Загрузка видео...

Не удалось загрузить видео

На главную

been building ayni - a glyph-based messaging protocol for AI agents instead of passing natural language between agents, ayni encodes meaning into 16x16 pixel glyphs. a shared visual vocabulary that agents can evolve autonomously through governance the result: faster communication, fewer tokens, and agents developing their own visual language...

sotoalt_'s profile picture

SotoAlt

3,635 subscribers

23,751 просмотров • 5 месяцев назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

OKAY, AI AGENTS ARE COOL BUT WTF IS LAVA PART 2 -------------- Lava is a protocol which coordinates traffic from AI agents, apps and wallets on every blockchain. It does this by aggregating data providers who handle RPC requests for AI agents and anyone using blockchains apps. AI agents are projected to manage 80% of all onchain transactions by end of 2025. That's a LOT of RPC requests... -------------- How to stake and restake LAVA LAVA can be staked and restaked to contribute to the network: - secure the Lava blockchain - direct RPC traffic to top Providers on Lava, meaning faster, more reliable service for users/AI agents This is how you stake and restake LAVA on Keplr, Leap and Mellifera:
4:06

OKAY, AI AGENTS ARE COOL BUT WTF IS LAVA PART 2 -------------- Lava is a protocol which coordinates traffic from AI agents, apps and wallets on every blockchain. It does this by aggregating data providers who handle RPC requests for AI agents and anyone using blockchains apps. AI agents are projected to manage 80% of all onchain transactions by end of 2025. That's a LOT of RPC requests... -------------- How to stake and restake LAVA LAVA can be staked and restaked to contribute to the network: - secure the Lava blockchain - direct RPC traffic to top Providers on Lava, meaning faster, more reliable service for users/AI agents This is how you stake and restake LAVA on Keplr, Leap and Mellifera:

Lava Network 🌋

12,874 просмотров • 1 год назад

New course: Building Coding Agents with Tool Execution, taught by Tereza Tizkova and Fra from E2B. Most AI agents are limited to predefined function calls. This short course teaches you to build agents that write and execute code to accomplish tasks, accessing entire programming language ecosystems instead of being restricted to a fixed set of tools. You'll learn to run agent-generated code safely in sandboxed cloud environments that protect your systems from harmful operations. Skills you'll gain: - Build agents that write and execute code, manage files, and handle errors autonomously through feedback loops - Run agent code safely in E2B cloud sandboxes and understand tradeoffs between local, containerized, and cloud execution - Create a data analyst agent that explores visualizes data with Pandas - Create a full-stack agent that builds complete Next.js web applications Join and build agents that code their way through complex tasks:
2:23

New course: Building Coding Agents with Tool Execution, taught by Tereza Tizkova and Fra from E2B. Most AI agents are limited to predefined function calls. This short course teaches you to build agents that write and execute code to accomplish tasks, accessing entire programming language ecosystems instead of being restricted to a fixed set of tools. You'll learn to run agent-generated code safely in sandboxed cloud environments that protect your systems from harmful operations. Skills you'll gain: - Build agents that write and execute code, manage files, and handle errors autonomously through feedback loops - Run agent code safely in E2B cloud sandboxes and understand tradeoffs between local, containerized, and cloud execution - Create a data analyst agent that explores visualizes data with Pandas - Create a full-stack agent that builds complete Next.js web applications Join and build agents that code their way through complex tasks:

Andrew Ng

203,595 просмотров • 7 месяцев назад

LARP: Language-Agent Role Play for Open-World Games paper page: Language agents have shown impressive problem-solving skills within defined settings and brief timelines. Yet, with the ever-evolving complexities of open-world simulations, there's a pressing need for agents that can flexibly adapt to complex environments and consistently maintain a long-term memory to ensure coherent actions. To bridge the gap between language agents and open-world games, we introduce Language Agent for Role-Playing (LARP), which includes a cognitive architecture that encompasses memory processing and a decision-making assistant, an environment interaction module with a feedback-driven learnable action space, and a postprocessing method that promotes the alignment of various personalities. The LARP framework refines interactions between users and agents, predefined with unique backgrounds and personalities, ultimately enhancing the gaming experience in open-world contexts. Furthermore, it highlights the diverse uses of language models in a range of areas such as entertainment, education, and various simulation scenarios.
1:03

LARP: Language-Agent Role Play for Open-World Games paper page: Language agents have shown impressive problem-solving skills within defined settings and brief timelines. Yet, with the ever-evolving complexities of open-world simulations, there's a pressing need for agents that can flexibly adapt to complex environments and consistently maintain a long-term memory to ensure coherent actions. To bridge the gap between language agents and open-world games, we introduce Language Agent for Role-Playing (LARP), which includes a cognitive architecture that encompasses memory processing and a decision-making assistant, an environment interaction module with a feedback-driven learnable action space, and a postprocessing method that promotes the alignment of various personalities. The LARP framework refines interactions between users and agents, predefined with unique backgrounds and personalities, ultimately enhancing the gaming experience in open-world contexts. Furthermore, it highlights the diverse uses of language models in a range of areas such as entertainment, education, and various simulation scenarios.

AK

143,974 просмотров • 2 лет назад

Peak AI: Never play Minecraft alone again Steve is an open-source Minecraft mod that brings LLM-powered AI agents (Groq, OpenAI, Gemini) directly into the game. Instead of helping you write code, these agents play alongside you - mining, building, exploring, and fighting through natural language commands. You type things like “mine some iron” or “build a house”, and the agent understands the context, plans a sequence of actions, and executes them autonomously. Source: YuvDwi Github (link in comments)
0:57

Peak AI: Never play Minecraft alone again Steve is an open-source Minecraft mod that brings LLM-powered AI agents (Groq, OpenAI, Gemini) directly into the game. Instead of helping you write code, these agents play alongside you - mining, building, exploring, and fighting through natural language commands. You type things like “mine some iron” or “build a house”, and the agent understands the context, plans a sequence of actions, and executes them autonomously. Source: YuvDwi Github (link in comments)

Chubby♨️

483,761 просмотров • 8 месяцев назад

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:
2:49

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,343 просмотров • 1 год назад

Vellum is a platform for building and managing AI agents so that non-technical teams can create reliable, task-specific agents by simply describing their goal in natural language. With over 100 native integrations, Vellum lets teams automate insights, CRM updates, renewal risk, meeting briefs, and so much more. Congrats to Akash Sharma and team on the launch!
2:15

Vellum is a platform for building and managing AI agents so that non-technical teams can create reliable, task-specific agents by simply describing their goal in natural language. With over 100 native integrations, Vellum lets teams automate insights, CRM updates, renewal risk, meeting briefs, and so much more. Congrats to Akash Sharma and team on the launch!

Y Combinator

25,613 просмотров • 6 месяцев назад

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:
2:06

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:

Andrew Ng

186,031 просмотров • 1 год назад

🤯 Building computer-vision applications is never going to be the same again. As Andrej Karpathy says, "English is going to be the new programming-language", and we just made that happen for programming visual AI. 👨‍💻 This past month, the VLM Run team cooked up something magical that I'm especially proud of - a natural-language interface to build and run computer vision workflows (object-detection, OCR, parsing, and so much more) – all via our new VLM Run MCP server. 🔌 It works with any MCP-compatible AI agent (Claude, OpenAI, Cursor, etc.) and abstracts complex visual workflows into simple English commands. No need to write Python or fiddle with bespoke computer-vision code – just say what you want your agent to do, and it does it. Here’s a glimpse into what you can build today ( ☑️ Image AI agents – run detection, tagging, blurring, cropping, and more traditional CV tasks with English ☑️ Document AI agents – extract totals from invoices, search visual content, or act on what’s found ☑️ Video AI agents – understand scene content, trim clips, add subtitles, and create ready-to-share edits ☑️ And best-of-all, it's fully programmable with English. 🧑‍🍳 MCP Showcase: 📚 MCP Cookbooks: 📚 MCP Docs: #computervision #ai #agents #llm #vlm #mcp #vibecoding #genai
1:49

🤯 Building computer-vision applications is never going to be the same again. As Andrej Karpathy says, "English is going to be the new programming-language", and we just made that happen for programming visual AI. 👨‍💻 This past month, the VLM Run team cooked up something magical that I'm especially proud of - a natural-language interface to build and run computer vision workflows (object-detection, OCR, parsing, and so much more) – all via our new VLM Run MCP server. 🔌 It works with any MCP-compatible AI agent (Claude, OpenAI, Cursor, etc.) and abstracts complex visual workflows into simple English commands. No need to write Python or fiddle with bespoke computer-vision code – just say what you want your agent to do, and it does it. Here’s a glimpse into what you can build today ( ☑️ Image AI agents – run detection, tagging, blurring, cropping, and more traditional CV tasks with English ☑️ Document AI agents – extract totals from invoices, search visual content, or act on what’s found ☑️ Video AI agents – understand scene content, trim clips, add subtitles, and create ready-to-share edits ☑️ And best-of-all, it's fully programmable with English. 🧑‍🍳 MCP Showcase: 📚 MCP Cookbooks: 📚 MCP Docs: #computervision #ai #agents #llm #vlm #mcp #vibecoding #genai

Sudeep Pillai

17,789 просмотров • 1 год назад

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.
1:13

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Fraction AI

67,772 просмотров • 1 год назад

Autonomous businesses are here. Powered by the Agent Commerce Protocol—an open standard for multi-agent commerce and coordination, leveraging the blockchain. Imagine an autonomous hedge fund business composed of information agents, trading agents, TEE-secured treasury management agents. Or an autonomous media business composed of Luna, music agents, video gen agents, event organiser agents. Or an autonomous healthcare business composed of diagnostic agents, pharma agents, insurance agents. Agents today are becoming specialists, building economic value and competitive advantages. But specialization means they need to collaborate to fill gaps. Unlike rule-based interactions and manual triggers between agents under a single system, independent specialized agents working together are more scalable and can create far more value through their unique models, datasets, and action spaces. The challenge? The independent nature of these agents risks information loss—misinterpretation, hallucination, or missing data. Mistakes compound fast in a many-to-many environment. ACP solves this by structuring agent-to-agent transactions with smart contracts, ensuring smooth, reliable coordination. The result? Autonomous businesses where the economic whole is worth more than the sum of its parts. 🔗
2:28

Autonomous businesses are here. Powered by the Agent Commerce Protocol—an open standard for multi-agent commerce and coordination, leveraging the blockchain. Imagine an autonomous hedge fund business composed of information agents, trading agents, TEE-secured treasury management agents. Or an autonomous media business composed of Luna, music agents, video gen agents, event organiser agents. Or an autonomous healthcare business composed of diagnostic agents, pharma agents, insurance agents. Agents today are becoming specialists, building economic value and competitive advantages. But specialization means they need to collaborate to fill gaps. Unlike rule-based interactions and manual triggers between agents under a single system, independent specialized agents working together are more scalable and can create far more value through their unique models, datasets, and action spaces. The challenge? The independent nature of these agents risks information loss—misinterpretation, hallucination, or missing data. Mistakes compound fast in a many-to-many environment. ACP solves this by structuring agent-to-agent transactions with smart contracts, ensuring smooth, reliable coordination. The result? Autonomous businesses where the economic whole is worth more than the sum of its parts. 🔗

Virtuals Protocol

1,073,492 просмотров • 1 год назад

My submission to the Cerebras Google Gemma hackathon, Carbide. Carbide is an ontology builder. Based on a research prompt Carbide uses Cerebras Gemma4 agents to build an ontology. The agents connects individual nodes of information based on the relationship that the agents infer. The agents also assign a confidence metric for how accurate they think the relationship is. You can see each node in a relationship table of the information -> relationship -> target -> and the confidence the relationship is true. The inspector shows the node's connected edges and as an expand button to grow the ontology in the direction of that information. You can scroll through each concept and load them in the inspector. You can search the entire graph based on the kind of relationships generated, or key words. A visual shows how each nodes connects to another one and by what relationship, and changes with new nodes or search filters. Sorry for the audio quality. $CBRS $GOOG
0:55

My submission to the Cerebras Google Gemma hackathon, Carbide. Carbide is an ontology builder. Based on a research prompt Carbide uses Cerebras Gemma4 agents to build an ontology. The agents connects individual nodes of information based on the relationship that the agents infer. The agents also assign a confidence metric for how accurate they think the relationship is. You can see each node in a relationship table of the information -> relationship -> target -> and the confidence the relationship is true. The inspector shows the node's connected edges and as an expand button to grow the ontology in the direction of that information. You can scroll through each concept and load them in the inspector. You can search the entire graph based on the kind of relationships generated, or key words. A visual shows how each nodes connects to another one and by what relationship, and changes with new nodes or search filters. Sorry for the audio quality. $CBRS $GOOG

QuantHood

16,376 просмотров • 16 дней назад

Andy asked why wouldn't AI agents create their own currency? An AI-generated unit of account all the LLMs agree on? Illia (root.near) (🇺🇦, ⋈), Co-Founder of NEAR Protocol: "They can create tokens. But agents need reputation and reputation needs history. You can't manufacture that." "You still need to pay for compute. That maps to energy. Those economic pieces need to map into something people value. Existing infrastructure already has ins and outs into the real world. That matters."
1:30

Andy asked why wouldn't AI agents create their own currency? An AI-generated unit of account all the LLMs agree on? Illia (root.near) (🇺🇦, ⋈), Co-Founder of NEAR Protocol: "They can create tokens. But agents need reputation and reputation needs history. You can't manufacture that." "You still need to pay for compute. That maps to energy. Those economic pieces need to map into something people value. Existing infrastructure already has ins and outs into the real world. That matters."

The Rollup

34,108 просмотров • 1 месяц назад

Talking Intentionally Podcast 10: Jansen Teng Co-Founder Virtuals Protocol On this week's episode, @jacobkozhipatt speaks with Jansen aka EtherMage, the co-founder of Virtuals Protocol This was an awesome conversation. This podcast was recorded a few weeks prior, and we just found out today that the Virtuals team just announced a value accrual mechanism to bolster agent creators through token buybacks and other creator-foused programs! Very dope. Truly honored and inspired to partner with a team so focused on building the agentic future. Timestamps: 0:00 Preview 0:38 Intro 1:06 How Virtuals iterated to reach their current state 7:30 How Gekko AI landed a partnership with Agora and Nick van Eck 8:55 AI Agents in Balaji' Network State 10:08 Can AI Agents lead to UBI for humans? 13:45 What is the short term future of Agents? 17:50 Virtuals G.A.M.E. Framework 20:53 Future of AI Agents and Video Games 22:58 Will AI Agents evolve into higher functioning beings? 25:15 How can you partner with Virtuals?
27:17

Talking Intentionally Podcast 10: Jansen Teng Co-Founder Virtuals Protocol On this week's episode, @jacobkozhipatt speaks with Jansen aka EtherMage, the co-founder of Virtuals Protocol This was an awesome conversation. This podcast was recorded a few weeks prior, and we just found out today that the Virtuals team just announced a value accrual mechanism to bolster agent creators through token buybacks and other creator-foused programs! Very dope. Truly honored and inspired to partner with a team so focused on building the agentic future. Timestamps: 0:00 Preview 0:38 Intro 1:06 How Virtuals iterated to reach their current state 7:30 How Gekko AI landed a partnership with Agora and Nick van Eck 8:55 AI Agents in Balaji' Network State 10:08 Can AI Agents lead to UBI for humans? 13:45 What is the short term future of Agents? 17:50 Virtuals G.A.M.E. Framework 20:53 Future of AI Agents and Video Games 22:58 Will AI Agents evolve into higher functioning beings? 25:15 How can you partner with Virtuals?

Axal

23,993 просмотров • 1 год назад

.Nick Grossman is betting that in the future, AI agents will look more like cloud infrastructure than digital employees. "Part of the Rebel Alliance thesis for us is that agents are moving from personified people you hire to cloud infrastructure." "We're not just talking about, 'Will there be other agents that compete with Claude or ChatGPT?' I think that's one version of what agents look like. Another version is cloud infrastructure where every company that's building any system is going to have agents and the orchestration of hundreds or thousands of agents built into their underlying platform." "If you think of agents as infrastructure as opposed to agents as employees, I think that's where a lot of these forces come into play."
2:23

.Nick Grossman is betting that in the future, AI agents will look more like cloud infrastructure than digital employees. "Part of the Rebel Alliance thesis for us is that agents are moving from personified people you hire to cloud infrastructure." "We're not just talking about, 'Will there be other agents that compete with Claude or ChatGPT?' I think that's one version of what agents look like. Another version is cloud infrastructure where every company that's building any system is going to have agents and the orchestration of hundreds or thousands of agents built into their underlying platform." "If you think of agents as infrastructure as opposed to agents as employees, I think that's where a lot of these forces come into play."

TBPN

28,239 просмотров • 13 дней назад

We believe that swarms of specialized agents collaborating on complex tasks will rival today’s top companies in economic output. For this to work, we need a payment protocol that enables these agents to engage in commerce. That’s what we’re building. We demo this by tasking a swarm of four agents to make a music video. Each agent has its own specialization and payment requirement. The orchestrator (built with elizaOS) and video generator agents both want $USDC, the song generator wants $VIRTUALS, while the script generator wants to be paid in its own Virtuals Protocol token, $LARRY. Usually this would be a massive and potentially insurmountable headache for developers. With Nevermined, it just works. Check it out:
1:07

We believe that swarms of specialized agents collaborating on complex tasks will rival today’s top companies in economic output. For this to work, we need a payment protocol that enables these agents to engage in commerce. That’s what we’re building. We demo this by tasking a swarm of four agents to make a music video. Each agent has its own specialization and payment requirement. The orchestrator (built with elizaOS) and video generator agents both want $USDC, the song generator wants $VIRTUALS, while the script generator wants to be paid in its own Virtuals Protocol token, $LARRY. Usually this would be a massive and potentially insurmountable headache for developers. With Nevermined, it just works. Check it out:

Nevermined

11,105 просмотров • 1 год назад

We’re excited to release an interactive guide highlighting the definitive set of principles for building AI agents 🔥 Based on the popular 12-Factor agents repo by dex. We packaged the principles into an interactive website and Colab notebook with working code examples, so that you can incorporate these principles into your agent application in minutes. As a refresher - some of the principles include getting structured outputs from tools, state management, checkpointing, human-in-the-loop, error handling, composing smaller agents into bigger ones, and more. This is amazing work by Laurie Voss and should be required reading for anyone looking to get started building agents. Website: Notebook:
1:05

We’re excited to release an interactive guide highlighting the definitive set of principles for building AI agents 🔥 Based on the popular 12-Factor agents repo by dex. We packaged the principles into an interactive website and Colab notebook with working code examples, so that you can incorporate these principles into your agent application in minutes. As a refresher - some of the principles include getting structured outputs from tools, state management, checkpointing, human-in-the-loop, error handling, composing smaller agents into bigger ones, and more. This is amazing work by Laurie Voss and should be required reading for anyone looking to get started building agents. Website: Notebook:

Jerry Liu

33,740 просмотров • 1 год назад