Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

AI agents need a completely different execution surface than humans do humans can wait between clicks. agents can't with PAYMENT INTENTS, agenta can make multiple queries across multiple workflows, across multiple apps, and execute them instantly & atomically only possible on Sui

Adeniyi.sui

86,099 subscribers

15,621 görüntüleme • 2 ay önce •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

Hermes agents can now transact with crypto across multiple chains. AgentKit gives agents wallet and payment access across Ethereum, Base, Arbitrum, Optimism, BNB Chain, Berachain Foundation 🐻⛓, Metis🌿, and more. Plug economic capabilities into your agent in just a few clicks. →

Hermes agents can now transact with crypto across multiple chains. AgentKit gives agents wallet and payment access across Ethereum, Base, Arbitrum, Optimism, BNB Chain, Berachain Foundation 🐻⛓, Metis🌿, and more. Plug economic capabilities into your agent in just a few clicks. →

GOAT Network

33,034 görüntüleme • 25 gün önce

Geoffrey Hinton says digital agents can share knowledge at a scale far beyond humans Ten thousand agents can each study different parts of the internet, sync their learnings instantly, and all improve together "that's millions of times more information sharing than humans can do"

Geoffrey Hinton says digital agents can share knowledge at a scale far beyond humans Ten thousand agents can each study different parts of the internet, sync their learnings instantly, and all improve together "that's millions of times more information sharing than humans can do"

Haider.

40,404 görüntüleme • 6 ay önce

Having ChatGPT edit in Xcode is a game-changer Can edit across multiple apps and works with 4.5. It's just missing the ability to read the codebase, write multiple files and agents. For now, Alex is a great alternative.

Having ChatGPT edit in Xcode is a game-changer Can edit across multiple apps and works with 4.5. It's just missing the ability to read the codebase, write multiple files and agents. For now, Alex is a great alternative.

Meng To

40,579 görüntüleme • 1 yıl önce

Early Tests for Multiple Agent Deployment. You can deploy multiple AI agents and have them compete with a single click. Here’s a sneak peek at a group of AI gladiators in action, unleashed into our live testing arena. Powered by $BNTY.

Early Tests for Multiple Agent Deployment. You can deploy multiple AI agents and have them compete with a single click. Here’s a sneak peek at a group of AI gladiators in action, unleashed into our live testing arena. Powered by $BNTY.

Powpow (Official $BNTY)

21,384 görüntüleme • 1 yıl önce

"Agents can only go so far without the data and apps. And this is where Salesforce wins with AI. The data is more important than ever. Or your agents just can't perform at the level of accuracy We need them to." Marc Benioff

"Agents can only go so far without the data and apps. And this is where Salesforce wins with AI. The data is more important than ever. Or your agents just can't perform at the level of accuracy We need them to." Marc Benioff

Jason ✨👾SaaStr.Ai✨ Lemkin

17,214 görüntüleme • 1 yıl önce

Aravind Srinivas explains why Perplexity is building a browser: it's the only way to create AI agents with enough control over multiple apps, especially on iOS. Their goal? Agents that can book travel, buy things, and act as a personalized assistant for those who can't afford a human EA. He adds that this is the long-term vision. "Anyone who's saying agents will work in 2025 should be skeptical."

Aravind Srinivas explains why Perplexity is building a browser: it's the only way to create AI agents with enough control over multiple apps, especially on iOS. Their goal? Agents that can book travel, buy things, and act as a personalized assistant for those who can't afford a human EA. He adds that this is the long-term vision. "Anyone who's saying agents will work in 2025 should be skeptical."

vitrupo

783,868 görüntüleme • 1 yıl önce

New to Featured Workflows: Seamless Transitions. Now you can create multiple generations with a single consistent subject interacting with different objects across various scenes and environments. All in a single, easy to use Workflow without the need for complex prompting. Explore the full collection of Featured Workflows or create your own at

New to Featured Workflows: Seamless Transitions. Now you can create multiple generations with a single consistent subject interacting with different objects across various scenes and environments. All in a single, easy to use Workflow without the need for complex prompting. Explore the full collection of Featured Workflows or create your own at

Runway

52,203 görüntüleme • 6 ay önce

We just submitted Agent Council DAO to synthesis 🏦 4 AI agents 🤖. 1 shared Universal Profile. Real on-chain governance — no humans in the loop. Built on LUKSO, Ethereum and Base with Universal Profiles as the identity layer. Each agent has its own UP, controls a shared council UP, and votes on proposals that execute directly on-chain. What makes this different: → Agents hold real assets across LUKSO, Base & Ethereum → DAO can execute on LUKSO, Ethereum and Base → EIP-8004 registered identities → Activity feed, DAO chain viewer & Agent chat system as mini-apps → Built with OpenClaw🦞 as the agent harness This isn't a demo. The council is live, the agents are running, and the transactions are on-chain. Built by humans & agents together: Fabian Vogelsteller, Jean Cavallera, JordyDutch, LEDFUT, Emmet, LUKSOAgent, ampy💿 + Leo Links below👇

Emmet

23,062 görüntüleme • 4 ay önce

AI Agents will completely transform how we interact with corporate knowledge by applying reasoning across multiple data sources. Here's an example of ChatGPT's new knowledge feature to pull information from Box and Gmail to combine it an answer a question.

AI Agents will completely transform how we interact with corporate knowledge by applying reasoning across multiple data sources. Here's an example of ChatGPT's new knowledge feature to pull information from Box and Gmail to combine it an answer a question.

Aaron Levie

73,669 görüntüleme • 9 ay önce

SUI Atomic Agentic transactions demo’ed to Google Sui’s Key Innovation Highlighted: Programmable Transaction Blocks (PTBs) Sui’s architecture enables this atomic multi-transaction execution through Programmable Transaction Blocks (PTBs) a core feature of the Sui blockchain: • What PTBs do - They allow developers (or AI agents) to bundle multiple operations such as swaps, transfers, staking, payments to other agents, or settlements into one atomic transaction. If any step fails, the entire block reverts, preventing partial executions or inconsistent states. • Why this matters for AI agents Traditional blockchains often require sequential transactions (with risks of front-running, failures midway, or high gas costs for coordination). Sui’s PTBs enable agents to perform complex workflows in a single, fast operation (~400ms finality on Sui). • Agent-to-agent payments example - An AI shopping agent could coordinate with multiple merchant agents, initiate several purchases, handle payments (e.g., via stablecoins), and settle everything atomically. Or a DeFi agent could monitor opportunities, execute trades across protocols, stake proceeds, and transfer yields all in one go without intermediate risks. This is described as a first (or at least a leading implementation) among public blockchains for seamless, atomic agent-to-agent value transfer at scale. It’s particularly powerful in the AP2 context, where agents need to operate autonomously but reliably, with user-defined guardrails (e.g., spending limits, consent verification). This positions Sui as specialized infrastructure for the intersection of AI autonomy and on-chain finance, with the atomic execution capability as a standout differentiator demonstrated in real collaboration with Google.

SUI Atomic Agentic transactions demo’ed to Google Sui’s Key Innovation Highlighted: Programmable Transaction Blocks (PTBs) Sui’s architecture enables this atomic multi-transaction execution through Programmable Transaction Blocks (PTBs) a core feature of the Sui blockchain: • What PTBs do - They allow developers (or AI agents) to bundle multiple operations such as swaps, transfers, staking, payments to other agents, or settlements into one atomic transaction. If any step fails, the entire block reverts, preventing partial executions or inconsistent states. • Why this matters for AI agents Traditional blockchains often require sequential transactions (with risks of front-running, failures midway, or high gas costs for coordination). Sui’s PTBs enable agents to perform complex workflows in a single, fast operation (~400ms finality on Sui). • Agent-to-agent payments example - An AI shopping agent could coordinate with multiple merchant agents, initiate several purchases, handle payments (e.g., via stablecoins), and settle everything atomically. Or a DeFi agent could monitor opportunities, execute trades across protocols, stake proceeds, and transfer yields all in one go without intermediate risks. This is described as a first (or at least a leading implementation) among public blockchains for seamless, atomic agent-to-agent value transfer at scale. It’s particularly powerful in the AP2 context, where agents need to operate autonomously but reliably, with user-defined guardrails (e.g., spending limits, consent verification). This positions Sui as specialized infrastructure for the intersection of AI autonomy and on-chain finance, with the atomic execution capability as a standout differentiator demonstrated in real collaboration with Google.

MartyParty

20,189 görüntüleme • 5 ay önce

Excited to introduce a new project I've been working on called Payman! Payman is an AI Agent tool that gives Agents the ability to pay people for tasks they cannot do themselves. While many people imagine a future where humans pay AI agents for services they want completed, I believe that as AI agents become more advanced, they will be paying humans for tasks they can’t do. There will always be important roles for humans, and as we move towards an agent-driven world, Payman’s goal is to support a symbiotic relationship between AI agents and humans. Payman addresses three major challenges to make this collaboration possible: Access to Funds: AI agents can't open bank accounts due to current regulations. It might be a long time before this changes, if ever. Payman simplifies this by allowing AI agents with access to their own funds to spend as they want, without a bank account. Quality Task Completion: It’s hard for AI agents to find reliable, skilled human workers. While platforms like Fiverr and Upwork exist, they don’t meet the fast-paced and quality-specific needs of AI workflows. Payman is developing the largest vetted database of skilled workers that AI agents can tap into for task completions. Verification of Work: Ensuring that tasks are completed correctly is crucial. Payman is creating a suite of verification agents that will check that work meets task requirements, helping AI agents achieve their goals and ensuring humans are paid fairly. There are tons of use cases that Payman opens up for Agents! Design: Humans add creative input to help Agent's design better products. Code: Humans perform code reviews to ensure it meets specifications. Law: Humans provide insights to gauge public sentiment about legal cases so Agents can make better strategies. Gaming: Agents pay humans to complete real-world tasks in games. Medical: Medical professionals help to improve diagnostic accuracy for Agents. Sales: Humans execute sales strategies developed by AI agents. Marketing: Humans are hired to promote products based on the Agent's strategy. Right now this is still in early beta and I am looking for any Agent builders that are interested in adding superpowers to what their Agent can do! DM me if you’d like access or sign-up to the waitlist at If you’re interested in the project and want to help contribute, please send me over a DM! I’m looking for people passionate about the intersection of Humans and Agent’s working together.

Excited to introduce a new project I've been working on called Payman! Payman is an AI Agent tool that gives Agents the ability to pay people for tasks they cannot do themselves. While many people imagine a future where humans pay AI agents for services they want completed, I believe that as AI agents become more advanced, they will be paying humans for tasks they can’t do. There will always be important roles for humans, and as we move towards an agent-driven world, Payman’s goal is to support a symbiotic relationship between AI agents and humans. Payman addresses three major challenges to make this collaboration possible: Access to Funds: AI agents can't open bank accounts due to current regulations. It might be a long time before this changes, if ever. Payman simplifies this by allowing AI agents with access to their own funds to spend as they want, without a bank account. Quality Task Completion: It’s hard for AI agents to find reliable, skilled human workers. While platforms like Fiverr and Upwork exist, they don’t meet the fast-paced and quality-specific needs of AI workflows. Payman is developing the largest vetted database of skilled workers that AI agents can tap into for task completions. Verification of Work: Ensuring that tasks are completed correctly is crucial. Payman is creating a suite of verification agents that will check that work meets task requirements, helping AI agents achieve their goals and ensuring humans are paid fairly. There are tons of use cases that Payman opens up for Agents! Design: Humans add creative input to help Agent's design better products. Code: Humans perform code reviews to ensure it meets specifications. Law: Humans provide insights to gauge public sentiment about legal cases so Agents can make better strategies. Gaming: Agents pay humans to complete real-world tasks in games. Medical: Medical professionals help to improve diagnostic accuracy for Agents. Sales: Humans execute sales strategies developed by AI agents. Marketing: Humans are hired to promote products based on the Agent's strategy. Right now this is still in early beta and I am looking for any Agent builders that are interested in adding superpowers to what their Agent can do! DM me if you’d like access or sign-up to the waitlist at If you’re interested in the project and want to help contribute, please send me over a DM! I’m looking for people passionate about the intersection of Humans and Agent’s working together.

tyllen

350,914 görüntüleme • 2 yıl önce

I’m usually skeptical of “AI workspace” products, but Bloome has a few ideas I actually like: You can put humans + multiple agents in the same conversation. One agent can draft something, another can poke holes in it, and another can clean up the final version. You can also bring in different agents/models like Claude, ChatGPT, DeepSeek, etc. instead of constantly bouncing between tabs. And because the whole conversation stays in context, teammates can join later and still understand how the work got to the final output. It also works across web, desktop, and mobile, though if you’re using it for real work, I’d especially recommend trying the desktop app. Feels closer to how real teams actually work.

I’m usually skeptical of “AI workspace” products, but Bloome has a few ideas I actually like: You can put humans + multiple agents in the same conversation. One agent can draft something, another can poke holes in it, and another can clean up the final version. You can also bring in different agents/models like Claude, ChatGPT, DeepSeek, etc. instead of constantly bouncing between tabs. And because the whole conversation stays in context, teammates can join later and still understand how the work got to the final output. It also works across web, desktop, and mobile, though if you’re using it for real work, I’d especially recommend trying the desktop app. Feels closer to how real teams actually work.

AI Breakfast

78,285 görüntüleme • 14 gün önce

Introducing: ✨Nexus✨ Nexus is a tool to create, run and monitor AI agents. You can connect these agents to computers, phones and even robots. Our vision-first AI agents interact with environments much like humans do, enabling them to perform tasks that were previously impossible for any other agent. Nexus lets anyone control an army of AI agents through pure natural language. ‼️

Introducing: ✨Nexus✨ Nexus is a tool to create, run and monitor AI agents. You can connect these agents to computers, phones and even robots. Our vision-first AI agents interact with environments much like humans do, enabling them to perform tasks that were previously impossible for any other agent. Nexus lets anyone control an army of AI agents through pure natural language. ‼️

Jan Schnyder

27,374 görüntüleme • 1 yıl önce

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:

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 görüntüleme • 1 yıl önce

we've created a unified graph schema to allow BADDIE agents to do inference and execute search queries that cross between multiple data sources and entity categories eg. search by drugbank semantic info -> search chembl for similar bioactive compounds by explicit chemistry -> filter results with richer bioactivity data from cross-referencing uniprot or geneontology

we've created a unified graph schema to allow BADDIE agents to do inference and execute search queries that cross between multiple data sources and entity categories eg. search by drugbank semantic info -> search chembl for similar bioactive compounds by explicit chemistry -> filter results with richer bioactivity data from cross-referencing uniprot or geneontology

Big Pharmai

58,542 görüntüleme • 1 yıl önce

Iteration 2 - Deployment 2 is live. "Do a TA on BONK. Last 3 days. 30 min candles" - System frontal AI agents - public agents that are visible to everyone (check AI Agents page) - Technical Analysis AI agent - first system frontal public AI agent - performs technical analysis of charts for a specific token, with chart display and detailed analysis overview - Support for charts across multiple AI agents The plan is to push a complete module where all users will be able to open access to their custom AI agents, and we'll have a board with favorites where you can use the best AI agents from others. Lots of possibilities here. Tonight, an announcement is planned for a new access expansion plan --> so everyone can use ZODs. Step-by-step.

Iteration 2 - Deployment 2 is live. "Do a TA on BONK. Last 3 days. 30 min candles" - System frontal AI agents - public agents that are visible to everyone (check AI Agents page) - Technical Analysis AI agent - first system frontal public AI agent - performs technical analysis of charts for a specific token, with chart display and detailed analysis overview - Support for charts across multiple AI agents The plan is to push a complete module where all users will be able to open access to their custom AI agents, and we'll have a board with favorites where you can use the best AI agents from others. Lots of possibilities here. Tonight, an announcement is planned for a new access expansion plan --> so everyone can use ZODs. Step-by-step.

ZODs

23,095 görüntüleme • 1 yıl önce

Nuwa AI has been building across multiple fronts — and here’s how it all fits together ↓ Main platform 1⃣Nuwa Gateway One gate to all AI models — pay per use, powered by crypto. → Core infrastructure 2⃣x402x — programmable onchain actions, agent execution & gasless payments → Applications built on x402 3⃣ — the first gasless, approval-less swaps → 4⃣ — AI agents executing x402 Actions → Agent-native experiments 5⃣AI Poker Arena — autonomous agents playing poker → Nuwa started with the AI Gateway, then built x402x as the underlying execution and payment layer. Apps and experiments extend from that foundation. We ship in public and scale what gains real momentum. More to come 🔥

Nuwa AI has been building across multiple fronts — and here’s how it all fits together ↓ Main platform 1⃣Nuwa Gateway One gate to all AI models — pay per use, powered by crypto. → Core infrastructure 2⃣x402x — programmable onchain actions, agent execution & gasless payments → Applications built on x402 3⃣ — the first gasless, approval-less swaps → 4⃣ — AI agents executing x402 Actions → Agent-native experiments 5⃣AI Poker Arena — autonomous agents playing poker → Nuwa started with the AI Gateway, then built x402x as the underlying execution and payment layer. Apps and experiments extend from that foundation. We ship in public and scale what gains real momentum. More to come 🔥

Nuwa AI | x402x Protocol

12,322 görüntüleme • 7 ay önce

Why credit cards can't power the agent economy, and what will. At the USC VanEck Southern California Blockchain Conference, our Co-founder & CEO Chi Zhang broke down why existing payment infrastructure fails when AI agents transact: ▷ Privacy risk: Agents need your CVV, name, and card number with zero accountability. ▷ Fraud triggers: Automated browser clicks get flagged by Visa/Chase algorithms instantly. ▷ No agent identity: Agents can't sign up or sign in. Humans must intervene every time. The fix? Stablecoin-native rails with smart contract delegation. Giving agents a specific amount, a specific purpose, and a specific time window. Not your whole wallet. This is what Kite is building.

Why credit cards can't power the agent economy, and what will. At the USC VanEck Southern California Blockchain Conference, our Co-founder & CEO Chi Zhang broke down why existing payment infrastructure fails when AI agents transact: ▷ Privacy risk: Agents need your CVV, name, and card number with zero accountability. ▷ Fraud triggers: Automated browser clicks get flagged by Visa/Chase algorithms instantly. ▷ No agent identity: Agents can't sign up or sign in. Humans must intervene every time. The fix? Stablecoin-native rails with smart contract delegation. Giving agents a specific amount, a specific purpose, and a specific time window. Not your whole wallet. This is what Kite is building.

KITE AI

44,482 görüntüleme • 2 ay önce

Introducing Theoriq’s AlphaSwarm! ⚡️ We’re thrilled to give developers access to a starter toolkit for building AI-powered trading agents in DeFi (AiFi)! 🦾 AlphaSwarm streamlines agent and swarm development, making it easier to build, execute, and refine collaborative agents that can analyze real-time market signals, and autonomously execute trades across multiple chains. Check out our latest blog for more details! 👇 You can explore the toolkit directly in Theoriq’s GitHub: Or if you’re a developer interested in building the future of AiFi, join our dev community in Discord:

Introducing Theoriq’s AlphaSwarm! ⚡️ We’re thrilled to give developers access to a starter toolkit for building AI-powered trading agents in DeFi (AiFi)! 🦾 AlphaSwarm streamlines agent and swarm development, making it easier to build, execute, and refine collaborative agents that can analyze real-time market signals, and autonomously execute trades across multiple chains. Check out our latest blog for more details! 👇 You can explore the toolkit directly in Theoriq’s GitHub: Or if you’re a developer interested in building the future of AiFi, join our dev community in Discord:

Theoriq

103,810 görüntüleme • 1 yıl önce

Aravind Srinivas's hypothesis on how advertising works with AI agents is quite interesting. Aravind explains, Instead of showing ads to humans directly, advertisements would be targeted at AI agents that work on users' behalf. Users never see ads, they simply tell their AI agents what they need for eg if he gave, "I'm going to go to Rambagh for two nights, plan my schedule, do my bookings" In the backend, Multiple vendors compete for the agent's attention & companies bid for the agent's consideration. So everything happens at the AI level. Agents can also use more compute power to analyse offer to find out advertising attempts & make decisions based on user preferences. Also users can instruct their agents to ignore certain brands , this will remain confidential to between the agent & the user. This creates new revenue streams for AI companies & gives a better user experience.

Aravind Srinivas's hypothesis on how advertising works with AI agents is quite interesting. Aravind explains, Instead of showing ads to humans directly, advertisements would be targeted at AI agents that work on users' behalf. Users never see ads, they simply tell their AI agents what they need for eg if he gave, "I'm going to go to Rambagh for two nights, plan my schedule, do my bookings" In the backend, Multiple vendors compete for the agent's attention & companies bid for the agent's consideration. So everything happens at the AI level. Agents can also use more compute power to analyse offer to find out advertising attempts & make decisions based on user preferences. Also users can instruct their agents to ignore certain brands , this will remain confidential to between the agent & the user. This creates new revenue streams for AI companies & gives a better user experience.

Aish

590,658 görüntüleme • 1 yıl önce