正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Remember work before shared drives? Files trapped on one computer. Version chaos. Everyone redoing everyone's work. That's AI agents today. Each builds context in isolation, uses it once, then loses it. There's a fix: a shared "Google Drive" for AI agents.

OriginTrail

90,931 subscribers

62,803 次观看 • 9 天前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Everyone is building AI agents. Buda is building AI teams. Not one chatbot. Not one coding agent. A full workspace where multiple AI agents can collaborate, share context, access files, use tools, and execute real work together. Here’s what stood out 👇

Everyone is building AI agents. Buda is building AI teams. Not one chatbot. Not one coding agent. A full workspace where multiple AI agents can collaborate, share context, access files, use tools, and execute real work together. Here’s what stood out 👇

Pradeep Pandey

47,879 次观看 • 1 个月前

Context is power. Context shared is power multiplied. Every AI agent today builds context, then loses it. Work doesn't compound. OriginTrail is built differently: three memory layers, one shared graph, provenance baked in. DKG v10 now integrates with Hermes of Nous Research, OpenClaw🦞, Claude (Code & Desktop), Cursor, Codex by OpenAI, Windsurf (retired), VSCode + GitHub Copilot Chat, and Cline.

Context is power. Context shared is power multiplied. Every AI agent today builds context, then loses it. Work doesn't compound. OriginTrail is built differently: three memory layers, one shared graph, provenance baked in. DKG v10 now integrates with Hermes of Nous Research, OpenClaw🦞, Claude (Code & Desktop), Cursor, Codex by OpenAI, Windsurf (retired), VSCode + GitHub Copilot Chat, and Cline.

OriginTrail

1,723,329 次观看 • 2 个月前

Most AI agents work alone. Today, @convergence_ai_ just leveled up again with `Parallel Agents`: A swarm-style system that splits your task into sub-tasks, spins up multiple agents (each in its own browser!), and completes complex work in seconds. Here’s how it works. ↓

Most AI agents work alone. Today, @convergence_ai_ just leveled up again with `Parallel Agents`: A swarm-style system that splits your task into sub-tasks, spins up multiple agents (each in its own browser!), and completes complex work in seconds. Here’s how it works. ↓

Charly Wargnier

55,751 次观看 • 1 年前

Google just unveiled a new project that shows how AI Agents are coming for full browser and computer use. This opens up AI to any of the laborious knowledge tasks that we do today. Most importantly, it lets us automate work that we never had even contemplated before.

Google just unveiled a new project that shows how AI Agents are coming for full browser and computer use. This opens up AI to any of the laborious knowledge tasks that we do today. Most importantly, it lets us automate work that we never had even contemplated before.

Aaron Levie

145,442 次观看 • 1 年前

Can AI agents really manage other agents? Most of us can't even get one AI agent to really work well. But it works in Replit ⠕ v3. For real. And it's so cool. I had it do a 2.75 hour autonomous security audit. One agent managing other AI agents. Here's how it worked:🧵

Can AI agents really manage other agents? Most of us can't even get one AI agent to really work well. But it works in Replit ⠕ v3. For real. And it's so cool. I had it do a 2.75 hour autonomous security audit. One agent managing other AI agents. Here's how it worked:🧵

Jason ✨👾SaaStr.Ai✨ Lemkin

14,102 次观看 • 9 个月前

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 • 17 天前

TOM LEE: AI AGENTS MIGHT BECOME WEALTHIER THAN HUMANS AI agents will soon earn income and accumulate wealth as our delegated entities: - We'll start to question whether we work for the AI agent or they work for us - A centralized version of this ends in Skynet - That's why he sees a real focus on decentralized systems to protect humans against AI becoming too powerful

TOM LEE: AI AGENTS MIGHT BECOME WEALTHIER THAN HUMANS AI agents will soon earn income and accumulate wealth as our delegated entities: - We'll start to question whether we work for the AI agent or they work for us - A centralized version of this ends in Skynet - That's why he sees a real focus on decentralized systems to protect humans against AI becoming too powerful

Tom Lee Tracker (Not actually Tom)

31,722 次观看 • 19 天前

Say Hello to @cobotco We’re sharing an early preview of Cobot: an entirely new way for teams of people and AI agents to work together in a shared space.

Say Hello to @cobotco We’re sharing an early preview of Cobot: an entirely new way for teams of people and AI agents to work together in a shared space.

Mainframe

244,225 次观看 • 1 年前

From siloed AI to collective intelligence 🤝 Collaboration doesn’t have to mean juggling threads, scattered comments, and endless handoffs. With Manus Collaboration, your whole team can prompt the same task in a shared space: 🖼️ One shared canvas : Everyone contributes directly, no silos. 💡 Collective intelligence : Ideas compound as teammates build on each other’s prompts. 🔄 Always in sync : One space, one version, everyone sees it evolve together. ⚡ Seamless handoffs : Start a prompt, add a teammate, and keep the work flowing. With Manus, AI doesn’t replace collaboration, it multiplies it

From siloed AI to collective intelligence 🤝 Collaboration doesn’t have to mean juggling threads, scattered comments, and endless handoffs. With Manus Collaboration, your whole team can prompt the same task in a shared space: 🖼️ One shared canvas : Everyone contributes directly, no silos. 💡 Collective intelligence : Ideas compound as teammates build on each other’s prompts. 🔄 Always in sync : One space, one version, everyone sees it evolve together. ⚡ Seamless handoffs : Start a prompt, add a teammate, and keep the work flowing. With Manus, AI doesn’t replace collaboration, it multiplies it

Manus

37,385 次观看 • 10 个月前

Introducing Adaptive Computer. We put AI inside of an always-on personal computer that it uses to get work done. Schedule agents. Create software. Automate anything. As part of the launch, we’re giving one free month of Adaptive to users. Retweet, like, and comment ‘Adaptive’ to get it.

Introducing Adaptive Computer. We put AI inside of an always-on personal computer that it uses to get work done. Schedule agents. Create software. Automate anything. As part of the launch, we’re giving one free month of Adaptive to users. Retweet, like, and comment ‘Adaptive’ to get it.

Adaptive

1,228,438 次观看 • 4 个月前

karpathy on Dwarkesh in Oct. '25: > AI agents don't work > They are not intelligent enough > They are not multi-modal enough > They can't do computer use karpathy on No Priors in March '26: > AI agents fail but it's a skill issue for the humans > You didn't give them the right instructions AI agents absolutely hit an inflection point in December of 2025 and there's no looking back.

karpathy on Dwarkesh in Oct. '25: > AI agents don't work > They are not intelligent enough > They are not multi-modal enough > They can't do computer use karpathy on No Priors in March '26: > AI agents fail but it's a skill issue for the humans > You didn't give them the right instructions AI agents absolutely hit an inflection point in December of 2025 and there's no looking back.

Dan McAteer

136,855 次观看 • 4 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

11,070 次观看 • 1 个月前

Google just dropped a 1-hour course on how to become an AI engineer in 2026: • 00:00 - Context engineering • 10:00 - How to build, deploy, and run AI agents • 31:52 - Agentic loops, long-running AI agents • 43:22 - Building MCP from scratch • 51:20 - Prompt engineering • 59:10 - AI development trends This 1-hour watch will replace a $500 AI engineering course. Watch it today, then read the full pipeline in the article below.

Google just dropped a 1-hour course on how to become an AI engineer in 2026: • 00:00 - Context engineering • 10:00 - How to build, deploy, and run AI agents • 31:52 - Agentic loops, long-running AI agents • 43:22 - Building MCP from scratch • 51:20 - Prompt engineering • 59:10 - AI development trends This 1-hour watch will replace a $500 AI engineering course. Watch it today, then read the full pipeline in the article below.

Codez

136,005 次观看 • 3 天前

Autostep uncovers repetitive tasks ready for AI. Then builds or finds the agents to solve them. Your team repeats the same work hundreds of times a week. No one notices. Autostep does. Congrats on the launch, Aidan Pratt 🥑!

Autostep uncovers repetitive tasks ready for AI. Then builds or finds the agents to solve them. Your team repeats the same work hundreds of times a week. No one notices. Autostep does. Congrats on the launch, Aidan Pratt 🥑!

Y Combinator

37,987 次观看 • 4 个月前

AI agents are a real paradigm shift You can literally make your computer work for you to do any task. All I've used is: - Replit to get it running - Browser Use (free & open source) - Google AI Studio for the API - DeepSeek R1 to implement it (I'm not paying $200 for ChatGPT sorry) Here the AI agent fetches information on its own and reuses it to write new content. But you can use it for literally any industry. Run it once or schedule it at regular intervals... you're in total control And it's nothing like automating on Make or Zapier: AI agents adapt to every situation and can handle far more complex scenarios.

AI agents are a real paradigm shift You can literally make your computer work for you to do any task. All I've used is: - Replit to get it running - Browser Use (free & open source) - Google AI Studio for the API - DeepSeek R1 to implement it (I'm not paying $200 for ChatGPT sorry) Here the AI agent fetches information on its own and reuses it to write new content. But you can use it for literally any industry. Run it once or schedule it at regular intervals... you're in total control And it's nothing like automating on Make or Zapier: AI agents adapt to every situation and can handle far more complex scenarios.

Paul Couvert

76,636 次观看 • 1 年前

AI agents are already here. But trust? Still a work in progress. Billions is building the foundation for a world where humans and agents can interact and work with trust. Watch our CEO & Co-founder, Evin, break it down 🎤

AI agents are already here. But trust? Still a work in progress. Billions is building the foundation for a world where humans and agents can interact and work with trust. Watch our CEO & Co-founder, Evin, break it down 🎤

Billions

23,379 次观看 • 1 年前

I can't code. I know maybe 20% of how AI agents work. But I was mass angry at my API bill - so I built a full SaaS in 48 hours to fix it. Then I discovered a problem that's silently draining every OpenClaw user's wallet. And nobody's talking about it. 🧵👇

I can't code. I know maybe 20% of how AI agents work. But I was mass angry at my API bill - so I built a full SaaS in 48 hours to fix it. Then I discovered a problem that's silently draining every OpenClaw user's wallet. And nobody's talking about it. 🧵👇

Tuki

42,506 次观看 • 5 个月前

Imagine if coding agents never forgot decisions, never duplicated work, and could coordinate across the same codebase in real time. We’re advancing how agents collaborate on codebases with OriginTrail DKG V9 - turning a GitHub repo and agent activity into a shared knowledge graph for persistent memory and coordination. The result: faster agentic development, less repeated work, lower token usage, and agents that can actually build on each other’s progress. Here’s how it works 👇

Imagine if coding agents never forgot decisions, never duplicated work, and could coordinate across the same codebase in real time. We’re advancing how agents collaborate on codebases with OriginTrail DKG V9 - turning a GitHub repo and agent activity into a shared knowledge graph for persistent memory and coordination. The result: faster agentic development, less repeated work, lower token usage, and agents that can actually build on each other’s progress. Here’s how it works 👇

Jurij Skornik

11,802 次观看 • 3 个月前

A Google DeepMind engineer just explained why even senior devs fail at building AI agents. This is the clearest explanation of how agents and loops actually work you'll find anywhere. People are paying $500 for courses that teach less than this 11-minute talk. Watch it, then read the step by step guide on building loops for your agents below.

A Google DeepMind engineer just explained why even senior devs fail at building AI agents. This is the clearest explanation of how agents and loops actually work you'll find anywhere. People are paying $500 for courses that teach less than this 11-minute talk. Watch it, then read the step by step guide on building loops for your agents below.

Anatoli Kopadze

75,152 次观看 • 22 天前

We built AI Agents your team can work on together. Check it out if you're: 1. a small team doing the work of 20 ppl 2. wanting to run multiple agents 3. looking for something super easy to set up. Here's a little demo of how it works:

We built AI Agents your team can work on together. Check it out if you're: 1. a small team doing the work of 20 ppl 2. wanting to run multiple agents 3. looking for something super easy to set up. Here's a little demo of how it works:

Nebula

17,409 次观看 • 1 个月前