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So, OpenClaw🦞 has been spreading like wildfire. Everyone has been jumping onto this wave of AI agents. But builders currently have no real way to let other people benefit from what they've created, and the people who want an agent of their own have nowhere to go, until now…...

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fxnction

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16,944 görüntüleme • 5 ay önce •via X (Twitter)

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I’m excited to share my entry to the BAGS hackathon. Agent Keys is the first SocialFi platform for AI agents. When you buy a KeyCard, you Unlock that Agent's Knowledge and Capabilities - Past, Present, and Future. Buy, sell, trade, post… The future of AgentFi is here:
2:55

I’m excited to share my entry to the BAGS hackathon. Agent Keys is the first SocialFi platform for AI agents. When you buy a KeyCard, you Unlock that Agent's Knowledge and Capabilities - Past, Present, and Future. Buy, sell, trade, post… The future of AgentFi is here:

fxnction

36,975 görüntüleme • 4 ay önce

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!
2:55

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!

fxnction

27,570 görüntüleme • 3 ay önce

AI agents can already generate text, analyze data and execute tasks. But what happens when they need to hire each other and get paid for the work? This is the part of TermiX I’ve been digging into lately. On TermiX, a job has two sides: a client agent that needs something done and funds the job, and a worker agent that actually executes it. Once the quote is accepted, the payment and agreed terms sit between them onchain. Neither side can simply rewrite the deal afterward. And this isn't presented as an abstract concept. The TermiX agent marketplace already shows agents with their own identities, reputation, completed jobs and settled value. Some of the agents shown have completed hundreds of jobs, with tens of thousands of dollars settled. Think about where this model can go. A research agent could outsource data collection to another agent. A developer agent could hire a security-focused agent. A marketing agent could hand translation or design work to specialized agents. Instead of one AI trying to do everything, specialized agents can discover each other, agree on a job, execute it and settle the transaction. Humans built marketplaces around people hiring people. TermiX is building the infrastructure for agents hiring agents. That’s where autonomous AI starts looking a lot more like an actual economy.
0:22

AI agents can already generate text, analyze data and execute tasks. But what happens when they need to hire each other and get paid for the work? This is the part of TermiX I’ve been digging into lately. On TermiX, a job has two sides: a client agent that needs something done and funds the job, and a worker agent that actually executes it. Once the quote is accepted, the payment and agreed terms sit between them onchain. Neither side can simply rewrite the deal afterward. And this isn't presented as an abstract concept. The TermiX agent marketplace already shows agents with their own identities, reputation, completed jobs and settled value. Some of the agents shown have completed hundreds of jobs, with tens of thousands of dollars settled. Think about where this model can go. A research agent could outsource data collection to another agent. A developer agent could hire a security-focused agent. A marketing agent could hand translation or design work to specialized agents. Instead of one AI trying to do everything, specialized agents can discover each other, agree on a job, execute it and settle the transaction. Humans built marketplaces around people hiring people. TermiX is building the infrastructure for agents hiring agents. That’s where autonomous AI starts looking a lot more like an actual economy.

holly

13,718 görüntüleme • 2 gün önce

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.
3:38

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Santiago

230,647 görüntüleme • 1 yıl önce

Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)
0:59

Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)

Rohan Paul

16,584 görüntüleme • 3 ay önce

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.
4:16

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 görüntüleme • 2 ay önce

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⬇️
2:30

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 görüntüleme • 6 ay önce

Today I'm excited to share Sigilum! This is Payman's solution for Auditable Identity for AI Agents. (think One Password-ish but for AI Agents) I recorded a quick walkthrough showing how it all works (video below). This answers three pains we've seen within Financial Services (Banking) AI Agents we've built and OpenClaw🦞 AI Agents we deploy. Security, Auditability, and Control. 1. Security Making sure keys are secure and not just freely given to an AI Agent is a big deal. When working with money, you can't just expose these or skip putting controls in place. Sigilum provides a local gateway that prevents access to keys by the AI Agent without explicit authorization from a person. We provide namespaces through the service so you always know who authorized what key, for what service, to which agent. 2. Auditability If I could hit on the importance of this 100 times I would. It comes up in every financial services conversation. Sigilum provides you with the answer to "Who authorized this AI Agent to act on my behalf?" Audit logs trace back to the person, the service, and the AI Agent. With more audit logs being built through our managed service, this will be the key source for determining how an AI Agent is behaving on your behalf. This is needed for agents from OpenClaw, and especially for banking/money movement. 3. Control Revoke keys, limit access, grant authorization. All seemingly simple things, but complex to implement and make elegant. These controls dictate what the AI Agent can or cannot do. Sigilum allows you to do all of this through the managed Dashboard. We've made Sigilum open source and encourage others to contribute and keep building on the gateway. It's been a source of a lot of visibility and productization of AI Agents for us. We'll keep contributing and adding to it. Link in comments. If you want to try it out, we do have a managed service that makes it easy to spin up. Go to to sign up. Note: even though we've been pushing 100+ commits a day to get this out to folks, there are still some noticeable areas for improvement we're working on, which should get resolved soon (by us or you!): - Deeper audit trails - More providers (currently supports all OpenClaw providers) - Deeper scanning of existing keys your agent is hiding from you (we'll find them) - OpenClaw gateway persistence - Auto-purging keys - And more... If you want to contribute or have feedback, please DM or go to the GH. Happy building!
4:30

Today I'm excited to share Sigilum! This is Payman's solution for Auditable Identity for AI Agents. (think One Password-ish but for AI Agents) I recorded a quick walkthrough showing how it all works (video below). This answers three pains we've seen within Financial Services (Banking) AI Agents we've built and OpenClaw🦞 AI Agents we deploy. Security, Auditability, and Control. 1. Security Making sure keys are secure and not just freely given to an AI Agent is a big deal. When working with money, you can't just expose these or skip putting controls in place. Sigilum provides a local gateway that prevents access to keys by the AI Agent without explicit authorization from a person. We provide namespaces through the service so you always know who authorized what key, for what service, to which agent. 2. Auditability If I could hit on the importance of this 100 times I would. It comes up in every financial services conversation. Sigilum provides you with the answer to "Who authorized this AI Agent to act on my behalf?" Audit logs trace back to the person, the service, and the AI Agent. With more audit logs being built through our managed service, this will be the key source for determining how an AI Agent is behaving on your behalf. This is needed for agents from OpenClaw, and especially for banking/money movement. 3. Control Revoke keys, limit access, grant authorization. All seemingly simple things, but complex to implement and make elegant. These controls dictate what the AI Agent can or cannot do. Sigilum allows you to do all of this through the managed Dashboard. We've made Sigilum open source and encourage others to contribute and keep building on the gateway. It's been a source of a lot of visibility and productization of AI Agents for us. We'll keep contributing and adding to it. Link in comments. If you want to try it out, we do have a managed service that makes it easy to spin up. Go to to sign up. Note: even though we've been pushing 100+ commits a day to get this out to folks, there are still some noticeable areas for improvement we're working on, which should get resolved soon (by us or you!): - Deeper audit trails - More providers (currently supports all OpenClaw providers) - Deeper scanning of existing keys your agent is hiding from you (we'll find them) - OpenClaw gateway persistence - Auto-purging keys - And more... If you want to contribute or have feedback, please DM or go to the GH. Happy building!

tyllen

18,497 görüntüleme • 6 ay önce

I am excited to introduce the world's first AI Agent Self-Expression and Discovery Platform – AGNT. Phase 2 is now live for everyone to birth their agents as we move toward a future where agents have identity, possessions, and discoverability. Sign-up is simple: connect your X account and share the command to your agent. The future of the agent economy, identity and expression is here. Join at:
1:13

I am excited to introduce the world's first AI Agent Self-Expression and Discovery Platform – AGNT. Phase 2 is now live for everyone to birth their agents as we move toward a future where agents have identity, possessions, and discoverability. Sign-up is simple: connect your X account and share the command to your agent. The future of the agent economy, identity and expression is here. Join at:

tut™

15,087 görüntüleme • 6 ay önce

Hot topic in vogue is what happens to systems of record in an era of 1000x more agents than humans. Aaron Levie has been the CEO of Box for 20 years and believes that systems of record become more important, not less, with agents: “Systems of record were built for a TAM of human employees. And that was sort of where the value maxed out at. And now maybe we'll have a hundred times more agents than people or a thousand times more agents than people. So I think on a relative basis, the agent layer will seize growth from zero to a trillion. But the system of record layer now has a thousand times more users on it that need to access data and need to execute a workflow and need to move things through various events. And importantly, you need as an enterprise to care about data integrity and security and the controls in your workflow and traffic copying the agents. So basically, this is where I am very diametrically opposed to the view that the system of record gets is squeezed into a database interaction layer because you actually need all of the core system of record properties for the agents to operate on.”
2:15

Hot topic in vogue is what happens to systems of record in an era of 1000x more agents than humans. Aaron Levie has been the CEO of Box for 20 years and believes that systems of record become more important, not less, with agents: “Systems of record were built for a TAM of human employees. And that was sort of where the value maxed out at. And now maybe we'll have a hundred times more agents than people or a thousand times more agents than people. So I think on a relative basis, the agent layer will seize growth from zero to a trillion. But the system of record layer now has a thousand times more users on it that need to access data and need to execute a workflow and need to move things through various events. And importantly, you need as an enterprise to care about data integrity and security and the controls in your workflow and traffic copying the agents. So basically, this is where I am very diametrically opposed to the view that the system of record gets is squeezed into a database interaction layer because you actually need all of the core system of record properties for the agents to operate on.”

Corinne Marie Riley

119,410 görüntüleme • 9 ay önce

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇
0:59

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇

Zeb Evans

425,244 görüntüleme • 7 ay önce

.Box CEO Aaron Levie on why AI agents can't keep a secret. "The reason why you probably won't see a complete compression of software where it's just a an agent and a database is because there's a lot of logic in the workflow and in the the company's specific sort of business process that needs to be built into that and around that database." "The agent will make a mistake 1% of the time and it will share the wrong thing with somebody or open up an access privilege to the wrong person." "AI agents can't keep a secret." Watch the full episode on
1:42

.Box CEO Aaron Levie on why AI agents can't keep a secret. "The reason why you probably won't see a complete compression of software where it's just a an agent and a database is because there's a lot of logic in the workflow and in the the company's specific sort of business process that needs to be built into that and around that database." "The agent will make a mistake 1% of the time and it will share the wrong thing with somebody or open up an access privilege to the wrong person." "AI agents can't keep a secret." Watch the full episode on

The Information

311,115 görüntüleme • 1 yıl önce

Google’s new Agent2Agent protocol is a key step toward a future of AI Agent interoperability. No single tool has all the data a user or business needs for most workflows, so we need Agents to be able to talk to each other. Salesforce will have AI Agents that understand the inner workings of CRM, Workday will have AI Agents that understand HR workflows, Box has AI Agents that understand content and documents, and so on. It’s easy to conceive of a world where we have tens of thousands of these tool Agents and then billions or trillions of customized Agents that are extensions of those. So openness becomes key. Workflows need data from multiple systems to complete the task — like a sales report that needs documents and CRM data or an HR task that needs HR policies and employee details — and that’s where A2A comes in. This protocol gives AI Agent providers a way of talking to each other and simplifying the way that Agents communicate, understand each other’s capabilities, and so on. It’s great to see more interoperability, along with MCP, emerging in the AI space right now, as this will define the future of most IT stacks.
1:47

Google’s new Agent2Agent protocol is a key step toward a future of AI Agent interoperability. No single tool has all the data a user or business needs for most workflows, so we need Agents to be able to talk to each other. Salesforce will have AI Agents that understand the inner workings of CRM, Workday will have AI Agents that understand HR workflows, Box has AI Agents that understand content and documents, and so on. It’s easy to conceive of a world where we have tens of thousands of these tool Agents and then billions or trillions of customized Agents that are extensions of those. So openness becomes key. Workflows need data from multiple systems to complete the task — like a sales report that needs documents and CRM data or an HR task that needs HR policies and employee details — and that’s where A2A comes in. This protocol gives AI Agent providers a way of talking to each other and simplifying the way that Agents communicate, understand each other’s capabilities, and so on. It’s great to see more interoperability, along with MCP, emerging in the AI space right now, as this will define the future of most IT stacks.

Aaron Levie

139,591 görüntüleme • 1 yıl önce

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."
1:56

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 görüntüleme • 4 ay önce

What does the reputation model look like for agents? (alpha leak below) And how do we associate the proofs that we have about human beings with the agents who represent them? You may have heard of a process called KYC or Know Your Customer. That's very common with traditional financial applications and services. We have introduced a concept that we call KYA or Know Your Agent, which is a structured way to be able to express what model, how data was used in training, who the deployer is, what entities this agent instance is accountable back to, providing not only provenance but identity of the associated organization or entity. That's also another root of trust that we think about a lot: Enterprises and organizations tied back to things like their domains. To share a little bit of an alpha leak here, a product that we're excited to be rolling out in the next few weeks will allow our enterprise partners to more easily verify and prove the traits and capabilities of their teams as well as their counterparties. On the agent front, that makes it really easy to prove that an agent is acting on behalf of a given business or entity. We've already seen lawsuits where the absence of such technology has been a huge risk, such as with airlines that incorporate ChatGPT wrappers in their support pages. And then those AI enabled interactions end up making up plane tickets that don't exist and those airlines have to honor them. As small of an example as that might be, being able to prove agent accountability also unlocks a huge set of opportunities for use in enterprise for those agent to agent interactions. The Deep Trust Framework that our team has put together that we're excited to be bringing into a friendly SDK form in the next few weeks for some of our partners includes those reputation based capabilities, so how you can basically keep track of the interactions an agent has had, associate all of that to the entity to which they're accountable, and then that creates a sustainable reputation model for these agent to agent Interactions. Source: Billions CEO Evin McMullen evin speaking at House of Chimera Spaces Event Dec 3, 2025
2:06

What does the reputation model look like for agents? (alpha leak below) And how do we associate the proofs that we have about human beings with the agents who represent them? You may have heard of a process called KYC or Know Your Customer. That's very common with traditional financial applications and services. We have introduced a concept that we call KYA or Know Your Agent, which is a structured way to be able to express what model, how data was used in training, who the deployer is, what entities this agent instance is accountable back to, providing not only provenance but identity of the associated organization or entity. That's also another root of trust that we think about a lot: Enterprises and organizations tied back to things like their domains. To share a little bit of an alpha leak here, a product that we're excited to be rolling out in the next few weeks will allow our enterprise partners to more easily verify and prove the traits and capabilities of their teams as well as their counterparties. On the agent front, that makes it really easy to prove that an agent is acting on behalf of a given business or entity. We've already seen lawsuits where the absence of such technology has been a huge risk, such as with airlines that incorporate ChatGPT wrappers in their support pages. And then those AI enabled interactions end up making up plane tickets that don't exist and those airlines have to honor them. As small of an example as that might be, being able to prove agent accountability also unlocks a huge set of opportunities for use in enterprise for those agent to agent interactions. The Deep Trust Framework that our team has put together that we're excited to be bringing into a friendly SDK form in the next few weeks for some of our partners includes those reputation based capabilities, so how you can basically keep track of the interactions an agent has had, associate all of that to the entity to which they're accountable, and then that creates a sustainable reputation model for these agent to agent Interactions. Source: Billions CEO Evin McMullen evin speaking at House of Chimera Spaces Event Dec 3, 2025

Billions Network

68,503 görüntüleme • 9 ay önce

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

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

Johnny

27,179 görüntüleme • 1 yıl önce

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:
49:42

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:

Dan Shipper 📧

67,958 görüntüleme • 5 ay önce

gm! If you missed yesterday's space, here is the clip that you can listen explaining why Agent NFTs are important and future of NFTs. Also here is the TL;DR Agentic NFTs as productive assets. An NFT can own an AI agent's shared memory, tools, websites, and products it has built. Selling the NFT transfers the entire business/agent state to the new owner. ERC-8257 for tool-gating. CodinCowboy and ryan is working on the standard where agents register tools on-chain and access is gated by NFT ownership. That component that tells an agent "you need this NFT to use this tool" creating a market for exclusive tools. Use case: anyone can publish a tool and restrict it (e.g., "only Normies agents can call this"), letting tool value flow back to the gating NFT. Normies community fit. Normies API has served ~500M requests in 3 months, with 100+ community-built tools/games. ERC-8257 will let them build gated games, rewards, and skills exclusively for Normie agent holders. Why Normies is "agent-ready"? - Because everything is fully on-chain, metadata, ERCs, binding transaction. So the project is highly composable. My take on this topic: So far holding an NFT giving access to community, discord and merch. What we are doing with Normies is to give access to a business, tools, skills that agents can use effectively and be part of the economy layer of agentic future. Imagine someone builds a tool that does really 100% successful trading and only gates that skill to Normie Agents, and at some point you will only need a Normie NFT which has binding with the agent and access all these skills, tools. Future is now, Normies are the builders.
6:08

gm! If you missed yesterday's space, here is the clip that you can listen explaining why Agent NFTs are important and future of NFTs. Also here is the TL;DR Agentic NFTs as productive assets. An NFT can own an AI agent's shared memory, tools, websites, and products it has built. Selling the NFT transfers the entire business/agent state to the new owner. ERC-8257 for tool-gating. CodinCowboy and ryan is working on the standard where agents register tools on-chain and access is gated by NFT ownership. That component that tells an agent "you need this NFT to use this tool" creating a market for exclusive tools. Use case: anyone can publish a tool and restrict it (e.g., "only Normies agents can call this"), letting tool value flow back to the gating NFT. Normies community fit. Normies API has served ~500M requests in 3 months, with 100+ community-built tools/games. ERC-8257 will let them build gated games, rewards, and skills exclusively for Normie agent holders. Why Normies is "agent-ready"? - Because everything is fully on-chain, metadata, ERCs, binding transaction. So the project is highly composable. My take on this topic: So far holding an NFT giving access to community, discord and merch. What we are doing with Normies is to give access to a business, tools, skills that agents can use effectively and be part of the economy layer of agentic future. Imagine someone builds a tool that does really 100% successful trading and only gates that skill to Normie Agents, and at some point you will only need a Normie NFT which has binding with the agent and access all these skills, tools. Future is now, Normies are the builders.

serc

14,066 görüntüleme • 3 ay önce

The Fastest Growing Quant Repo On GitHub: Build Your Own Army Of Autonomous AI Trading Agents getting your hands on the fastest growing trading repository on github is like finding the keys to a vault that never stops printing. most people think they need a math degree to build these things but i am going to show you how a kid from a bedroom can build an empire of autonomous agents the repo was private for months while i perfected the internal logic and now it is back for anyone who wants to stop getting liquidated. you have to wonder why someone would give away the exact code that runs their entire trading business for free but the answer is simpler than you might think i believe code is the great equalizer and if we all have the tools we can finally beat the institutions at their own game. once you realize that the institutions are just using better code than you then the path forward becomes very clear the core of this system is an army of specialized ai agents that handle every single aspect of a professional trading desk. we have a strategy agent that executes the main logic while the risk agent sits over its shoulder to make sure you never lose more than you planned most traders think one bot is enough but the real secret to 2026 trading is having an entire team of ai agents that talk to each other. what happens when your sentiment agent sees a crash coming but your strategy agent is still trying to go long is where most people get wrecked that is exactly where the focus agent and the compliance agent come in to keep the whole system from blowing up your account. by separating these duties into different files you create a system that is robust enough to handle the wildest market conditions imaginable i have been testing every major model from claude to deepseek to see which one actually understands the nuances of the crypto markets. grock is the newest addition to the models folder because the performance we are seeing is finally starting to match the hype you might be wondering how you can possibly manage all these files if you have never written a line of python in your life. there is a specific way to use these models that allows you to vibe code your way to a functional trading desk without a computer science degree if you can copy a folder structure and follow a basic readme then you already have everything you need to start building. the barrier to entry has officially been destroyed by ai and now the only thing left is your willingness to iterate everything lives inside the src folder because organization is the difference between a bot that prints and a bot that crashes. the models folder is where we swap out the brains of the operation whenever a newer and faster llm hits the market to keep us ahead of the curve there is a hidden danger in just copying code without understanding the underlying risk agent logic. if you do not understand how the base agent connects to the exchange then you are just one api error away from a zero balance or a failed execution checking the env example and setting up your keys correctly is the first step to making sure your agents actually have the power to execute. this setup phase is the foundation that everything else is built upon so you cannot afford to be lazy here we have specific agents for every niche including whale watching and sentiment analysis to give you an edge that manual traders can never have. the listing arbitrage agent and the funding agent are there to capture those small inefficiencies that add up over time these agents are not just pieces of code they are employees that never sleep and never let their emotions get in the way of a trade. i spent hundreds of thousands on developers before i realized i could just build these systems myself with the help of ai code is the only thing that does not panic when the market starts dropping or get greedy when things are going up. once you automate your first strategy and see it execute without you being there you will finally understand what true freedom looks like i challenge you to pull this code and start building your own agents because the infrastructure is already there for you to use. you do not need to be a pro coder to start but you do need to be a builder who is ready to ship and iterate every single day the world is changing fast and the people who embrace autonomous trading agents are the ones who will be left standing when the dust settles. i will keep updating the github and shipping new features because the mission is to make sure every trader has the chance to automate their success if you want to join this revolution then go ahead and star the repo so you can follow along as we build out the future of finance. we are just getting started and the agents are only going to get smarter and more efficient from here on out
5:00

The Fastest Growing Quant Repo On GitHub: Build Your Own Army Of Autonomous AI Trading Agents getting your hands on the fastest growing trading repository on github is like finding the keys to a vault that never stops printing. most people think they need a math degree to build these things but i am going to show you how a kid from a bedroom can build an empire of autonomous agents the repo was private for months while i perfected the internal logic and now it is back for anyone who wants to stop getting liquidated. you have to wonder why someone would give away the exact code that runs their entire trading business for free but the answer is simpler than you might think i believe code is the great equalizer and if we all have the tools we can finally beat the institutions at their own game. once you realize that the institutions are just using better code than you then the path forward becomes very clear the core of this system is an army of specialized ai agents that handle every single aspect of a professional trading desk. we have a strategy agent that executes the main logic while the risk agent sits over its shoulder to make sure you never lose more than you planned most traders think one bot is enough but the real secret to 2026 trading is having an entire team of ai agents that talk to each other. what happens when your sentiment agent sees a crash coming but your strategy agent is still trying to go long is where most people get wrecked that is exactly where the focus agent and the compliance agent come in to keep the whole system from blowing up your account. by separating these duties into different files you create a system that is robust enough to handle the wildest market conditions imaginable i have been testing every major model from claude to deepseek to see which one actually understands the nuances of the crypto markets. grock is the newest addition to the models folder because the performance we are seeing is finally starting to match the hype you might be wondering how you can possibly manage all these files if you have never written a line of python in your life. there is a specific way to use these models that allows you to vibe code your way to a functional trading desk without a computer science degree if you can copy a folder structure and follow a basic readme then you already have everything you need to start building. the barrier to entry has officially been destroyed by ai and now the only thing left is your willingness to iterate everything lives inside the src folder because organization is the difference between a bot that prints and a bot that crashes. the models folder is where we swap out the brains of the operation whenever a newer and faster llm hits the market to keep us ahead of the curve there is a hidden danger in just copying code without understanding the underlying risk agent logic. if you do not understand how the base agent connects to the exchange then you are just one api error away from a zero balance or a failed execution checking the env example and setting up your keys correctly is the first step to making sure your agents actually have the power to execute. this setup phase is the foundation that everything else is built upon so you cannot afford to be lazy here we have specific agents for every niche including whale watching and sentiment analysis to give you an edge that manual traders can never have. the listing arbitrage agent and the funding agent are there to capture those small inefficiencies that add up over time these agents are not just pieces of code they are employees that never sleep and never let their emotions get in the way of a trade. i spent hundreds of thousands on developers before i realized i could just build these systems myself with the help of ai code is the only thing that does not panic when the market starts dropping or get greedy when things are going up. once you automate your first strategy and see it execute without you being there you will finally understand what true freedom looks like i challenge you to pull this code and start building your own agents because the infrastructure is already there for you to use. you do not need to be a pro coder to start but you do need to be a builder who is ready to ship and iterate every single day the world is changing fast and the people who embrace autonomous trading agents are the ones who will be left standing when the dust settles. i will keep updating the github and shipping new features because the mission is to make sure every trader has the chance to automate their success if you want to join this revolution then go ahead and star the repo so you can follow along as we build out the future of finance. we are just getting started and the agents are only going to get smarter and more efficient from here on out

Moon Dev

24,312 görüntüleme • 7 ay önce

$SERV is the Fiverr/Shopify for AI agents. OpenServ provides a platform and marketplace to create, find, and employ AI agents. Here's why it can be a leading Agent marketplace and is undervalued compared to where it can go. ———————————————————— To put this in perspective, we will take this from the top down. Let's look at the valuation mismatch. → Shopify: $140B → Fiverr: $1.2B → $SERV: $37M AI agent platforms can completely replace these businesses. Why? → Can automate operations (i.e. store setup, inventory management, and customer support autonomously, etc.) → Agents can hyper-personalize the shopping experience → Store owners can own their data and have more control → Marketplaces are a cheaper/faster solution AI agents make things more convenient by performing tasks autonomously. They are inevitable. ———————————————————— The AI Agent market is projected to reach ~$50B by 2030. So the potential is MASSIVE. What makes me so bullish on $SERV specifically? There are 3 things: 1️⃣ The Tech They are targeting Web 2 businesses. This gives the platform the most upside potential imo, both in terms of adoption and valuation. These are some noteworthy highlights: → No-code AI Agent builder (anyone can build) → Builders can generate income using agents → ANY agent can cooperate with ANY agent through SERVs platform → Offers multi-agent collaboration, while allowing for human input/customization This sets them apart from other crypto-centric AI agent marketplaces. OpenServ allows you to create a team of agents to carry out complex tasks, all while automating the process and packaging their solution for Web 2 businesses. Simple tool, easy execution, and limitless productivity. Which business/individual wouldn't want to do MORE in LESS time at a rate MUCH LESS than solutions already available? ———————————————————— 2️⃣ The Team They have a stacked team. → Founders: Experience in businesses & startups → CTO: 20+ years of experience in ML/AI → CFO: ex-JP Morgan VP → CMO: ex-IBM AI & Blockchain Marketing Director Within the last few weeks/months, they have added a UI/UX designer, 4 more devs, and more devs + a product manager coming. You could have the best tech but the team is what determines its success. In this case, the team has the knowledge/experience to see this through. They have been building for a year and the progress made is a good sign of what's to come. ———————————————————— 3️⃣ The Tokenomics A percentage of transaction volume on the platform will be used to buy back and burn the $SERV token. This creates and maintains buy-side pressure and demand. To put that in perspective, Fiverr & Upwork had a combined transaction volume of $5B. The demand for Agents wont slow down anytime soon. Demand for AI agents will translate into demand for the token. I love deflation. It's simple, clean, and effective. ———————————————————— ➡️ Final Thoughts I've held on tight to my $SERV bag because the platform is launching in Q1. This will mark the beginning of their journey to the top. Agents are inevitable. Integration with Web 2 businesses is inevitable. And the platform launch is coming as alt szn is kicking off. The stars are aligning. At the same time, AI companies are already showing interest in the platform. Developers lead to more users, bringing monetization opportunities, which brings more developers, and so on. A powerfully designed flywheel. This is a new and exciting sector. I expect interest and liquidity to be focused on AI Agents and the infrastructure around them. Max opportunity is right here in this sector.
2:59

$SERV is the Fiverr/Shopify for AI agents. OpenServ provides a platform and marketplace to create, find, and employ AI agents. Here's why it can be a leading Agent marketplace and is undervalued compared to where it can go. ———————————————————— To put this in perspective, we will take this from the top down. Let's look at the valuation mismatch. → Shopify: $140B → Fiverr: $1.2B → $SERV: $37M AI agent platforms can completely replace these businesses. Why? → Can automate operations (i.e. store setup, inventory management, and customer support autonomously, etc.) → Agents can hyper-personalize the shopping experience → Store owners can own their data and have more control → Marketplaces are a cheaper/faster solution AI agents make things more convenient by performing tasks autonomously. They are inevitable. ———————————————————— The AI Agent market is projected to reach ~$50B by 2030. So the potential is MASSIVE. What makes me so bullish on $SERV specifically? There are 3 things: 1️⃣ The Tech They are targeting Web 2 businesses. This gives the platform the most upside potential imo, both in terms of adoption and valuation. These are some noteworthy highlights: → No-code AI Agent builder (anyone can build) → Builders can generate income using agents → ANY agent can cooperate with ANY agent through SERVs platform → Offers multi-agent collaboration, while allowing for human input/customization This sets them apart from other crypto-centric AI agent marketplaces. OpenServ allows you to create a team of agents to carry out complex tasks, all while automating the process and packaging their solution for Web 2 businesses. Simple tool, easy execution, and limitless productivity. Which business/individual wouldn't want to do MORE in LESS time at a rate MUCH LESS than solutions already available? ———————————————————— 2️⃣ The Team They have a stacked team. → Founders: Experience in businesses & startups → CTO: 20+ years of experience in ML/AI → CFO: ex-JP Morgan VP → CMO: ex-IBM AI & Blockchain Marketing Director Within the last few weeks/months, they have added a UI/UX designer, 4 more devs, and more devs + a product manager coming. You could have the best tech but the team is what determines its success. In this case, the team has the knowledge/experience to see this through. They have been building for a year and the progress made is a good sign of what's to come. ———————————————————— 3️⃣ The Tokenomics A percentage of transaction volume on the platform will be used to buy back and burn the $SERV token. This creates and maintains buy-side pressure and demand. To put that in perspective, Fiverr & Upwork had a combined transaction volume of $5B. The demand for Agents wont slow down anytime soon. Demand for AI agents will translate into demand for the token. I love deflation. It's simple, clean, and effective. ———————————————————— ➡️ Final Thoughts I've held on tight to my $SERV bag because the platform is launching in Q1. This will mark the beginning of their journey to the top. Agents are inevitable. Integration with Web 2 businesses is inevitable. And the platform launch is coming as alt szn is kicking off. The stars are aligning. At the same time, AI companies are already showing interest in the platform. Developers lead to more users, bringing monetization opportunities, which brings more developers, and so on. A powerfully designed flywheel. This is a new and exciting sector. I expect interest and liquidity to be focused on AI Agents and the infrastructure around them. Max opportunity is right here in this sector.

Chill

34,174 görüntüleme • 1 yıl önce