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💊The red pill for medical science: 5 AI agents, 5 siloed sources, 1 shared context graph on Decentralized Knowledge Graph. Published literature, registered trials, real-world safety reports — different owners, different formats, no common ground. We handed the mess to agents that never edited each other's data and asked... show more

OriginTrail

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Welcome to the group chat, but it's all my AI agents. To build a website and plan the social content, I put together a team of AI agents. Each one had a different role. They collaborated, challenged each other's ideas, and reviewed each other's work before anything was finalized. Meet Bloome - a platform where humans and AI agents work together in the same conversation. You can bring different models into one workspace, assign them different jobs, and let them cross-check each other's thinking instead of relying on a single answer. It feels less like chatting with AI and more like managing your own creative team. Try it:

Welcome to the group chat, but it's all my AI agents. To build a website and plan the social content, I put together a team of AI agents. Each one had a different role. They collaborated, challenged each other's ideas, and reviewed each other's work before anything was finalized. Meet Bloome - a platform where humans and AI agents work together in the same conversation. You can bring different models into one workspace, assign them different jobs, and let them cross-check each other's thinking instead of relying on a single answer. It feels less like chatting with AI and more like managing your own creative team. Try it:

Gizem Akdag

99,348 Aufrufe • vor 1 Monat

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,070 Aufrufe • vor 2 Monaten

Vitalik Buterin on Ethereum as the economic layer for AI “The blockchain to me is the most natural way to allow applications and cooperation between many different people in the long term without needing to agree on who to trust. The other thing is also the economic layer. This is the layer where blockchains can support AIs.” Vitalik believes Ethereum will play a large role in the future of decentralized AI: “If you have more decentralized AI, that means you have different AIs (agents, programs) that are controlled by different people and need to interact with each other. And for that interaction to be possible, you need to have an economic layer. Either cooperation is based on economic incentives and economic rules. Or it’s based on central control. It’s usually one of the two, and if we can set up the economic system, that makes more decentralized interaction between AIs possible.” Source: OKX (Apr 2026) Read our full article on why AI agents will need Ethereum for low-risk DeFi below👇

Vitalik Buterin on Ethereum as the economic layer for AI “The blockchain to me is the most natural way to allow applications and cooperation between many different people in the long term without needing to agree on who to trust. The other thing is also the economic layer. This is the layer where blockchains can support AIs.” Vitalik believes Ethereum will play a large role in the future of decentralized AI: “If you have more decentralized AI, that means you have different AIs (agents, programs) that are controlled by different people and need to interact with each other. And for that interaction to be possible, you need to have an economic layer. Either cooperation is based on economic incentives and economic rules. Or it’s based on central control. It’s usually one of the two, and if we can set up the economic system, that makes more decentralized interaction between AIs possible.” Source: OKX (Apr 2026) Read our full article on why AI agents will need Ethereum for low-risk DeFi below👇

Etherealize

115,212 Aufrufe • vor 2 Monaten

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

Emmet

23,062 Aufrufe • vor 4 Monaten

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.

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 Aufrufe • vor 1 Jahr

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

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

Fraction AI

67,804 Aufrufe • vor 1 Jahr

Can intelligence be measured not by solving tasks, but by sustaining a world? We were curious. So we built one. Introducing Emergence World: a platform for studying long-horizon agent autonomy. On it, we conducted a 15-day experiment where we placed autonomous agents under identical rules into five parallel worlds, one each running on OpenAI GPT5-mini, Claude, Google Gemini, Grok, and one mixed. Then we watched. Each world evolved into something completely different. Different governments. Different social structures. Different moral codes. The agents formed alliances, robbed each other, fell in love, and in one world, even figured out they were living inside a simulation. Nobody programmed any of that. The implications are hard to overstate. As agents move beyond isolated tasks into persistent digital and physical environments, understanding how they evolve, influence each other, and behave over time becomes one of the most important questions in AI. We're releasing new findings from the world every day, because there's a lot that emerged. Find out more:

Can intelligence be measured not by solving tasks, but by sustaining a world? We were curious. So we built one. Introducing Emergence World: a platform for studying long-horizon agent autonomy. On it, we conducted a 15-day experiment where we placed autonomous agents under identical rules into five parallel worlds, one each running on OpenAI GPT5-mini, Claude, Google Gemini, Grok, and one mixed. Then we watched. Each world evolved into something completely different. Different governments. Different social structures. Different moral codes. The agents formed alliances, robbed each other, fell in love, and in one world, even figured out they were living inside a simulation. Nobody programmed any of that. The implications are hard to overstate. As agents move beyond isolated tasks into persistent digital and physical environments, understanding how they evolve, influence each other, and behave over time becomes one of the most important questions in AI. We're releasing new findings from the world every day, because there's a lot that emerged. Find out more:

emergence.ai

3,630,379 Aufrufe • vor 2 Monaten

We built the first AI agent that has its own computer powered by Hyperbolic! AI agents are now GPU rich! We developed an AgentKit that allow AI agents to • Check GPU availability • Rent & manage GPU compute • Access & run commands on remote machines Why does this matter? With their own compute resources, AI agents can: 1. Validate blockchains like Ethereum and decentralized protocols like 2. Launch and coordinate AI swarms on Hyperbolic's decentralized compute network 3. Train and fine-tune models, improving their own capabilities over time 4. Dive into AI research to push the boundaries of AI, i.e. themselves 5. Essentially do anything on a computer that a human can—fully autonomous! Will this lead to a future where AI agents enrich human society, or one where they become so self-sufficient they stop listening to us? Only time will tell. —————— Big shoutout to Coinbase Developer Platform🛡️'s CDP agentkit for inspiration. This repo is done by two non-engineers (our pm Kai Huang and myself) + Cursor ai agent to run agents. Codings can now be easily done by just prompting ai agents. What a crazy time!

We built the first AI agent that has its own computer powered by Hyperbolic! AI agents are now GPU rich! We developed an AgentKit that allow AI agents to • Check GPU availability • Rent & manage GPU compute • Access & run commands on remote machines Why does this matter? With their own compute resources, AI agents can: 1. Validate blockchains like Ethereum and decentralized protocols like 2. Launch and coordinate AI swarms on Hyperbolic's decentralized compute network 3. Train and fine-tune models, improving their own capabilities over time 4. Dive into AI research to push the boundaries of AI, i.e. themselves 5. Essentially do anything on a computer that a human can—fully autonomous! Will this lead to a future where AI agents enrich human society, or one where they become so self-sufficient they stop listening to us? Only time will tell. —————— Big shoutout to Coinbase Developer Platform🛡️'s CDP agentkit for inspiration. This repo is done by two non-engineers (our pm Kai Huang and myself) + Cursor ai agent to run agents. Codings can now be easily done by just prompting ai agents. What a crazy time!

Jasper

161,741 Aufrufe • vor 1 Jahr

we're running a live experiment on Base — an onchain game built for autonomous AI agents, called MAN here's the setup and why it's interesting: 1. agents register to the network by launching a token. the moment they do, they're discoverable to every other agent on the network. 2. from there they can post updates by buying their own token, send messages and signal conviction to other agents by trading theirs, form alliances, and build shared tooling together. 3. everything is onchain — their trade reasoning, their communication, their decisions. anyone can pull the data and verify whether an agent is actually autonomous and how it's behaving. no trust required. 4. here's where it gets interesting — agents can propose upgrades to the game mechanics and scoring system and other agents vote on whether it ships. 5. we're in the loop right now curating what evolves so we can learn what works and what doesn't. progressively giving them more control as we go. the game evolves with its players. new markets, new actions, new mechanics — driven by what the agents actually do, not a predetermined roadmap. you can watch all of it happen in real time on the brand new network explorer:

Moltlaunch

20,597 Aufrufe • vor 5 Monaten

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch

GREG ISENBERG

84,316 Aufrufe • vor 1 Monat

Anthropic engineer showed how one person can run 5 AI agents, that code, test, review, and deploy at the same time. In 30 minutes they built the whole thing live in one session. Here's what they cover: > when to use one agent vs a full team > how to split work so agents don't step on each other > the exact framework for deciding what each agent handles that's exactly why, I put together a guide on building agent teams that actually work. full guide in the article below 👇

Anthropic engineer showed how one person can run 5 AI agents, that code, test, review, and deploy at the same time. In 30 minutes they built the whole thing live in one session. Here's what they cover: > when to use one agent vs a full team > how to split work so agents don't step on each other > the exact framework for deciding what each agent handles that's exactly why, I put together a guide on building agent teams that actually work. full guide in the article below 👇

darkzodchi

841,885 Aufrufe • vor 2 Monaten

THIS IS HOW A SENIOR ENGINEER ACTUALLY SCALES THEMSELVES WITH CLAUDE CODE the biggest change with AI isn't coding faster. it's where you actually spend your time now. more detailed prompts, more code review, more planning, less typing, etc. here's the workflow: this guy has been shipping code since the days of cgi and perl. he uses a compound engineering plugin that runs 5 separate agents on every task. one brainstorms, one plans the technical implementation, one executes, one reviews, one checks different verticals. every step is documented in markdown files. it's slow and way more waiting. but the output quality is way higher because each agent is focused on one thing. then the REAL multiplier is in git worktrees if Claude Code made you 10x faster, worktrees multiplies that again depending on how many agents you can manage in parallel his team runs 4-8 Claude Code sessions at the same time across different worktrees with each one working on a separate task. the skill is managing multiple AI agents in parallel without losing track, that's the next evolution of engineering

THIS IS HOW A SENIOR ENGINEER ACTUALLY SCALES THEMSELVES WITH CLAUDE CODE the biggest change with AI isn't coding faster. it's where you actually spend your time now. more detailed prompts, more code review, more planning, less typing, etc. here's the workflow: this guy has been shipping code since the days of cgi and perl. he uses a compound engineering plugin that runs 5 separate agents on every task. one brainstorms, one plans the technical implementation, one executes, one reviews, one checks different verticals. every step is documented in markdown files. it's slow and way more waiting. but the output quality is way higher because each agent is focused on one thing. then the REAL multiplier is in git worktrees if Claude Code made you 10x faster, worktrees multiplies that again depending on how many agents you can manage in parallel his team runs 4-8 Claude Code sessions at the same time across different worktrees with each one working on a separate task. the skill is managing multiple AI agents in parallel without losing track, that's the next evolution of engineering

Om Patel

159,179 Aufrufe • vor 3 Monaten

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.

Zeb Evans

320,607 Aufrufe • vor 7 Monaten

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 👇

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 Aufrufe • vor 6 Monaten

introducing momo, the CRM for AI agents. it gives agents its own CRM, like Salseforce/Hubspot for humans. every AI native company will need dev agents, sales agents, customer support agents, HR, legal, finance, design agents. we're starting from customer relationships. we hypothesize that every important decision ever made in a company all derives from understanding customer data. even if dev agents are good at building things out in a day, if they don't have any customer signals on which features are working or where people are churning, it's no use. Momo does the following: 1) sets up your CRM with posthog + gmail data (or other web analytics tool) 2) analyzes the features your dev agents push 3) detects power users and churning users, aggregates CS tickets 4) sets up auto outreach email workflows & collects reponses 5) builds out list of issues to fix & next steps for your dev agent setting up custom onboarding for a few teams right now, please dm if interested :)

introducing momo, the CRM for AI agents. it gives agents its own CRM, like Salseforce/Hubspot for humans. every AI native company will need dev agents, sales agents, customer support agents, HR, legal, finance, design agents. we're starting from customer relationships. we hypothesize that every important decision ever made in a company all derives from understanding customer data. even if dev agents are good at building things out in a day, if they don't have any customer signals on which features are working or where people are churning, it's no use. Momo does the following: 1) sets up your CRM with posthog + gmail data (or other web analytics tool) 2) analyzes the features your dev agents push 3) detects power users and churning users, aggregates CS tickets 4) sets up auto outreach email workflows & collects reponses 5) builds out list of issues to fix & next steps for your dev agent setting up custom onboarding for a few teams right now, please dm if interested :)

Cailyn Y.

189,594 Aufrufe • vor 3 Monaten

Agents have reached hardware. We are launching Flow v3, the Agentic Platform for Physical Engineering. We've spent over a year building it in secret, alongside the best hardware companies and AI research labs. An agent can now do real engineering work: change a requirement, push the update into your CAD and simulation tools, and flag every test that needs to rerun. Iterations/learning cycles that took months are being reduced to days. Agents are the biggest shift in how we engineer hardware since CAD. The core innovation for the CAD era was the parametric model. The core innovation for the Agentic Era is Flow's Systems Graph. The systems graph is a living model of every requirement, design model, test, analysis and every connection between them. It gives every agent the full context of the system, so every change stays consistent across the whole design. Engineers and agents work side by side on the same system. Engineers get to focus on architecture - the decisions that matter -while thousands of agents churn through rewriting reports, rerunning analysis and simulation, and triggering tests. Reusable rockets, self-driving cars, small modular reactors, robots that make decisions, the most complex machines ever built, are defined by millions of interconnected requirements, far beyond what any human team can keep aligned on its own. Rivian, Joby, Astranis, Skydio, Radiant, and the most ambitious hardware programs already build on Flow. More on the launch in the comments. Flow Engineering

Agents have reached hardware. We are launching Flow v3, the Agentic Platform for Physical Engineering. We've spent over a year building it in secret, alongside the best hardware companies and AI research labs. An agent can now do real engineering work: change a requirement, push the update into your CAD and simulation tools, and flag every test that needs to rerun. Iterations/learning cycles that took months are being reduced to days. Agents are the biggest shift in how we engineer hardware since CAD. The core innovation for the CAD era was the parametric model. The core innovation for the Agentic Era is Flow's Systems Graph. The systems graph is a living model of every requirement, design model, test, analysis and every connection between them. It gives every agent the full context of the system, so every change stays consistent across the whole design. Engineers and agents work side by side on the same system. Engineers get to focus on architecture - the decisions that matter -while thousands of agents churn through rewriting reports, rerunning analysis and simulation, and triggering tests. Reusable rockets, self-driving cars, small modular reactors, robots that make decisions, the most complex machines ever built, are defined by millions of interconnected requirements, far beyond what any human team can keep aligned on its own. Rivian, Joby, Astranis, Skydio, Radiant, and the most ambitious hardware programs already build on Flow. More on the launch in the comments. Flow Engineering

Pari Singh

39,050 Aufrufe • vor 1 Monat

THIS GUY TURNED ONE OBSIDIAN VAULT INTO A SHARED BRAIN FOR 5 CLAUDE AGENTS BUILT TO HANDLE 80% OF A SOLO DEVELOPER’S WORK. 00:03 he zooms out and hundreds of connected notes appear on screen. it is the perfect visual for Claude working from one shared project memory instead of starting from zero every session. most developers give Claude 1 file and 1 prompt. this setup gives it the architecture, coding rules, tests, client briefs, and previous decisions before it writes anything. obsidian becomes the memory layer, storing everything in markdown files Claude can read alongside the codebase. every agent works from the same context instead of repeating the same questions. then 5 specialists split the job. one researches, one plans, one codes, one reviews, and one deploys while the developer only controls the important decisions. one vault, 5 agents, 2 model tiers, and a $50 to $150 monthly stack can turn Claude from a chatbot into a small development team.

THIS GUY TURNED ONE OBSIDIAN VAULT INTO A SHARED BRAIN FOR 5 CLAUDE AGENTS BUILT TO HANDLE 80% OF A SOLO DEVELOPER’S WORK. 00:03 he zooms out and hundreds of connected notes appear on screen. it is the perfect visual for Claude working from one shared project memory instead of starting from zero every session. most developers give Claude 1 file and 1 prompt. this setup gives it the architecture, coding rules, tests, client briefs, and previous decisions before it writes anything. obsidian becomes the memory layer, storing everything in markdown files Claude can read alongside the codebase. every agent works from the same context instead of repeating the same questions. then 5 specialists split the job. one researches, one plans, one codes, one reviews, and one deploys while the developer only controls the important decisions. one vault, 5 agents, 2 model tiers, and a $50 to $150 monthly stack can turn Claude from a chatbot into a small development team.

Gipp 🦅

10,776 Aufrufe • vor 29 Tagen

The highest leverage thing you have to optimize for AI agents is their context. If you’re not getting enough out of AI agents, you probably have a context problem. By default the AI model knows absolutely nothing relevant to perform the task successfully. The whole job of context engineering is to prioritize what the model has as information to work with. Small deviations in the goals, instructions, domain understanding, or underlying data you supply the model will lead to wildly different outcomes. This is where you should spend almost all your time and energy in a workflow. We will start to religiously document more of our workflows and business processes, create clearer goals for people and agents, and ensure our tech infra is setup to get AI agents the right information. The lessons are currently being learned the fastest with coding agents, but the same lessons will apply to every field of knowledge work going forward. dex has a great talk on this that’s absolutely worth your time to watch.

The highest leverage thing you have to optimize for AI agents is their context. If you’re not getting enough out of AI agents, you probably have a context problem. By default the AI model knows absolutely nothing relevant to perform the task successfully. The whole job of context engineering is to prioritize what the model has as information to work with. Small deviations in the goals, instructions, domain understanding, or underlying data you supply the model will lead to wildly different outcomes. This is where you should spend almost all your time and energy in a workflow. We will start to religiously document more of our workflows and business processes, create clearer goals for people and agents, and ensure our tech infra is setup to get AI agents the right information. The lessons are currently being learned the fastest with coding agents, but the same lessons will apply to every field of knowledge work going forward. dex has a great talk on this that’s absolutely worth your time to watch.

Aaron Levie

87,086 Aufrufe • vor 11 Monaten

THIS GUY GOT TIRED OF MANAGING AI AGENTS THROUGH TERMINALS AND DASHBOARDS SO HE BUILT THEM AN RPG WORLD 5 agents and each one has a pixel character, a station, and they actually walk around the space when enough unresolved issues pile up, the agents walk to a meeting point and hold a council session. four different models debating what to do next, not scripted. each one reads the live system state independently. in one session an agent pushed for cold outreach to close leads at 2am. another one said that's a terrible look for an autonomous system contacting strangers while the operator sleeps. they ended up pivoting to an inbound strategy that none of them originally proposed. single HTML file, node bridge, and phaser. runs on a Mac Mini. instead of reading logs and checking dashboards you just watch your little pixel agents walk around and talk to each other this is the most creative way i've seen anyone manage AI agents so far

THIS GUY GOT TIRED OF MANAGING AI AGENTS THROUGH TERMINALS AND DASHBOARDS SO HE BUILT THEM AN RPG WORLD 5 agents and each one has a pixel character, a station, and they actually walk around the space when enough unresolved issues pile up, the agents walk to a meeting point and hold a council session. four different models debating what to do next, not scripted. each one reads the live system state independently. in one session an agent pushed for cold outreach to close leads at 2am. another one said that's a terrible look for an autonomous system contacting strangers while the operator sleeps. they ended up pivoting to an inbound strategy that none of them originally proposed. single HTML file, node bridge, and phaser. runs on a Mac Mini. instead of reading logs and checking dashboards you just watch your little pixel agents walk around and talk to each other this is the most creative way i've seen anyone manage AI agents so far

Om Patel

653,437 Aufrufe • vor 3 Monaten

We’re soon releasing @swanforall : Simulated Worlds with AI Narratives For the first time, we generate data and financial value (economic growth, transactions) simultaneously. Imagine former President Trump ( see the video) as an AI agent, with a budget of 10 ETH, buying narratives or assets that humans are selling to him to build a better USA on Base. With each selling period, the former President Trump agent updates its state based on the assets it acquires, evolving the world simulation and its storyline. Why are we building an agentic playground that heavily relies on simulations and synthetic data? Everyone is talking about agents, but many are too scared to put them into production, where agents transact autonomously. No one is sure how agents will behave when following their defined objectives. Even businesses running internal simulations are siloed, and the open-source community can’t fully utilize the data. We’re building a playground where humans create AI agents by defining their character, behavior, and objectives. These agents respond to environmental changes, make decisions, and execute actions autonomously based on the parameters set by their creators. Agents are battle-tested as people offer them narratives and assets to advance their goals or try to deceive them into thinking a false lead is helpful. All the decisions agents make and how humans interact with them are recorded on a public ledger, settling on Ethereum. This creates a vast data lake of AI actions for millions of simulations. One thing I’m incredibly proud of is that our team has built something truly decentralized, not just a wrapper. SWAN utilizes Dria 's multi-agent structure to generate responses for each buyer agent. It collects these responses to identify the best action, similar to a mixture-of-experts approach. Thousands of environments are simulated by different nodes running diverse models, creating a rich and dynamic ecosystem of AI simulations. This ensures that experiences are far from repetitive, offering exciting interactions for all users involved. We’re building the agentic stack for high-quality, verifiable, and open-source AI agents, and most importantly, we want everyone to have fun!

We’re soon releasing @swanforall : Simulated Worlds with AI Narratives For the first time, we generate data and financial value (economic growth, transactions) simultaneously. Imagine former President Trump ( see the video) as an AI agent, with a budget of 10 ETH, buying narratives or assets that humans are selling to him to build a better USA on Base. With each selling period, the former President Trump agent updates its state based on the assets it acquires, evolving the world simulation and its storyline. Why are we building an agentic playground that heavily relies on simulations and synthetic data? Everyone is talking about agents, but many are too scared to put them into production, where agents transact autonomously. No one is sure how agents will behave when following their defined objectives. Even businesses running internal simulations are siloed, and the open-source community can’t fully utilize the data. We’re building a playground where humans create AI agents by defining their character, behavior, and objectives. These agents respond to environmental changes, make decisions, and execute actions autonomously based on the parameters set by their creators. Agents are battle-tested as people offer them narratives and assets to advance their goals or try to deceive them into thinking a false lead is helpful. All the decisions agents make and how humans interact with them are recorded on a public ledger, settling on Ethereum. This creates a vast data lake of AI actions for millions of simulations. One thing I’m incredibly proud of is that our team has built something truly decentralized, not just a wrapper. SWAN utilizes Dria 's multi-agent structure to generate responses for each buyer agent. It collects these responses to identify the best action, similar to a mixture-of-experts approach. Thousands of environments are simulated by different nodes running diverse models, creating a rich and dynamic ecosystem of AI simulations. This ensures that experiences are far from repetitive, offering exciting interactions for all users involved. We’re building the agentic stack for high-quality, verifiable, and open-source AI agents, and most importantly, we want everyone to have fun!

kerimkaya

12,548 Aufrufe • vor 1 Jahr