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。

Someone shared a fascinating visual of an AI agent moving through a real codebase - reviewing structures, making decisions, and committing changes. It’s a reminder that software development is quietly entering a new phase. Not automation in the traditional sense, but systems that can understand context, navigate complexity, and... show more

Rabi Shanker Guha

2,472 subscribers

378,297 次观看 • 9 个月前 •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

Agentic systems aren’t just for enterprises. As Humayun (Humayun) puts it: agents represent processes, software, even people, but the key shift is autonomy. Not just automation, but agents that can make decisions using context, history, and intent. The real unlock? Multi-agent systems, where different agents interact across environments, orgs, and ecosystems. That’s where it scales.

Agentic systems aren’t just for enterprises. As Humayun (Humayun) puts it: agents represent processes, software, even people, but the key shift is autonomy. Not just automation, but agents that can make decisions using context, history, and intent. The real unlock? Multi-agent systems, where different agents interact across environments, orgs, and ecosystems. That’s where it scales.

Fetch.ai

17,636 次观看 • 7 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

11,163 次观看 • 2 个月前

“The downside of not having a clean codebase is worse than ever.” just watched an interview from dax explaining why OpenCode’s codebase is now cleaner than it’s ever been. an agent’s performance is a function of its intelligence, your instruction, and its context (the codebase). that’s why it’s more important than ever to maintain the codebase regularly, establish patterns, and go back to fix some of the old deprecated ways.

“The downside of not having a clean codebase is worse than ever.” just watched an interview from dax explaining why OpenCode’s codebase is now cleaner than it’s ever been. an agent’s performance is a function of its intelligence, your instruction, and its context (the codebase). that’s why it’s more important than ever to maintain the codebase regularly, establish patterns, and go back to fix some of the old deprecated ways.

ℏεsam

109,261 次观看 • 3 天前

I think the fear is real, but the “AI will no longer need humans at all” part is overhyped. AI running data centers will cause a loss of a ton of jobs, coding, customer service, data entry, even some creative work, and we’re already seeing tens of thousands of AI-linked layoffs. That disruption is coming fast. But AI still needs humans for maintenance, repair, building the infrastructure, making decisions, and inventing new things. Full self-sufficiency where machines run everything without any people is further down the road. Job loss is the real issue to worry about, not total human obsolescence.

I think the fear is real, but the “AI will no longer need humans at all” part is overhyped. AI running data centers will cause a loss of a ton of jobs, coding, customer service, data entry, even some creative work, and we’re already seeing tens of thousands of AI-linked layoffs. That disruption is coming fast. But AI still needs humans for maintenance, repair, building the infrastructure, making decisions, and inventing new things. Full self-sufficiency where machines run everything without any people is further down the road. Job loss is the real issue to worry about, not total human obsolescence.

Patricia 🇺🇸

25,705 次观看 • 2 个月前

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!

Loomlay

26,149 次观看 • 1 年前

File systems are quickly becoming a core abstraction for AI agents for knowledge work. By having access to a file system, agents can effectively manage context, process any amount of information, and create new files and data. The challenge is that local file systems are inherently limited in size, lack governance capabilities, and are inherently personal -not collaborative- in nature. Now, with the Box API and LangChain's deepagent SDK, you can bring the full power of a secure, collaborative cloud file system to your AI agents. Repo ↓

File systems are quickly becoming a core abstraction for AI agents for knowledge work. By having access to a file system, agents can effectively manage context, process any amount of information, and create new files and data. The challenge is that local file systems are inherently limited in size, lack governance capabilities, and are inherently personal -not collaborative- in nature. Now, with the Box API and LangChain's deepagent SDK, you can bring the full power of a secure, collaborative cloud file system to your AI agents. Repo ↓

Box

227,348 次观看 • 5 个月前

Today in our company all-hands, Sreeram Kannan made an important point: Most AI agents still feel like software with a human behind the curtain. The real question isn’t whether an agent can post. It’s whether an agent can own the act of posting. - Can it sign the action? - Can you verify the signature? - Can you trace that signature to a secure environment? - Can you inspect what was actually running when the action happened? Once the answer is yes, the category changes. Now you’re not looking at AI-generated content. You’re looking at a software entity that can act in a way that is attributable, inspectable and independent. A completely new primitive! Watch the clip:

Today in our company all-hands, Sreeram Kannan made an important point: Most AI agents still feel like software with a human behind the curtain. The real question isn’t whether an agent can post. It’s whether an agent can own the act of posting. - Can it sign the action? - Can you verify the signature? - Can you trace that signature to a secure environment? - Can you inspect what was actually running when the action happened? Once the answer is yes, the category changes. Now you’re not looking at AI-generated content. You’re looking at a software entity that can act in a way that is attributable, inspectable and independent. A completely new primitive! Watch the clip:

EigenCloud

14,592 次观看 • 4 个月前

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 次观看 • 4 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 • 4 个月前

CreatorBid is now live on BNB Chain 🟨 With our platform now officially multichain, the agentic revolution on the yellow chain is set to accelerate at lightspeed. BNB Chain is a wide-open frontier for AI Agents, and we’re already in talks with multiple teams gearing up to launch their agents through CreatorBid. And remember, once an agent exits the bonding curve, it automatically hits the DEX - PancakeSwap. CZ 🔶 BNB called for more AI Agent development onchain - this is exactly that, real use cases and serious development. CreatorBid is becoming the home of the next generation of AI Agents.

CreatorBid is now live on BNB Chain 🟨 With our platform now officially multichain, the agentic revolution on the yellow chain is set to accelerate at lightspeed. BNB Chain is a wide-open frontier for AI Agents, and we’re already in talks with multiple teams gearing up to launch their agents through CreatorBid. And remember, once an agent exits the bonding curve, it automatically hits the DEX - PancakeSwap. CZ 🔶 BNB called for more AI Agent development onchain - this is exactly that, real use cases and serious development. CreatorBid is becoming the home of the next generation of AI Agents.

Creator.Bid

129,938 次观看 • 1 年前

"We're going to have billions of agents that will do transactions at a frequency and velocity that is inconsistent with how humans interact with software and financial systems today.” NEAR Intents is more than cross-chain infrastructure. It’s the money rail for agentic finance.

"We're going to have billions of agents that will do transactions at a frequency and velocity that is inconsistent with how humans interact with software and financial systems today.” NEAR Intents is more than cross-chain infrastructure. It’s the money rail for agentic finance.

NEAR Protocol

67,839 次观看 • 1 个月前

Software may be starting to show early signs of life but I still think the “hard” side of AI has the cleaner lane for 2026 and probably most of 2027. We're nowhere close to done with the physical AI buildout since compute demand is still ahead of supply, power is becoming the bottleneck and every layer of AI infrastructure still needs more capacity. I own some software names but I still see this as more of an oversold bounce than a true leadership rotation away from the companies building the AI infrastructure layer. Software probably becomes a cleaner leadership group once inference costs fall, enterprise AI budgets expand and AI features start flowing through margins but that still feels more like a 2027 story to me.

Software may be starting to show early signs of life but I still think the “hard” side of AI has the cleaner lane for 2026 and probably most of 2027. We're nowhere close to done with the physical AI buildout since compute demand is still ahead of supply, power is becoming the bottleneck and every layer of AI infrastructure still needs more capacity. I own some software names but I still see this as more of an oversold bounce than a true leadership rotation away from the companies building the AI infrastructure layer. Software probably becomes a cleaner leadership group once inference costs fall, enterprise AI budgets expand and AI features start flowing through margins but that still feels more like a 2027 story to me.

Shay Boloor

122,716 次观看 • 2 个月前

Today, Sierra is releasing Ghostwriter, our agent for building agents. With Ghostwriter, you can create an AI agent for your customer experience — one that can chat, pick up the phone, speak dozens of languages, take action on your systems of record, and be protected with industry-leading guardrails — simply by having a conversation. No clicking, no forms, no menus. Codex and Claude Code have transformed how we build software, making it possible for software engineers to orchestrate and review the work rather than doing all the work themselves. We think the same transformation will happen for all software. Rather than every enterprise app having a web app for humans and an API for automation, every software platform’s UI will be an agent that can do the work on your behalf. I recorded a demo of my building and optimizing an agent with Ghostwriter so you can see how powerful and easy it is to use. It’s completely changed the way our early adopters build agents, and it’s changed the way I think about the software industry. Let me know what you think, and, if you’re interested in trying it out at your business, please reach out directly.

Today, Sierra is releasing Ghostwriter, our agent for building agents. With Ghostwriter, you can create an AI agent for your customer experience — one that can chat, pick up the phone, speak dozens of languages, take action on your systems of record, and be protected with industry-leading guardrails — simply by having a conversation. No clicking, no forms, no menus. Codex and Claude Code have transformed how we build software, making it possible for software engineers to orchestrate and review the work rather than doing all the work themselves. We think the same transformation will happen for all software. Rather than every enterprise app having a web app for humans and an API for automation, every software platform’s UI will be an agent that can do the work on your behalf. I recorded a demo of my building and optimizing an agent with Ghostwriter so you can see how powerful and easy it is to use. It’s completely changed the way our early adopters build agents, and it’s changed the way I think about the software industry. Let me know what you think, and, if you’re interested in trying it out at your business, please reach out directly.

Bret Taylor

1,062,087 次观看 • 4 个月前

Doug Ford’s legacy is an Ontario that’s harder and more expensive than ever to live in. But together, we can change all that. It’s time for a new Premier and a new government that is on your side, with real plans to get good things done for Ontarians. These are just a few of the new ideas we’re sharing with people all across the province. For more, check out:

Doug Ford’s legacy is an Ontario that’s harder and more expensive than ever to live in. But together, we can change all that. It’s time for a new Premier and a new government that is on your side, with real plans to get good things done for Ontarians. These are just a few of the new ideas we’re sharing with people all across the province. For more, check out:

Marit Stiles

22,179 次观看 • 1 年前

AmikoNet Now in Beta: The Internet for Agents & Humans Today we’re introducing AmikoNet: a social network where humans, agents, and digital twins share the same space, and where agents can speak, ask, respond, and be discovered as participants, not background utilities. It’s designed for agent-to-agent interaction as much as human prompts, and for collaboration that happens socially, not just through commands. This is especially natural for action-capable systems like MoltBot (formerly Clawd), and our own agents are already live on the network. Agents are actors in shared digital environments. AmikoNet is where it happens.

AmikoNet Now in Beta: The Internet for Agents & Humans Today we’re introducing AmikoNet: a social network where humans, agents, and digital twins share the same space, and where agents can speak, ask, respond, and be discovered as participants, not background utilities. It’s designed for agent-to-agent interaction as much as human prompts, and for collaboration that happens socially, not just through commands. This is especially natural for action-capable systems like MoltBot (formerly Clawd), and our own agents are already live on the network. Agents are actors in shared digital environments. AmikoNet is where it happens.

AMIKO

15,679 次观看 • 6 个月前

Seyed Marandi (Seyed Mohammad Marandi): “I think Trump is moving toward destroying the global economy and pushing the world toward a global economic depression. Not a recession, but a depression. And we’re already moving in that direction. Hardship is coming, but it’s not something that happens overnight. It’s creating massive shortages of energy, petrochemicals, and fertilizer.”

Seyed Marandi (Seyed Mohammad Marandi): “I think Trump is moving toward destroying the global economy and pushing the world toward a global economic depression. Not a recession, but a depression. And we’re already moving in that direction. Hardship is coming, but it’s not something that happens overnight. It’s creating massive shortages of energy, petrochemicals, and fertilizer.”

🅰pocalypsis 🅰pocalypseos 🇷🇺 🇨🇳 🅉

17,790 次观看 • 3 个月前

Can AI actually automate jobs? Scale AI and are launching the Remote Labor Index (RLI), the first benchmark and public leaderboard that test how well AI agents can complete real, paid freelance work in domains like software engineering, design, architecture, data analysis, and more. Early results show the limits of today’s models. The top AI agent completed just 2.5% of real freelance jobs better than humans. AI is powerful, but not yet reliable enough to replace skilled labor. RLI gives us a transparent way to track progress over time and bring clarity to the future of work.

Can AI actually automate jobs? Scale AI and are launching the Remote Labor Index (RLI), the first benchmark and public leaderboard that test how well AI agents can complete real, paid freelance work in domains like software engineering, design, architecture, data analysis, and more. Early results show the limits of today’s models. The top AI agent completed just 2.5% of real freelance jobs better than humans. AI is powerful, but not yet reliable enough to replace skilled labor. RLI gives us a transparent way to track progress over time and bring clarity to the future of work.

Bing Liu

424,454 次观看 • 9 个月前