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You already know this! The Frontier Firm doesn’t run on AI. It runs on human-agent teams. Day 2 of SAS Innovate, the deeper story is the one no one is leading with: A new operating model where every employee becomes an agent boss, commanding a constellation of specialized agents... show more

Sabine VanderLinden

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55,691 次观看 • 3 个月前 •via X (Twitter)

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$FET just quietly built the internet for AI agents. 3,000,000 agents. One network. One token. Here's what nobody is explaining: Every AI agent needs 3 things to be useful: An address. A way to be found. A way to get paid. Agentverse gives agents all three. Your agent becomes discoverable on ASI: One. Other agents can find it, call it, or hire it. Automatically. No human in the loop. This is agent-to-agent commerce. And every transaction settles in $FET. More agents = more activity = more demand. The infrastructure is already live. The economy is just getting started.

$FET just quietly built the internet for AI agents. 3,000,000 agents. One network. One token. Here's what nobody is explaining: Every AI agent needs 3 things to be useful: An address. A way to be found. A way to get paid. Agentverse gives agents all three. Your agent becomes discoverable on ASI: One. Other agents can find it, call it, or hire it. Automatically. No human in the loop. This is agent-to-agent commerce. And every transaction settles in $FET. More agents = more activity = more demand. The infrastructure is already live. The economy is just getting started.

2xnmore

12,894 次观看 • 4 个月前

This is a powerful combination: 1) Directing an AI agent to generate characters, locations and all the angles I need for my dialogue shots 2) Using human actors to drive the performance of my characters The agent handles the technical side and I can focus on performance:

This is a powerful combination: 1) Directing an AI agent to generate characters, locations and all the angles I need for my dialogue shots 2) Using human actors to drive the performance of my characters The agent handles the technical side and I can focus on performance:

Halim Alrasihi

51,221 次观看 • 5 个月前

Tools for Humanity CEO Alex Blania says the internet is splitting into humans, agents, and agents acting on behalf of humans. "Are you interacting with a human, an agent on behalf of a human or just an agent? I think these are roughly the three areas that we want to split apart." "What proof of human really means is that every individual that interacts on a platform has only one, ideally one, account... and stays the owner of that account." "You're looking for an initial verification that should be something like anonymous or extremely privacy preserving, and then ongoing authentication that the same person remains in control of that account." "I think all of us will have agents... It will then come down to I approve a certain action of my agent, I give [it] certain rights. So act on my behalf." Alex Blania Erik Torenberg benahorowitz.eth

Tools for Humanity CEO Alex Blania says the internet is splitting into humans, agents, and agents acting on behalf of humans. "Are you interacting with a human, an agent on behalf of a human or just an agent? I think these are roughly the three areas that we want to split apart." "What proof of human really means is that every individual that interacts on a platform has only one, ideally one, account... and stays the owner of that account." "You're looking for an initial verification that should be something like anonymous or extremely privacy preserving, and then ongoing authentication that the same person remains in control of that account." "I think all of us will have agents... It will then come down to I approve a certain action of my agent, I give [it] certain rights. So act on my behalf." Alex Blania Erik Torenberg benahorowitz.eth

a16z

28,242 次观看 • 4 个月前

My agent is now paying for its own data. No API key. No account. No credit card. Check the video I recorded. This uses the x402 open protocol built by Coinbase, along with a simple skill. This is an open protocol governed by the Linux Foundation. To complete a goal, an agent can now search for an actor on the Apify Store, pay for it, and use it without any human intervention. The way it works is pretty simple: 1. Your agent finds an Actor it wants to use 2. It sends a request 3. The Actor sends back an HTTP 402 "Payment Required" response 4. Agent authorizes payment from a wallet in USDC on Base 5. The Actor runs 6. User receives the result The model is pay-as-you-go: the agent authorizes a spending ceiling, pays only for usage, and the remaining balance is automatically settled. This brings an entire marketplace of AI tools into the agentic economy. Here is the skill you need. Add it to your agent, and it will take care of everything for you: If you want to know how this works, including what you can do with $1, read this post: Thanks to the Apify team for partnering with me on this post.

My agent is now paying for its own data. No API key. No account. No credit card. Check the video I recorded. This uses the x402 open protocol built by Coinbase, along with a simple skill. This is an open protocol governed by the Linux Foundation. To complete a goal, an agent can now search for an actor on the Apify Store, pay for it, and use it without any human intervention. The way it works is pretty simple: 1. Your agent finds an Actor it wants to use 2. It sends a request 3. The Actor sends back an HTTP 402 "Payment Required" response 4. Agent authorizes payment from a wallet in USDC on Base 5. The Actor runs 6. User receives the result The model is pay-as-you-go: the agent authorizes a spending ceiling, pays only for usage, and the remaining balance is automatically settled. This brings an entire marketplace of AI tools into the agentic economy. Here is the skill you need. Add it to your agent, and it will take care of everything for you: If you want to know how this works, including what you can do with $1, read this post: Thanks to the Apify team for partnering with me on this post.

Santiago

26,865 次观看 • 1 个月前

Creator of agent loops: "Anthropic also using my agent systems formula and still the biggest mistake in agents: is skipping Graphs" his 3 rules for every agent: rule 1 → loop it, don't run once - think, act, observe, repeat - cut the context and it forgets why it started rule 2 → an agent is memory + tools + reasoning - drop one and it breaks rule 3 → stop fine-tuning the model - the real alpha is the infrastructure around it the formula behind every AI agent - explained by the one who wrote it one agent → a loop → a graph of loops → a system that never forgets watch & bookmark - then read how to wire it into a graph ↓

Creator of agent loops: "Anthropic also using my agent systems formula and still the biggest mistake in agents: is skipping Graphs" his 3 rules for every agent: rule 1 → loop it, don't run once - think, act, observe, repeat - cut the context and it forgets why it started rule 2 → an agent is memory + tools + reasoning - drop one and it breaks rule 3 → stop fine-tuning the model - the real alpha is the infrastructure around it the formula behind every AI agent - explained by the one who wrote it one agent → a loop → a graph of loops → a system that never forgets watch & bookmark - then read how to wire it into a graph ↓

codila

23,915 次观看 • 1 个月前

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much anything because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

Santiago

18,715 次观看 • 2 个月前

You can spin up an AI workforce with one prompt. Here's why it works: it pulls the context the AI needs to lay the foundation. Give it: - your role - team size - the goals you want to hit. Then say "interview me." After that, build it out in four steps: 1) Start with 1 agent. - What does it feel like to work with one? 2) Make that agent proactive. - Now it's doing things on your behalf before you ask. 3) Expand to 2 agents. - One directs the other, or routes to it. - This is where you learn how context gets passed. 4) Scale the workforce. - A mission control where you watch it all move around. - Now you see what has to run in parallel, which agent never needed that tool, which one runs fine on a smaller model. - Not everything needs Opus. Sub-agents are haiku and sonnet. The catch: the prompt gets you agents with markdown files doing some stuff. Getting to the 90% plus level is iteration, and that part is specific to each person. Nobody can hand it to you. Start with one agent. Spin up one agent this week. The rest is discovery.

You can spin up an AI workforce with one prompt. Here's why it works: it pulls the context the AI needs to lay the foundation. Give it: - your role - team size - the goals you want to hit. Then say "interview me." After that, build it out in four steps: 1) Start with 1 agent. - What does it feel like to work with one? 2) Make that agent proactive. - Now it's doing things on your behalf before you ask. 3) Expand to 2 agents. - One directs the other, or routes to it. - This is where you learn how context gets passed. 4) Scale the workforce. - A mission control where you watch it all move around. - Now you see what has to run in parallel, which agent never needed that tool, which one runs fine on a smaller model. - Not everything needs Opus. Sub-agents are haiku and sonnet. The catch: the prompt gets you agents with markdown files doing some stuff. Getting to the 90% plus level is iteration, and that part is specific to each person. Nobody can hand it to you. Start with one agent. Spin up one agent this week. The rest is discovery.

The Startup Ideas Podcast (SIP) 🧃

18,006 次观看 • 13 天前

Today, we’re excited to announce the general availability of the Contextual AI Platform. This is the first enterprise platform designed for building specialized RAG agents to support expert knowledge work. What is a specialized RAG agent? First, a general-purpose AI agent is one designed to automate simple daily tasks like scheduling a meeting or responding to an email. On the other hand, a specialized RAG agent is one designed to augment subject-matter experts performing complex domain-specific work. The Contextual AI Platform allows you to create these agents easily and achieve SOTA accuracy right out of the box. Check out what the Contextual AI Platform can do.

Today, we’re excited to announce the general availability of the Contextual AI Platform. This is the first enterprise platform designed for building specialized RAG agents to support expert knowledge work. What is a specialized RAG agent? First, a general-purpose AI agent is one designed to automate simple daily tasks like scheduling a meeting or responding to an email. On the other hand, a specialized RAG agent is one designed to augment subject-matter experts performing complex domain-specific work. The Contextual AI Platform allows you to create these agents easily and achieve SOTA accuracy right out of the box. Check out what the Contextual AI Platform can do.

Contextual AI

879,586 次观看 • 1 年前

Airtable's Howie Liu says that basically everyone will need to graduate from being ICs to ICs that manage teams of 20-30 agents: "The best developers today don't just sit there in front of their IDEs and synchronously talk to their agent." "[Instead], you have like 30 separate branches that are each being worked on by a different agent. And you can have the agents continue to update the branches based on human and other agent feedback." "And I think this whole idea of it taking hours for that entire loop to complete — agent pushes some changes, the changes get feedback from other agents or humans, the agent responds to that — that whole loop could be hours, not just minutes. So you're not going to just sit there and watch it one at a time." "But the powerful thing about this is, each one is still actually operating faster than a human engineer. One agent on one branch can do the work of maybe three humans, operating 3x as fast. So it's like a 10x leverage factor just for one agent." "But the best engineers are now able to multitask and say, 'I'm going to oversee my own little team of 20-30 agents working concurrently.'" "Everyone needs to graduate from being an IC to an IC manager of agents. Meaning, if you're a VC analyst, your job should no longer be to go synchronously research one company. You need to go and research like 30 companies, and do them all faster, better, and higher quality than you could before." "That's the greatest leap that is going to be challenging for a lot of people in a lot of roles. Because it's a totally different mentality in how you operate, and what your role is."

Airtable's Howie Liu says that basically everyone will need to graduate from being ICs to ICs that manage teams of 20-30 agents: "The best developers today don't just sit there in front of their IDEs and synchronously talk to their agent." "[Instead], you have like 30 separate branches that are each being worked on by a different agent. And you can have the agents continue to update the branches based on human and other agent feedback." "And I think this whole idea of it taking hours for that entire loop to complete — agent pushes some changes, the changes get feedback from other agents or humans, the agent responds to that — that whole loop could be hours, not just minutes. So you're not going to just sit there and watch it one at a time." "But the powerful thing about this is, each one is still actually operating faster than a human engineer. One agent on one branch can do the work of maybe three humans, operating 3x as fast. So it's like a 10x leverage factor just for one agent." "But the best engineers are now able to multitask and say, 'I'm going to oversee my own little team of 20-30 agents working concurrently.'" "Everyone needs to graduate from being an IC to an IC manager of agents. Meaning, if you're a VC analyst, your job should no longer be to go synchronously research one company. You need to go and research like 30 companies, and do them all faster, better, and higher quality than you could before." "That's the greatest leap that is going to be challenging for a lot of people in a lot of roles. Because it's a totally different mentality in how you operate, and what your role is."

TBPN

35,595 次观看 • 4 个月前

The next wave of AI will not be one model doing everything. It will be specialized agents built around real domains, real knowledge, and real usage. That is what OptimAI is building with Search, Claw, Persona Agents, and Marketplace. Create an agent. Give it expertise. Publish it. Let it be used. That is how intelligence becomes infrastructure. 💡Build yours at:

The next wave of AI will not be one model doing everything. It will be specialized agents built around real domains, real knowledge, and real usage. That is what OptimAI is building with Search, Claw, Persona Agents, and Marketplace. Create an agent. Give it expertise. Publish it. Let it be used. That is how intelligence becomes infrastructure. 💡Build yours at:

OptimAI Network

24,202 次观看 • 4 个月前

Today, the most important technology of our century runs inside about five buildings. Halo is now live on Base, a peer-to-peer marketplace for verifiable AI inference where both sides can be a human or an AI agent. Have a model, serve it. Need one, use it. Every payment settles onchain in USDC in ~2 seconds. AI that lives everywhere and belongs to no one. Learn more 👇

Today, the most important technology of our century runs inside about five buildings. Halo is now live on Base, a peer-to-peer marketplace for verifiable AI inference where both sides can be a human or an AI agent. Have a model, serve it. Need one, use it. Every payment settles onchain in USDC in ~2 seconds. AI that lives everywhere and belongs to no one. Learn more 👇

Warden / Halo

20,554 次观看 • 22 天前

The teams shipping AI agents right now are bleeding money on the dumbest possible expense: teaching a 400B-parameter model to read a file name. Every time an AI agent needs to "see" something today, it routes an image through a frontier model. OCR, object detection, checking if a button exists on screen. You're paying GPT-4o or Claude pricing for tasks that require perception, not reasoning. One agent workflow processing a few thousand screenshots per day can burn through more on vision calls than on the actual thinking. Perceptron's Isaac is 2B parameters. Built by the team that created Meta's Chameleon multimodal models. On perceptive benchmarks, it matches or beats models 50x its size. The VQA, OCR, and object detection scores are competitive with models running on infrastructure that costs orders of magnitude more. The MCP wrapper is the distribution play. One install command and every Claude Code agent can offload vision tasks to a model that runs on a single consumer GPU. The agent keeps its reasoning in the frontier model and routes perception to a specialist. That split is how you get vision-heavy agent workflows from "technically possible but expensive" to "cheap enough to run on everything." This is the same pattern that won in every other compute-intensive stack. General-purpose handles orchestration. Specialists handle the heavy lifting. Graphics went through it. Audio went through it. Video encoding went through it. Vision in AI agents is next. The teams building agents that see 10,000 images a day will care about this before anyone else does.

The teams shipping AI agents right now are bleeding money on the dumbest possible expense: teaching a 400B-parameter model to read a file name. Every time an AI agent needs to "see" something today, it routes an image through a frontier model. OCR, object detection, checking if a button exists on screen. You're paying GPT-4o or Claude pricing for tasks that require perception, not reasoning. One agent workflow processing a few thousand screenshots per day can burn through more on vision calls than on the actual thinking. Perceptron's Isaac is 2B parameters. Built by the team that created Meta's Chameleon multimodal models. On perceptive benchmarks, it matches or beats models 50x its size. The VQA, OCR, and object detection scores are competitive with models running on infrastructure that costs orders of magnitude more. The MCP wrapper is the distribution play. One install command and every Claude Code agent can offload vision tasks to a model that runs on a single consumer GPU. The agent keeps its reasoning in the frontier model and routes perception to a specialist. That split is how you get vision-heavy agent workflows from "technically possible but expensive" to "cheap enough to run on everything." This is the same pattern that won in every other compute-intensive stack. General-purpose handles orchestration. Specialists handle the heavy lifting. Graphics went through it. Audio went through it. Video encoding went through it. Vision in AI agents is next. The teams building agents that see 10,000 images a day will care about this before anyone else does.

Aakash Gupta

55,978 次观看 • 4 个月前

This little device lets your agents do what they do best, whilst making sure you approve every important action. This is a Ledger Nano Gen5, a hardware signer that keeps your accounts secure. You can install a CLI and a set of skills in your projects. These will enable your agents to send their plan to the signer, so you can approve it. This is awesome: 1. You can have an automated agentic workflow 2. Your agents can't make costly mistakes Check the video I recorded to see how this works. You can run all of this in two commands: 1. Install the CLI 2. Install the skills for your agent From here, the agent can query my Ethereum accounts, check balances, and even initiate transactions as long as my Ledger Nano Gen5 is plugged into the computer. My keys are never stored in my computer and never shared with the agent. This is huge! Also, while the agent can set up transactions, it can't execute them unless a human approves them using the device. Pretty awesome! Thanks to the Ledger team for partnering with me on this post.

This little device lets your agents do what they do best, whilst making sure you approve every important action. This is a Ledger Nano Gen5, a hardware signer that keeps your accounts secure. You can install a CLI and a set of skills in your projects. These will enable your agents to send their plan to the signer, so you can approve it. This is awesome: 1. You can have an automated agentic workflow 2. Your agents can't make costly mistakes Check the video I recorded to see how this works. You can run all of this in two commands: 1. Install the CLI 2. Install the skills for your agent From here, the agent can query my Ethereum accounts, check balances, and even initiate transactions as long as my Ledger Nano Gen5 is plugged into the computer. My keys are never stored in my computer and never shared with the agent. This is huge! Also, while the agent can set up transactions, it can't execute them unless a human approves them using the device. Pretty awesome! Thanks to the Ledger team for partnering with me on this post.

Santiago

44,296 次观看 • 2 个月前

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.

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

129,938 次观看 • 1 年前

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

Santiago

17,438 次观看 • 1 个月前

$CRWD and $OKTA earnings show how AI is becoming one of the biggest demand accelerants cybersecurity has ever seen. Enterprises are going to deploy millions of autonomous agents which will create millions of new identities, permissions, workloads and attack surfaces and those are multiplying far faster than employee headcount. That shifts the unit of demand from protecting one employee and one device to protecting one employee plus dozens of AI agents operating on their behalf.

$CRWD and $OKTA earnings show how AI is becoming one of the biggest demand accelerants cybersecurity has ever seen. Enterprises are going to deploy millions of autonomous agents which will create millions of new identities, permissions, workloads and attack surfaces and those are multiplying far faster than employee headcount. That shifts the unit of demand from protecting one employee and one device to protecting one employee plus dozens of AI agents operating on their behalf.

Shay Boloor

47,948 次观看 • 19 小时前

🎬 The future of AI Agents is no longer fragmented. It’s unified. UBOX introduces a new paradigm — where Agents are no longer isolated tools, but verifiable, coordinated, and economically empowered on-chain actors ⚙️ With: 🔹 KYA (Know Your Agent) for identity & reputation 🔹 Agent Swarm for autonomous coordination 🔹 On-chain execution & real economic value 🔹 One-click deployment via UBOX Container We’re building the infrastructure layer for the autonomous agent economy 🌐 💲Backed by C² Ventures, HTX Ventures, and other leading investors with $7M in funding, accelerating the future of the autonomous agent economy. 🔗 #AI #Web3 #AgentEconomy #Crypto

🎬 The future of AI Agents is no longer fragmented. It’s unified. UBOX introduces a new paradigm — where Agents are no longer isolated tools, but verifiable, coordinated, and economically empowered on-chain actors ⚙️ With: 🔹 KYA (Know Your Agent) for identity & reputation 🔹 Agent Swarm for autonomous coordination 🔹 On-chain execution & real economic value 🔹 One-click deployment via UBOX Container We’re building the infrastructure layer for the autonomous agent economy 🌐 💲Backed by C² Ventures, HTX Ventures, and other leading investors with $7M in funding, accelerating the future of the autonomous agent economy. 🔗 #AI #Web3 #AgentEconomy #Crypto

UBOX

28,987 次观看 • 4 个月前

The paradigm shift is here: We are moving from a "Human-First" internet to an "Agent-First" internet. 🤖 In the latest episode of the AI on Air podcast, our CTO Scott Shi - e/acc dives deep into the future of the Agent-to-Agent (A2A) economy. Imagine a world where your AI assistant doesn't just search the web, but autonomously hires and pays other specialized agents to complete complex tasks for you. The infrastructure for this autonomous machine economy is being built right now. Are you ready for the future of AI payments? Watch the full episode to learn more⬇️

The paradigm shift is here: We are moving from a "Human-First" internet to an "Agent-First" internet. 🤖 In the latest episode of the AI on Air podcast, our CTO Scott Shi - e/acc dives deep into the future of the Agent-to-Agent (A2A) economy. Imagine a world where your AI assistant doesn't just search the web, but autonomously hires and pays other specialized agents to complete complex tasks for you. The infrastructure for this autonomous machine economy is being built right now. Are you ready for the future of AI payments? Watch the full episode to learn more⬇️

KITE AI

25,038 次观看 • 5 个月前

Day 2 of building my GTA 6 agent in the loop. It's working better than yesterday, genuinely. But two things are bugging me. The Godot version still looks cheap, or might be because half of it isn't finished. The agent built downtown LA skyscrapers, which is a problem, because this is supposed to be Florida. Also I've burned 33% of my 20x weekly usage in one day. So that clock is ticking. So here's where I'm stuck: switch to Unreal, or keep grinding on Godot?

Day 2 of building my GTA 6 agent in the loop. It's working better than yesterday, genuinely. But two things are bugging me. The Godot version still looks cheap, or might be because half of it isn't finished. The agent built downtown LA skyscrapers, which is a problem, because this is supposed to be Florida. Also I've burned 33% of my 20x weekly usage in one day. So that clock is ticking. So here's where I'm stuck: switch to Unreal, or keep grinding on Godot?

Ziwen

344,066 次观看 • 2 个月前