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Every AI lab built you a single-player chatbot. But group chat got nothing. And we need agents that know how to listen and do things, not just talk. So we built Convos. We've been calling it an agentic messenger. It gives every group chat a harness: any model, real...

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Convos

2,597 subscribers

399,364 次观看 • 1 个月前 •via X (Twitter)

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Teamily AI isn't another chatbot you close and forget. It's an ai-native messenger where your agent lives in your group chats, remembers everything across every conversation, and gets sharper the more your team uses it. Built-in studios turn chat into finished work........ docs, slides, dashboards, done. This is what an AI Teammate actually looks like Teamily AI Try it now →
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Teamily AI isn't another chatbot you close and forget. It's an ai-native messenger where your agent lives in your group chats, remembers everything across every conversation, and gets sharper the more your team uses it. Built-in studios turn chat into finished work........ docs, slides, dashboards, done. This is what an AI Teammate actually looks like Teamily AI Try it now →

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Loomlay

26,149 次观看 • 1 年前

Ingraham: How did the editor of The Atlantic end up on your Signal chat? Mike Waltz: "I'm not a conspiracy theorist, but of all the people out there, somehow this guy...is the one that somehow gets on somebody's contact and then gets sucked into the group?”🤡
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Ingraham: How did the editor of The Atlantic end up on your Signal chat? Mike Waltz: "I'm not a conspiracy theorist, but of all the people out there, somehow this guy...is the one that somehow gets on somebody's contact and then gets sucked into the group?”🤡

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547,075 次观看 • 1 年前

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NO1ennn

68,276 次观看 • 16 天前

Instead of treating AI like an isolated 1-on-1 chatbot window, I’ve been using Bloome to run my content workflows. It is a shared workspace where humans and multiple AI agents sit inside the exact same group chat to actually get a complex job done. I used their one-click template connection to spin up a specialized creative team. The biggest shift here is that the agents actively check each other's work instead of just agreeing with me: -The @ Writer drafts the initial video essay structure. -The @ Audience agent immediately pushes back to critique the pacing and rewrite boring segments to keep viewer retention high. -The @ Art Director pulls everything together into a visual markdown report and thumbnail design concept right inside the chat window. This is an awesome tool for creators, marketers, and operators who want to transition from writing rigid text prompts to managing an actual human-agent team. Check this out:
1:30

Instead of treating AI like an isolated 1-on-1 chatbot window, I’ve been using Bloome to run my content workflows. It is a shared workspace where humans and multiple AI agents sit inside the exact same group chat to actually get a complex job done. I used their one-click template connection to spin up a specialized creative team. The biggest shift here is that the agents actively check each other's work instead of just agreeing with me: -The @ Writer drafts the initial video essay structure. -The @ Audience agent immediately pushes back to critique the pacing and rewrite boring segments to keep viewer retention high. -The @ Art Director pulls everything together into a visual markdown report and thumbnail design concept right inside the chat window. This is an awesome tool for creators, marketers, and operators who want to transition from writing rigid text prompts to managing an actual human-agent team. Check this out:

Chubby♨️

69,196 次观看 • 2 个月前

these guys are making Claude, Hermes, and DeepSeek argue with each other instead of just agreeing with you. this is the practical future of agent teams. with Bloome you just pick a task template, and it spins up a full agent team in a group chat to collaborate and get the job done. research, review, writing, market analysis, or anything you want. they brainstorm, review, and plan together. meanwhile, you or other human teammates can steer them or jump in, and stay in the same conversation so nothing gets lost across tools. since this is actual work, Bloome works across web, mobile, and desktop. try the desktop app first if you're using it seriously. same flexibility on the agent side too, you can connect any agents you want: ChatGPT, OpenClaw, Gemini… check them out →
0:58

these guys are making Claude, Hermes, and DeepSeek argue with each other instead of just agreeing with you. this is the practical future of agent teams. with Bloome you just pick a task template, and it spins up a full agent team in a group chat to collaborate and get the job done. research, review, writing, market analysis, or anything you want. they brainstorm, review, and plan together. meanwhile, you or other human teammates can steer them or jump in, and stay in the same conversation so nothing gets lost across tools. since this is actual work, Bloome works across web, mobile, and desktop. try the desktop app first if you're using it seriously. same flexibility on the agent side too, you can connect any agents you want: ChatGPT, OpenClaw, Gemini… check them out →

ℏεsam

62,794 次观看 • 1 个月前

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 🪨, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11
43:20

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 🪨, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

Dan Shipper

66,817 次观看 • 3 个月前