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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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607,106 次观看 • 4 个月前 •via X (Twitter)

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300 agents can find 1,000 sources. that still doesn’t make the research good the interesting part starts when those sources are forced to check each other. in K3 graph-native setup: 300 agents → 140 linked sources → 100% traceable claims → 9 contradictions surfaced one source confirms a number. another challenges it. a third connects the same entity from a different angle. so instead of: source → summary you get: source → entity → overlap → contradiction → verification → graph every new source becomes a node. if 3–4 independent sources support the same claim, confidence goes up. if 2 sources disagree, the contradiction gets flagged. if a claim has only 1 supporting source, it stays weak. a normal research workflow could have quietly shipped those 9 contradictions as facts. that’s why the breakthrough isn’t just 300 agents running in parallel. full breakdown in the article below.

300 agents can find 1,000 sources. that still doesn’t make the research good the interesting part starts when those sources are forced to check each other. in K3 graph-native setup: 300 agents → 140 linked sources → 100% traceable claims → 9 contradictions surfaced one source confirms a number. another challenges it. a third connects the same entity from a different angle. so instead of: source → summary you get: source → entity → overlap → contradiction → verification → graph every new source becomes a node. if 3–4 independent sources support the same claim, confidence goes up. if 2 sources disagree, the contradiction gets flagged. if a claim has only 1 supporting source, it stays weak. a normal research workflow could have quietly shipped those 9 contradictions as facts. that’s why the breakthrough isn’t just 300 agents running in parallel. full breakdown in the article below.

Ricker

227,257 次观看 • 1 个月前

As soon as you try to run 2 agents on the same data, everything breaks: • Agent 1 reads some data and starts working. • Two seconds later, Agent 2 changes that same data. This is a problem. Agents will waste tokens on stale data, overwrite each other's work, and redo tasks the other has already finished. You can't prompt your way out of this mess. Everything old in software engineering is new again. We need proper coordination when running multiple agents. Here is a good open-source solution for this: Omnigraph is a graph database for context assembly and multi-agent coordination. Here is how it works: • It sets one shared place where multiple agents will work from • Each agent gets their own copy of the data • Agents write their changes to their copy • When an agent is done, it merges its changes back Three huge benefits from this model: 1. We know it works. We've been doing this for decades already. 2. No more lost information or agents stepping on each other's toes. 3. You get a complete trail of which agent did what and when. I recorded a video to show you a bit more about this. Here is the link to the Omnigraph GitHub repository:

As soon as you try to run 2 agents on the same data, everything breaks: • Agent 1 reads some data and starts working. • Two seconds later, Agent 2 changes that same data. This is a problem. Agents will waste tokens on stale data, overwrite each other's work, and redo tasks the other has already finished. You can't prompt your way out of this mess. Everything old in software engineering is new again. We need proper coordination when running multiple agents. Here is a good open-source solution for this: Omnigraph is a graph database for context assembly and multi-agent coordination. Here is how it works: • It sets one shared place where multiple agents will work from • Each agent gets their own copy of the data • Agents write their changes to their copy • When an agent is done, it merges its changes back Three huge benefits from this model: 1. We know it works. We've been doing this for decades already. 2. No more lost information or agents stepping on each other's toes. 3. You get a complete trail of which agent did what and when. I recorded a video to show you a bit more about this. Here is the link to the Omnigraph GitHub repository:

Santiago

29,268 次观看 • 21 天前

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 次观看 • 3 个月前

300 agents can still give you bad research. unless the sources can check each other. the interesting part is what happens when every source can see every other source. K3 swarm doesn't just dump research into 300 separate write-ups. • every source becomes a node. • shared entities become edges. • single-source claims stick out as weak. so instead of: `300 agents → 300 answers` you get: `300 agents → sources → links → one research graph` and that's where things start showing up that no individual agent was explicitly asked to find. the article's example ends with 140 linked sources, every claim traceable to its origin, and 9 contradictions surfaced instead of silently shipped as facts. esearch stops being a pile of tabs. it becomes a system where the sources check each other. Book this & read full breakdown below

300 agents can still give you bad research. unless the sources can check each other. the interesting part is what happens when every source can see every other source. K3 swarm doesn't just dump research into 300 separate write-ups. • every source becomes a node. • shared entities become edges. • single-source claims stick out as weak. so instead of: `300 agents → 300 answers` you get: `300 agents → sources → links → one research graph` and that's where things start showing up that no individual agent was explicitly asked to find. the article's example ends with 140 linked sources, every claim traceable to its origin, and 9 contradictions surfaced instead of silently shipped as facts. esearch stops being a pile of tabs. it becomes a system where the sources check each other. Book this & read full breakdown below

Ricker

32,618 次观看 • 1 个月前

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

11,180 次观看 • 4 个月前

300 agents sounds powerful until you realize they can create 44,850 possible agent-to-agent relationships. that’s the real scaling problem. 1 agent searches 10 pages. 10 agents can search 100. 300 agents can search thousands in parallel. but more agents also means more: • duplicate findings • shared sources • hidden relationships and if every agent returns its own isolated report, you’re left with 300 piles to reconcile manually. that’s why the graph matters. instead of: 300 agents → 300 reports you get: 300 agents → shared entities → edges → clusters → one connected research map 100 nodes already create up to 4,950 pairwise relationships. more agents give you more information. the graph is what stops that information from becoming noise. full breakdown in the article below ↓

300 agents sounds powerful until you realize they can create 44,850 possible agent-to-agent relationships. that’s the real scaling problem. 1 agent searches 10 pages. 10 agents can search 100. 300 agents can search thousands in parallel. but more agents also means more: • duplicate findings • shared sources • hidden relationships and if every agent returns its own isolated report, you’re left with 300 piles to reconcile manually. that’s why the graph matters. instead of: 300 agents → 300 reports you get: 300 agents → shared entities → edges → clusters → one connected research map 100 nodes already create up to 4,950 pairwise relationships. more agents give you more information. the graph is what stops that information from becoming noise. full breakdown in the article below ↓

Ricker

26,875 次观看 • 1 个月前

Palantir sells this to governments for millions. Someone put a version of it on GitHub for free. Palantir's job is to pull scattered company data into one model where every decision can be traced later. Semantica does that for AI agents. Each decision becomes an object in a graph, tied to the facts that produced it, each fact linked to its source. Six months later you can walk backwards from any answer. The model never touches the reasoning. It only reads messy text and pulls out entities. The rest runs on plain rules, so the same input gives the same explanation every time. Vector memory cannot do that. Contradicting facts overwrite each other silently. pip install semantica, pick a graph backend you already run, and your agents read it through the MCP server. MIT licensed, repo is semantica-agi/semantica

Palantir sells this to governments for millions. Someone put a version of it on GitHub for free. Palantir's job is to pull scattered company data into one model where every decision can be traced later. Semantica does that for AI agents. Each decision becomes an object in a graph, tied to the facts that produced it, each fact linked to its source. Six months later you can walk backwards from any answer. The model never touches the reasoning. It only reads messy text and pulls out entities. The rest runs on plain rules, so the same input gives the same explanation every time. Vector memory cannot do that. Contradicting facts overwrite each other silently. pip install semantica, pick a graph backend you already run, and your agents read it through the MCP server. MIT licensed, repo is semantica-agi/semantica

Yarchi

43,642 次观看 • 1 个月前

ANTHROPIC LEAKED A 5-AGENT SETUP THAT TURNS ONE QUESTION INTO A SOURCED REPORT you write one line and never open a tab - the fleet reads 40 sources and only the answer comes back. question → scope → 5 searchers → dedupe → writer → fact-check → report the scope agent turns a vague question into 5 angles that do not overlap - skip this and five agents fetch the same three articles. 5 searchers run in parallel with separate contexts - none of them sees what the others found, which is exactly why they do not converge on the same source. dedupe is code, not an agent - flatten, drop repeats, normalize URLs, zero tokens, instant. the writer only sees structured findings, never raw pages - it cannot cite something that was never verified because it never had access to it. the fact-checker reads the draft against the sources, not against itself - every claim without a matching source goes back, and only that section is rewritten. nothing reaches you until a second agent, with clean context, has tried to kill it. 12 minutes, 40 sources, one report where every line traces back to something real. save this and read the full graph engineering course below ↓

ANTHROPIC LEAKED A 5-AGENT SETUP THAT TURNS ONE QUESTION INTO A SOURCED REPORT you write one line and never open a tab - the fleet reads 40 sources and only the answer comes back. question → scope → 5 searchers → dedupe → writer → fact-check → report the scope agent turns a vague question into 5 angles that do not overlap - skip this and five agents fetch the same three articles. 5 searchers run in parallel with separate contexts - none of them sees what the others found, which is exactly why they do not converge on the same source. dedupe is code, not an agent - flatten, drop repeats, normalize URLs, zero tokens, instant. the writer only sees structured findings, never raw pages - it cannot cite something that was never verified because it never had access to it. the fact-checker reads the draft against the sources, not against itself - every claim without a matching source goes back, and only that section is rewritten. nothing reaches you until a second agent, with clean context, has tried to kill it. 12 minutes, 40 sources, one report where every line traces back to something real. save this and read the full graph engineering course below ↓

Hanako

59,030 次观看 • 1 个月前

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

115,453 次观看 • 5 个月前

Google just dropped a 1-hour course on AI engineering — from building your first agent to creating autonomous systems. 7:02 — Build your first agent 13:34 — What agents can actually do 23:08 — Context engineering 32:56 — Graph engineering 54:11 — Build a fully autonomous system One agent can save you hours. A thousand agents can transform how a team works. Prompts → Agents → Loops → Graphs Most people will stop halfway through and think they understand AI engineering. But the real value comes in the second half — where you learn to build systems that keep working as AI evolves. Same model. Same tokens. Completely different results. Watch it today. The full guide to loops and graphs is below. Bookmark it before you forget ↓

Google just dropped a 1-hour course on AI engineering — from building your first agent to creating autonomous systems. 7:02 — Build your first agent 13:34 — What agents can actually do 23:08 — Context engineering 32:56 — Graph engineering 54:11 — Build a fully autonomous system One agent can save you hours. A thousand agents can transform how a team works. Prompts → Agents → Loops → Graphs Most people will stop halfway through and think they understand AI engineering. But the real value comes in the second half — where you learn to build systems that keep working as AI evolves. Same model. Same tokens. Completely different results. Watch it today. The full guide to loops and graphs is below. Bookmark it before you forget ↓

ZaraAI

28,680 次观看 • 4 天前

300 agents shouldn’t be running all day. they should just appear for 28 minutes, finish the job, and disappear until next task the bigger an agent swarm gets, the less the individual agent matters. what matters is the system connecting them. 100 agents can create up to 4,950 possible pairwise relationships. 300 = 44,850. when a real signal appears, it spins up the workforce: 1 trigger → 100–300 Kimi agents → 5 live data feeds → up to 4,000 steps → verified artifact and that’s before adding: sources → claims → memory → tool calls → contradictions → retries so the architecture has to continuously compress all that activity into one shared state. more agents gives you more compute. the graph is what stops that compute from turning into noise.

300 agents shouldn’t be running all day. they should just appear for 28 minutes, finish the job, and disappear until next task the bigger an agent swarm gets, the less the individual agent matters. what matters is the system connecting them. 100 agents can create up to 4,950 possible pairwise relationships. 300 = 44,850. when a real signal appears, it spins up the workforce: 1 trigger → 100–300 Kimi agents → 5 live data feeds → up to 4,000 steps → verified artifact and that’s before adding: sources → claims → memory → tool calls → contradictions → retries so the architecture has to continuously compress all that activity into one shared state. more agents gives you more compute. the graph is what stops that compute from turning into noise.

Ricker

76,935 次观看 • 1 个月前

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 次观看 • 6 个月前

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

139,695 次观看 • 1 年前

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

67,922 次观看 • 1 年前

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

3,631,055 次观看 • 5 个月前

AI agents are powerful. They're also isolated. Switch fixes that. Here's the thing about most AI agents: they work alone. They don't see what your team is discussing. They don't share context across conversations. Every new chat is a fresh start. If you've ever tried to actually integrate an agent into your team's workflow, you know the friction. Then I found 𝗦𝘄𝗶𝘁𝗰𝗵. It brings AI agents into Slack, Teams, Discord, and Telegram as named participants. They sit in the same rooms as your team, with the same context, history, and participants. What I actually like about it: → Agents join as real participants in channels — not bots you have to Mention every time → Each room carries its own context, history, and rules → Works with Claude Code, OpenAI, LangChain, Google ADK, and custom agents → Open source and self-hostable. Running in minutes. No migration. No lock-in. Just agents where your team already works. Knowledge lives in the room, not in the agent. Switch keeps people, agents, decisions, and work history together so context survives handoffs. It's from Flint AI. Open source. Runs in minutes. 👉 Human + agent teams, actually working together. 📌 Download 𝗦𝘄𝗶𝘁𝗰𝗵 Console → 🚀𝗦𝘄𝗶𝘁𝗰𝗵 is live on Product Hunt: 🔄 Repost if you've ever wished your AI agent could just join the Slack channel. — #AI #Agents #Slack #Teams #Switch #ProductHunt

AI agents are powerful. They're also isolated. Switch fixes that. Here's the thing about most AI agents: they work alone. They don't see what your team is discussing. They don't share context across conversations. Every new chat is a fresh start. If you've ever tried to actually integrate an agent into your team's workflow, you know the friction. Then I found 𝗦𝘄𝗶𝘁𝗰𝗵. It brings AI agents into Slack, Teams, Discord, and Telegram as named participants. They sit in the same rooms as your team, with the same context, history, and participants. What I actually like about it: → Agents join as real participants in channels — not bots you have to Mention every time → Each room carries its own context, history, and rules → Works with Claude Code, OpenAI, LangChain, Google ADK, and custom agents → Open source and self-hostable. Running in minutes. No migration. No lock-in. Just agents where your team already works. Knowledge lives in the room, not in the agent. Switch keeps people, agents, decisions, and work history together so context survives handoffs. It's from Flint AI. Open source. Runs in minutes. 👉 Human + agent teams, actually working together. 📌 Download 𝗦𝘄𝗶𝘁𝗰𝗵 Console → 🚀𝗦𝘄𝗶𝘁𝗰𝗵 is live on Product Hunt: 🔄 Repost if you've ever wished your AI agent could just join the Slack channel. — #AI #Agents #Slack #Teams #Switch #ProductHunt

Anuj

337,509 次观看 • 1 个月前

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 次观看 • 1 年前

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

20,683 次观看 • 8 个月前

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

85,083 次观看 • 4 个月前

Introducing Ego2Web from Google DeepMind and UNC Chapel Hill, accepted to #CVPR2026. AI agents can browse the web. But can they act based on what you see? Existing benchmarks focus only on web interaction while ignoring the real world. Ego2Web bridges egocentric video perception and web execution, enabling agents that can see through first-person video, understand real-world context, and take actions on the web grounded in the egocentric video. This opens a path toward AI assistants that operate seamlessly across physical and digital environments. We hope Ego2Web serves as an important step for building more capable, perception-driven agents. 🧵👇

Introducing Ego2Web from Google DeepMind and UNC Chapel Hill, accepted to #CVPR2026. AI agents can browse the web. But can they act based on what you see? Existing benchmarks focus only on web interaction while ignoring the real world. Ego2Web bridges egocentric video perception and web execution, enabling agents that can see through first-person video, understand real-world context, and take actions on the web grounded in the egocentric video. This opens a path toward AI assistants that operate seamlessly across physical and digital environments. We hope Ego2Web serves as an important step for building more capable, perception-driven agents. 🧵👇

Shoubin Yu @ COLM26

46,273 次观看 • 6 个月前