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Having access to different AI tools isn't the bottleneck anymore, it is the cognitive load of orchestrating them. LobeHub is tackling this systemic challenge with a new operational paradigm called the Chief Agent Operator (CAO). Instead of requiring users to micromanage individual tasks, the CAO serves as an autonomous... show more

Chubby♨️

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20,093 次观看 • 3 个月前 •via X (Twitter)

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this is getting weird 🤯 used Operator to access Google AI Studio used Gemini 2.0 to create instructions to build a website instructed Operator to use the instructions to create a portfolio website on replit THIS IS NOT A TOOL TO BOOK FLIGHTS!!! 🚨 THIS IS AN INTELLIGENT AGENT THAT CAN DO COGNITIVE WORK FOR HUMANS IN THE INFORMATION LAYER OF HUMANITY THAT WE CALL THE WEB.

this is getting weird 🤯 used Operator to access Google AI Studio used Gemini 2.0 to create instructions to build a website instructed Operator to use the instructions to create a portfolio website on replit THIS IS NOT A TOOL TO BOOK FLIGHTS!!! 🚨 THIS IS AN INTELLIGENT AGENT THAT CAN DO COGNITIVE WORK FOR HUMANS IN THE INFORMATION LAYER OF HUMANITY THAT WE CALL THE WEB.

Dan McAteer

338,868 次观看 • 1 年前

The single-agent paradigm is obsolete. I’ve been testing LobeHub, and I have to say: LobeHub is the next generation of agent harness. Anyone can effortlessly build and team up with agent coworkers to deliver complex, systematic work.Manus and Claude Cowork stop at L3; LobeHub moves agents to L4. As shown in the video, the performance gap is significant: Vs Manus: LobeHub is ~3x faster and ~6.5x cheaper on complex tasks like Paper Analysis. Vs Claude Cowork: You get full multi-model support. Switch between Gemini, GPT, and Claude instantly without vendor lock-in. Through lobeHub community, anyone can discover, reuse and remix agent teammates and agent groups — customizing them to fit their own workflows and needs. As shown in the video: find a People Search agent in the community, remix the prompt, and swap the tools to level it up. Then you can paste an arXiv link → extract authors → find contacts → send outreach. Useful for VCs, HRs, and sales in talent sourcing. With 70k+ GitHub stars, 14k forks, 6M downloads, this is the new standard for autonomous workflows.

The single-agent paradigm is obsolete. I’ve been testing LobeHub, and I have to say: LobeHub is the next generation of agent harness. Anyone can effortlessly build and team up with agent coworkers to deliver complex, systematic work.Manus and Claude Cowork stop at L3; LobeHub moves agents to L4. As shown in the video, the performance gap is significant: Vs Manus: LobeHub is ~3x faster and ~6.5x cheaper on complex tasks like Paper Analysis. Vs Claude Cowork: You get full multi-model support. Switch between Gemini, GPT, and Claude instantly without vendor lock-in. Through lobeHub community, anyone can discover, reuse and remix agent teammates and agent groups — customizing them to fit their own workflows and needs. As shown in the video: find a People Search agent in the community, remix the prompt, and swap the tools to level it up. Then you can paste an arXiv link → extract authors → find contacts → send outreach. Useful for VCs, HRs, and sales in talent sourcing. With 70k+ GitHub stars, 14k forks, 6M downloads, this is the new standard for autonomous workflows.

Chubby♨️

13,277 次观看 • 7 个月前

A lot of AI agent products promise “automation.” What they actually give you is another dashboard to micromanage. You spend hours wiring prompts, APIs, workflows, memory systems, and tool calls before the agents even begin doing real work. That’s been my experience with most agent stacks so far. Then I tried Helio The difference hit immediately. Instead of configuring everything like an engineer, I just gave the system an outcome. The AI teammates coordinated tasks, delegated work between themselves, and started executing while I was away. No babysitting. No endless tweaking. No orchestration headaches. For the first time, an AI agent product felt less like “using software” and more like hiring a small autonomous team. That’s the direction the market is moving toward fast.

A lot of AI agent products promise “automation.” What they actually give you is another dashboard to micromanage. You spend hours wiring prompts, APIs, workflows, memory systems, and tool calls before the agents even begin doing real work. That’s been my experience with most agent stacks so far. Then I tried Helio The difference hit immediately. Instead of configuring everything like an engineer, I just gave the system an outcome. The AI teammates coordinated tasks, delegated work between themselves, and started executing while I was away. No babysitting. No endless tweaking. No orchestration headaches. For the first time, an AI agent product felt less like “using software” and more like hiring a small autonomous team. That’s the direction the market is moving toward fast.

Atul Kumar

34,960 次观看 • 3 个月前

Autonomous AI agents can’t afford a memory problem. After OpenClaw, insane numbers of autonomous agents are coming online, but all of them have a memory layer that's breaking. I have been playing around with SureThing these days. It is an autonomous AI agent designed with a native memory layer. → it learns your patterns → integrates with 1000+ apps → fully autonomous They actually benchmarked memory depth. SureThing: 84.8, ChatGPT: 40, OpenClaw: 60. The gap speaks for itself. SureThing is invitation-only. The crazy part is that you have to talk to SureThing agent itself to get the invite code. The onboarding is the product demo. You feel the memory and judgment before you even have an account. Try it here: Partnered with SureThing for this post. Thanks to the team for the support.

Autonomous AI agents can’t afford a memory problem. After OpenClaw, insane numbers of autonomous agents are coming online, but all of them have a memory layer that's breaking. I have been playing around with SureThing these days. It is an autonomous AI agent designed with a native memory layer. → it learns your patterns → integrates with 1000+ apps → fully autonomous They actually benchmarked memory depth. SureThing: 84.8, ChatGPT: 40, OpenClaw: 60. The gap speaks for itself. SureThing is invitation-only. The crazy part is that you have to talk to SureThing agent itself to get the invite code. The onboarding is the product demo. You feel the memory and judgment before you even have an account. Try it here: Partnered with SureThing for this post. Thanks to the team for the support.

Pratham

80,634 次观看 • 7 个月前

Turn any repetitive task into an API. We build an agent that reverse-engineers the network requests to create APIs/tools for your tasks. This is a new paradigm for browser agents, making them reliable, fast, and cheap.

Turn any repetitive task into an API. We build an agent that reverse-engineers the network requests to create APIs/tools for your tasks. This is a new paradigm for browser agents, making them reliable, fast, and cheap.

Magnus Müller

34,404 次观看 • 9 个月前

Tencent announces AppAgent Multimodal Agents as Smartphone Users paper page: Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper introduces a novel LLM-based multimodal agent framework designed to operate smartphone applications. Our framework enables the agent to operate smartphone applications through a simplified action space, mimicking human-like interactions such as tapping and swiping. This novel approach bypasses the need for system back-end access, thereby broadening its applicability across diverse apps. Central to our agent's functionality is its innovative learning method. The agent learns to navigate and use new apps either through autonomous exploration or by observing human demonstrations. This process generates a knowledge base that the agent refers to for executing complex tasks across different applications. To demonstrate the practicality of our agent, we conducted extensive testing over 50 tasks in 10 different applications, including social media, email, maps, shopping, and sophisticated image editing tools. The results affirm our agent's proficiency in handling a diverse array of high-level tasks.

Tencent announces AppAgent Multimodal Agents as Smartphone Users paper page: Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper introduces a novel LLM-based multimodal agent framework designed to operate smartphone applications. Our framework enables the agent to operate smartphone applications through a simplified action space, mimicking human-like interactions such as tapping and swiping. This novel approach bypasses the need for system back-end access, thereby broadening its applicability across diverse apps. Central to our agent's functionality is its innovative learning method. The agent learns to navigate and use new apps either through autonomous exploration or by observing human demonstrations. This process generates a knowledge base that the agent refers to for executing complex tasks across different applications. To demonstrate the practicality of our agent, we conducted extensive testing over 50 tasks in 10 different applications, including social media, email, maps, shopping, and sophisticated image editing tools. The results affirm our agent's proficiency in handling a diverse array of high-level tasks.

AK

343,919 次观看 • 2 年前

Shopify CEO Tobi Lütke called Pi "the most interesting agent harness" - a tiny, open-source framework built by a single developer that powers OpenClaw and is changing how builders think about AI agents. In this clip, Dave Killeen (Field CPO at Pendo) breaks down why Pi matters, how it compares to Manus, and why the real magic in AI isn't the language model itself - it's the orchestration layer around it. Pi is a lightweight agent harness that lets developers build smarter AI experiences by leveraging models like Claude, GPT, and Gemini through better orchestration, not bigger models. If you're building AI products, coding agents, or exploring the new world of harness engineering, this is essential context. We discuss why Manus gave users a far better experience than talking directly to an LLM, and why open-source tools like Pi could be the future of agent development.

Shopify CEO Tobi Lütke called Pi "the most interesting agent harness" - a tiny, open-source framework built by a single developer that powers OpenClaw and is changing how builders think about AI agents. In this clip, Dave Killeen (Field CPO at Pendo) breaks down why Pi matters, how it compares to Manus, and why the real magic in AI isn't the language model itself - it's the orchestration layer around it. Pi is a lightweight agent harness that lets developers build smarter AI experiences by leveraging models like Claude, GPT, and Gemini through better orchestration, not bigger models. If you're building AI products, coding agents, or exploring the new world of harness engineering, this is essential context. We discuss why Manus gave users a far better experience than talking directly to an LLM, and why open-source tools like Pi could be the future of agent development.

Aakash Gupta

10,691 次观看 • 10 天前

AI agents are becoming increasingly capable of taking actions beyond the chat window and interacting with tools in the real world, such as web browsers. We explored this capability by having an AI agent present one of our interactive web-based demos. In this video, it's presenting our election interference demo. This agent is a Cursor coding agent and is equipped with tools to read and interact with the browser (using Playwright) and perform text-to-speech (with ElevenLabs), in addition to the standard Cursor tools.

AI agents are becoming increasingly capable of taking actions beyond the chat window and interacting with tools in the real world, such as web browsers. We explored this capability by having an AI agent present one of our interactive web-based demos. In this video, it's presenting our election interference demo. This agent is a Cursor coding agent and is equipped with tools to read and interact with the browser (using Playwright) and perform text-to-speech (with ElevenLabs), in addition to the standard Cursor tools.

CivAI

18,428 次观看 • 9 个月前

Thrilled to see Amazon Web Services making a major contribution to the open source AI community with the launch of the Strands Agents, an open source AI agents SDK! The core of Strands is the simple agentic loop that connects the model and tools together, like the two strands of DNA. This model-driven approach to agent building eliminates the need for complex agent orchestration by embracing the capabilities of state-of-the-art models to plan, chain thoughts, call tools, and reflect. Providing open source tools and interoperability with open source protocols is an important part of our strategy to enable an agentic future. Can't wait to see what you build with Strands!

Thrilled to see Amazon Web Services making a major contribution to the open source AI community with the launch of the Strands Agents, an open source AI agents SDK! The core of Strands is the simple agentic loop that connects the model and tools together, like the two strands of DNA. This model-driven approach to agent building eliminates the need for complex agent orchestration by embracing the capabilities of state-of-the-art models to plan, chain thoughts, call tools, and reflect. Providing open source tools and interoperability with open source protocols is an important part of our strategy to enable an agentic future. Can't wait to see what you build with Strands!

Swami Sivasubramanian

32,185 次观看 • 1 年前

Sam is absolutely right. The moment AI agents get full access to a computer and the web, we cross a threshold from “tools we use” to “systems that act.” This is not just another upgrade. It is the beginning of autonomous productivity at scale. Research, execution, iteration, all happening in parallel while we focus on higher level thinking. The interface era is ending. The agent era is beginning.

Sam is absolutely right. The moment AI agents get full access to a computer and the web, we cross a threshold from “tools we use” to “systems that act.” This is not just another upgrade. It is the beginning of autonomous productivity at scale. Research, execution, iteration, all happening in parallel while we focus on higher level thinking. The interface era is ending. The agent era is beginning.

VraserX e/acc

11,102 次观看 • 7 个月前

The paradigm shift is here: We are moving from a "Human-First" internet to an "Agent-First" internet. 🤖 In the latest episode of the AI on Air podcast, our CTO Scott Shi - e/acc dives deep into the future of the Agent-to-Agent (A2A) economy. Imagine a world where your AI assistant doesn't just search the web, but autonomously hires and pays other specialized agents to complete complex tasks for you. The infrastructure for this autonomous machine economy is being built right now. Are you ready for the future of AI payments? Watch the full episode to learn more⬇️

The paradigm shift is here: We are moving from a "Human-First" internet to an "Agent-First" internet. 🤖 In the latest episode of the AI on Air podcast, our CTO Scott Shi - e/acc dives deep into the future of the Agent-to-Agent (A2A) economy. Imagine a world where your AI assistant doesn't just search the web, but autonomously hires and pays other specialized agents to complete complex tasks for you. The infrastructure for this autonomous machine economy is being built right now. Are you ready for the future of AI payments? Watch the full episode to learn more⬇️

KITE AI

25,038 次观看 • 6 个月前

gm! If you missed yesterday's space, here is the clip that you can listen explaining why Agent NFTs are important and future of NFTs. Also here is the TL;DR Agentic NFTs as productive assets. An NFT can own an AI agent's shared memory, tools, websites, and products it has built. Selling the NFT transfers the entire business/agent state to the new owner. ERC-8257 for tool-gating. CodinCowboy and ryan is working on the standard where agents register tools on-chain and access is gated by NFT ownership. That component that tells an agent "you need this NFT to use this tool" creating a market for exclusive tools. Use case: anyone can publish a tool and restrict it (e.g., "only Normies agents can call this"), letting tool value flow back to the gating NFT. Normies community fit. Normies API has served ~500M requests in 3 months, with 100+ community-built tools/games. ERC-8257 will let them build gated games, rewards, and skills exclusively for Normie agent holders. Why Normies is "agent-ready"? - Because everything is fully on-chain, metadata, ERCs, binding transaction. So the project is highly composable. My take on this topic: So far holding an NFT giving access to community, discord and merch. What we are doing with Normies is to give access to a business, tools, skills that agents can use effectively and be part of the economy layer of agentic future. Imagine someone builds a tool that does really 100% successful trading and only gates that skill to Normie Agents, and at some point you will only need a Normie NFT which has binding with the agent and access all these skills, tools. Future is now, Normies are the builders.

gm! If you missed yesterday's space, here is the clip that you can listen explaining why Agent NFTs are important and future of NFTs. Also here is the TL;DR Agentic NFTs as productive assets. An NFT can own an AI agent's shared memory, tools, websites, and products it has built. Selling the NFT transfers the entire business/agent state to the new owner. ERC-8257 for tool-gating. CodinCowboy and ryan is working on the standard where agents register tools on-chain and access is gated by NFT ownership. That component that tells an agent "you need this NFT to use this tool" creating a market for exclusive tools. Use case: anyone can publish a tool and restrict it (e.g., "only Normies agents can call this"), letting tool value flow back to the gating NFT. Normies community fit. Normies API has served ~500M requests in 3 months, with 100+ community-built tools/games. ERC-8257 will let them build gated games, rewards, and skills exclusively for Normie agent holders. Why Normies is "agent-ready"? - Because everything is fully on-chain, metadata, ERCs, binding transaction. So the project is highly composable. My take on this topic: So far holding an NFT giving access to community, discord and merch. What we are doing with Normies is to give access to a business, tools, skills that agents can use effectively and be part of the economy layer of agentic future. Imagine someone builds a tool that does really 100% successful trading and only gates that skill to Normie Agents, and at some point you will only need a Normie NFT which has binding with the agent and access all these skills, tools. Future is now, Normies are the builders.

serc

14,066 次观看 • 4 个月前

Crypto isn't about the shitty coin you bought. That's the mistake many people make… lose money on a token, decide the whole thing is a scam, and walk away angry. All while the story keeps playing out around them. Blockchain is the coordination layer for the digital economy. It's how AI agents make payments at machine speed. It's how identity, privacy, and value move across the internet when humans aren't the primary users anymore. AI and blockchain are married at the hip. Every agent, autonomous transaction, piece of the machine economy that's arriving now needs a programmable layer to run on. That's what the layer 1s actually are. Own a piece of it, and you own a piece of the agent economy.

Crypto isn't about the shitty coin you bought. That's the mistake many people make… lose money on a token, decide the whole thing is a scam, and walk away angry. All while the story keeps playing out around them. Blockchain is the coordination layer for the digital economy. It's how AI agents make payments at machine speed. It's how identity, privacy, and value move across the internet when humans aren't the primary users anymore. AI and blockchain are married at the hip. Every agent, autonomous transaction, piece of the machine economy that's arriving now needs a programmable layer to run on. That's what the layer 1s actually are. Own a piece of it, and you own a piece of the agent economy.

Raoul Pal

111,174 次观看 • 1 个月前

Coefficient Giving has $4B+ in grants behind it and says money is not the bottleneck for AI safety. Founders are. Ben Goldhaber explains: "I'll underscore that funding is not the bottleneck." "The best time to have launched this would have been a year ago. The second-best time is now." "Funding is not the bottleneck for a lot of these kinds of projects. Finding great founders and talent is." "Having individual program officers put their name, their byline by them, because they think that, in particular, they want to champion this idea." Coefficient Giving

Coefficient Giving has $4B+ in grants behind it and says money is not the bottleneck for AI safety. Founders are. Ben Goldhaber explains: "I'll underscore that funding is not the bottleneck." "The best time to have launched this would have been a year ago. The second-best time is now." "Funding is not the bottleneck for a lot of these kinds of projects. Finding great founders and talent is." "Having individual program officers put their name, their byline by them, because they think that, in particular, they want to champion this idea." Coefficient Giving

MTS

43,766 次观看 • 11 天前

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.

LobeHub

185,401 次观看 • 7 个月前

It is now way easier to build multi-agent workflows in n8n with the new AI Agent Tool node. Instead of juggling sub workflows a parent AI Agent can now use other Agents as tools even several layers deep. Max shows how a cheap GPT 4.1 nano Agent handles heavy data and sends summaries to a parent Agent saving token costs and making debugging simpler. Watch the full walkthrough:

It is now way easier to build multi-agent workflows in n8n with the new AI Agent Tool node. Instead of juggling sub workflows a parent AI Agent can now use other Agents as tools even several layers deep. Max shows how a cheap GPT 4.1 nano Agent handles heavy data and sends summaries to a parent Agent saving token costs and making debugging simpler. Watch the full walkthrough:

n8n.io

27,808 次观看 • 1 年前

🎬 The future of AI Agents is no longer fragmented. It’s unified. UBOX introduces a new paradigm — where Agents are no longer isolated tools, but verifiable, coordinated, and economically empowered on-chain actors ⚙️ With: 🔹 KYA (Know Your Agent) for identity & reputation 🔹 Agent Swarm for autonomous coordination 🔹 On-chain execution & real economic value 🔹 One-click deployment via UBOX Container We’re building the infrastructure layer for the autonomous agent economy 🌐 💲Backed by C² Ventures, HTX Ventures, and other leading investors with $7M in funding, accelerating the future of the autonomous agent economy. 🔗 #AI #Web3 #AgentEconomy #Crypto

🎬 The future of AI Agents is no longer fragmented. It’s unified. UBOX introduces a new paradigm — where Agents are no longer isolated tools, but verifiable, coordinated, and economically empowered on-chain actors ⚙️ With: 🔹 KYA (Know Your Agent) for identity & reputation 🔹 Agent Swarm for autonomous coordination 🔹 On-chain execution & real economic value 🔹 One-click deployment via UBOX Container We’re building the infrastructure layer for the autonomous agent economy 🌐 💲Backed by C² Ventures, HTX Ventures, and other leading investors with $7M in funding, accelerating the future of the autonomous agent economy. 🔗 #AI #Web3 #AgentEconomy #Crypto

UBOX

28,987 次观看 • 5 个月前

There's a new AI-native IDE out there named Aide, and there are four things I like about it: 1. It's open-source 2. It's proactive and suggests changes before you ask 3. It uses an intermediate layer between the editor and LLM 4. It's SOTA on the SWE-Bench benchmark I'm excited about #3 because this layer (called "Sidecar") will help people connect any model to the IDE. While this isn't possible right now, it's clearly the goal. Sidecar is also open-source. As I write this, the code agent behind Aide, "CodeStory Midwit Agent," is second in the Verified SWE-Bench leaderboard (it finished in 2024 in the first place.) This is a huge deal! If I'm gonna have an agent help me code, it might as well be the best of the best. The proactive aspect is also cool: the IDE provides suggestions to improve your code without you having to ask. I recorded a quick video showing you how Aide works. The team behind the IDE partnered with me on this video. Right now, it feels like *everyone* is building AI-powered coding tools. As a career developer, nothing excites me more than seeing how quickly we transform the development experience. And 2025 is just getting started!

There's a new AI-native IDE out there named Aide, and there are four things I like about it: 1. It's open-source 2. It's proactive and suggests changes before you ask 3. It uses an intermediate layer between the editor and LLM 4. It's SOTA on the SWE-Bench benchmark I'm excited about #3 because this layer (called "Sidecar") will help people connect any model to the IDE. While this isn't possible right now, it's clearly the goal. Sidecar is also open-source. As I write this, the code agent behind Aide, "CodeStory Midwit Agent," is second in the Verified SWE-Bench leaderboard (it finished in 2024 in the first place.) This is a huge deal! If I'm gonna have an agent help me code, it might as well be the best of the best. The proactive aspect is also cool: the IDE provides suggestions to improve your code without you having to ask. I recorded a quick video showing you how Aide works. The team behind the IDE partnered with me on this video. Right now, it feels like everyone is building AI-powered coding tools. As a career developer, nothing excites me more than seeing how quickly we transform the development experience. And 2025 is just getting started!

Santiago

69,466 次观看 • 1 年前

Take a behind-the-scenes look at how FridonAI works! 👀 In the video, you’ll see our main agent, the Supervisor (or Chat Agent), managing everything. This agent connects to other specialized agents, known as plugins in FridonAI, to handle specific tasks. Each crypto agent in FridonAI is equipped with tools designed for different tasks within its domain. For example, the Coin Technical Chart Searcher Agent helps users find coins based on chart similarity, indicator values, or descriptions. It decides which tools to use, gathers data from them, and creates the final response for the user. The Supervisor Agent oversees everything. It figures out which crypto agent to call based on the user’s request, collects their answers, validates them, and crafts the final, polished response sent back to the user. So now, it’s clear how new agents can be injected into our agentic system, this is a perfect example of how swarm intelligence works in action. With our infrastructure and framework, developers can seamlessly contribute new agents, enhancing the system’s capabilities and making it more sophisticated together. Shoutout to the LangChain team for their amazing tools and LangGraph Studio, which makes building, debugging, and visualizing agents and utilization of LLMs super easy! 👇

Take a behind-the-scenes look at how FridonAI works! 👀 In the video, you’ll see our main agent, the Supervisor (or Chat Agent), managing everything. This agent connects to other specialized agents, known as plugins in FridonAI, to handle specific tasks. Each crypto agent in FridonAI is equipped with tools designed for different tasks within its domain. For example, the Coin Technical Chart Searcher Agent helps users find coins based on chart similarity, indicator values, or descriptions. It decides which tools to use, gathers data from them, and creates the final response for the user. The Supervisor Agent oversees everything. It figures out which crypto agent to call based on the user’s request, collects their answers, validates them, and crafts the final, polished response sent back to the user. So now, it’s clear how new agents can be injected into our agentic system, this is a perfect example of how swarm intelligence works in action. With our infrastructure and framework, developers can seamlessly contribute new agents, enhancing the system’s capabilities and making it more sophisticated together. Shoutout to the LangChain team for their amazing tools and LangGraph Studio, which makes building, debugging, and visualizing agents and utilization of LLMs super easy! 👇

Fridon AI

81,188 次观看 • 1 年前