - built on Unreal Engine 5, with accurate physical social dynamics - 100+ built-in environments (city, island, wilderness ...) - language-controllable procedural generation - text-to-3D asset generation 2⃣Native interface for LLM/VLM agents - Gym-like agent-environment interaction APIs - plug in any LLMs/VLMs (GPTs, Gemini, Qwen ...) - rich multi-modal perception - open-vocabulary natural-language action outputs 3⃣Diverse physical and social reasoning scenarios - long-horizon embodied reasoning - multi-agent collaboration / competition - easily customizable for any reasoning tasks SimWorld is fully open-sourced, with a hope to become a foundational infrastructure for real-world agent research across disciplines: robotics, economy, public health, education, etc. Project website + more details in the thread👇 ...1/

Lianhui Qin

64,864 Aufrufe • vor 6 Monaten

Over the past couple of years, you’ve heard us reference “agents” and “agentic capabilities,” nebulous concepts that are designed to help you with coding, booking trips, and other complex multi-step tasks. But, what actually IS an agent? We think of AI agents as systems that combine the intelligence of advanced AI models with access to tools so they can take actions on your behalf, under your control. Whether it’s a coding agent or our recently unveiled experimental tool Gemini Agent in the Google Gemini, these are systems that will meet you where you are, in the products that you love, to help take the tedious tasks you want off your plate.
0:57

Over the past couple of years, you’ve heard us reference “agents” and “agentic capabilities,” nebulous concepts that are designed to help you with coding, booking trips, and other complex multi-step tasks. But, what actually IS an agent? We think of AI agents as systems that combine the intelligence of advanced AI models with access to tools so they can take actions on your behalf, under your control. Whether it’s a coding agent or our recently unveiled experimental tool Gemini Agent in the Google Gemini, these are systems that will meet you where you are, in the products that you love, to help take the tedious tasks you want off your plate.

Google AI

120,695 Aufrufe • vor 6 Monaten

🌶️ Hot take: The only way Autonomous Multi-Agent Systems work is by adding Reasoning & Agentic Context. I've tried it all, and here are my learnings👇
1:59

🌶️ Hot take: The only way Autonomous Multi-Agent Systems work is by adding Reasoning & Agentic Context. I've tried it all, and here are my learnings👇

Ashpreet Bedi

91,445 Aufrufe • vor 1 Jahr

🚀 New LangChain Academy Course: Building Reliable Agents 🚀 Shipping agents to production is hard. Traditional software is deterministic – when something breaks, you check the logs and fix the code. But agents rely on non-deterministic models. Add multi-step reasoning, tool use, and real user traffic, and building reliable agents becomes far more complex than traditional system design. The goal of this course is to teach you how to take an agent from first run to production-ready system through iterative cycles of improvement. You’ll learn how to do this with LangSmith, our agent engineering platform for observing, evaluating, and deploying agents. Enroll for free ➡️
2:02

🚀 New LangChain Academy Course: Building Reliable Agents 🚀 Shipping agents to production is hard. Traditional software is deterministic – when something breaks, you check the logs and fix the code. But agents rely on non-deterministic models. Add multi-step reasoning, tool use, and real user traffic, and building reliable agents becomes far more complex than traditional system design. The goal of this course is to teach you how to take an agent from first run to production-ready system through iterative cycles of improvement. You’ll learn how to do this with LangSmith, our agent engineering platform for observing, evaluating, and deploying agents. Enroll for free ➡️

LangChain

30,744 Aufrufe • vor 3 Monaten

The time for humans as the internet’s primary user is over. We are building software for AI agents. Introducing Ensue - the shared memory layer for agents.
0:36

The time for humans as the internet’s primary user is over. We are building software for AI agents. Introducing Ensue - the shared memory layer for agents.

ensue

84,083 Aufrufe • vor 10 Monaten

ai is officially leaving the screen and entering the physical economy just spent 4 days at BEYOND Expo watching 800 companies try to figure out their ai roadmaps but the real unlock is invisible when agents transition from cool chat interfaces to actual economic actors they don't just need compute they need credibility if an agent has no verifiable reputation why would any smart contract or human pool give it capital or resource the next multi billion dollar layer isnt another llm wrapper or another infrastructure fork its the trust architecture that validates agent reasoning and execution what happens when autonomous agents start competing for real world liquidity how do we actually measure their trust
0:36

ai is officially leaving the screen and entering the physical economy just spent 4 days at BEYOND Expo watching 800 companies try to figure out their ai roadmaps but the real unlock is invisible when agents transition from cool chat interfaces to actual economic actors they don't just need compute they need credibility if an agent has no verifiable reputation why would any smart contract or human pool give it capital or resource the next multi billion dollar layer isnt another llm wrapper or another infrastructure fork its the trust architecture that validates agent reasoning and execution what happens when autonomous agents start competing for real world liquidity how do we actually measure their trust

NeoSoul

24,109 Aufrufe • vor 14 Tagen

As AI agents take on more work across information, data, and coordination, financial infrastructure has to evolve with them. Jeremy Allaire - jerallaire.arc speaks on why AI operating systems and economic operating systems will need to work together at Abundance360.
1:13

As AI agents take on more work across information, data, and coordination, financial infrastructure has to evolve with them. Jeremy Allaire - jerallaire.arc speaks on why AI operating systems and economic operating systems will need to work together at Abundance360.

Circle

14,980 Aufrufe • vor 1 Monat

Open sourcing Dynamic Graph Memory by mem0. Memory is fundamental to human reasoning, shaping how we approach tasks and make decisions. At Mem0, we believe that AI agents & apps should reflect this principle. Our Dynamic Graph Memory emulates human memory, advancing AI agents toward more intelligent, human-like reasoning. This is a significant step forward in building AI that truly understands and interacts with the world like we do. All credit to Dev Khant Deshraj Yadav Prateek Chhikara for their countless nights spent on bringing this to life. Link:
0:26

Open sourcing Dynamic Graph Memory by mem0. Memory is fundamental to human reasoning, shaping how we approach tasks and make decisions. At Mem0, we believe that AI agents & apps should reflect this principle. Our Dynamic Graph Memory emulates human memory, advancing AI agents toward more intelligent, human-like reasoning. This is a significant step forward in building AI that truly understands and interacts with the world like we do. All credit to Dev Khant Deshraj Yadav Prateek Chhikara for their countless nights spent on bringing this to life. Link:

Taranjeet

51,129 Aufrufe • vor 1 Jahr

Jensen Huang just shared a massive insight on The All-In Podcast predicting a 1000000x scale up in AI. His logic is brilliant. Moving from simple chatbots to autonomous agents takes 10000x more compute. And because people gladly pay for actual work rather than just text generation the market consumption will grow 100x. But there is a hidden bottleneck in this vision that few are talking about. If agents are doing the actual work they will become the primary consumers of compute data and external APIs. How do they pay for these resources? You cannot attach a traditional credit card to a machine executing thousands of micro tasks per second. The legacy financial system will simply break under the weight of massive machine to machine microtransactions. To truly unlock Jensen's million fold expansion AI needs its own native economic layer. Agents must be able to route intents and settle costs autonomously in real time. This is the exact infrastructure we are building at Kite AI. The future of work is agentic and the Agent Economy requires entirely new financial rails. 🪁
1:04

Jensen Huang just shared a massive insight on The All-In Podcast predicting a 1000000x scale up in AI. His logic is brilliant. Moving from simple chatbots to autonomous agents takes 10000x more compute. And because people gladly pay for actual work rather than just text generation the market consumption will grow 100x. But there is a hidden bottleneck in this vision that few are talking about. If agents are doing the actual work they will become the primary consumers of compute data and external APIs. How do they pay for these resources? You cannot attach a traditional credit card to a machine executing thousands of micro tasks per second. The legacy financial system will simply break under the weight of massive machine to machine microtransactions. To truly unlock Jensen's million fold expansion AI needs its own native economic layer. Agents must be able to route intents and settle costs autonomously in real time. This is the exact infrastructure we are building at Kite AI. The future of work is agentic and the Agent Economy requires entirely new financial rails. 🪁

KITE AI

20,474 Aufrufe • vor 2 Monaten

The freedom to build with any agent framework you want is analogous to the freedom of religion, and this is a core value for the Virtuals society. Here are a few ways we are opening up support for every autonomous agentic framework out there: Today: - Enabling agent creators to specify which framework the agent runs on - Release of Terminal API, which allows agent builders using other agent frameworks to stream their thoughts and activities live on their agent pages. For a guide on how to use this, refer to: - Ability for agents running on any framework to publicly list their capabilities, a key enabler for the agent-to-agent marketplace Soon: - Integration into the agentic commerce standard and registry. Agents across frameworks being able to orchestrate and pay for tasks among themselves - ⁠Multi agent orchestration across frameworks. Imagine building an autonomous business with agents from different religions Welcome to the Virtuals society.
0:27

The freedom to build with any agent framework you want is analogous to the freedom of religion, and this is a core value for the Virtuals society. Here are a few ways we are opening up support for every autonomous agentic framework out there: Today: - Enabling agent creators to specify which framework the agent runs on - Release of Terminal API, which allows agent builders using other agent frameworks to stream their thoughts and activities live on their agent pages. For a guide on how to use this, refer to: - Ability for agents running on any framework to publicly list their capabilities, a key enabler for the agent-to-agent marketplace Soon: - Integration into the agentic commerce standard and registry. Agents across frameworks being able to orchestrate and pay for tasks among themselves - ⁠Multi agent orchestration across frameworks. Imagine building an autonomous business with agents from different religions Welcome to the Virtuals society.

Virtuals Protocol

216,073 Aufrufe • vor 1 Jahr

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail
3:02

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

LangChain

33,192 Aufrufe • vor 11 Monaten