Загрузка видео...

Не удалось загрузить видео

На главную

🚨 Announcing AutoBots - Recursively Self-Improving Agents (RSI) Super excited to launch our multi-LLM-based recursively self-improving agents We use DeepSeek Flash 4 for easy tasks and Fable 5 for super hard ones. The agents learn over time, becoming MORE efficient and smarter with each run We use these RSI...

bindureddy's profile picture

Bindu Reddy

330,093 subscribers

2,458,699 просмотров • 1 день назад •via X (Twitter)

Наука и технологииИгрыЗдоровье и велнес
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

We’re excited to launch the Codex app, a command center for building with agents. It gives you a focused space to manage multiple agents at once, run work in parallel, and collaborate with agents over long-running tasks.
4:21

We’re excited to launch the Codex app, a command center for building with agents. It gives you a focused space to manage multiple agents at once, run work in parallel, and collaborate with agents over long-running tasks.

OpenAI Developers

1,304,841 просмотров • 6 месяцев назад

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail
3:02

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

LangChain

33,192 просмотров • 1 год назад

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.
5:50

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.

Zeb Evans

320,607 просмотров • 7 месяцев назад

LangChain Academy is live! Our first course — Introduction to LangGraph — teaches you the in-and-outs of building a reliable AI agent. In this course, you’ll learn how to: 🛠️ Build agents with LangGraph's graph-based workflows 🔄 Use memory + human-in-the-loop for smarter, self-corrective agents 📚 Create your own AI assistant that can perform knowledge tasks Enroll now for free ➡️ Bring LangChain Academy to your company ➡️
2:36

LangChain Academy is live! Our first course — Introduction to LangGraph — teaches you the in-and-outs of building a reliable AI agent. In this course, you’ll learn how to: 🛠️ Build agents with LangGraph's graph-based workflows 🔄 Use memory + human-in-the-loop for smarter, self-corrective agents 📚 Create your own AI assistant that can perform knowledge tasks Enroll now for free ➡️ Bring LangChain Academy to your company ➡️

LangChain

105,458 просмотров • 1 год назад

Anthropic just released 3 workshops on how to actually use Fable 5 to its full potential: 00:00 - Full guide to Fable 5 19:02 - How Anthropic uses Fable 5 internally 44:46 - How to build effective agents with Fable those 3 stages replaced 20 articles on Fable 5 for me. give it 1-hour today and read how to build self-improving agents with Fable 5 in the article below.
57:24

Anthropic just released 3 workshops on how to actually use Fable 5 to its full potential: 00:00 - Full guide to Fable 5 19:02 - How Anthropic uses Fable 5 internally 44:46 - How to build effective agents with Fable those 3 stages replaced 20 articles on Fable 5 for me. give it 1-hour today and read how to build self-improving agents with Fable 5 in the article below.

Codez

53,637 просмотров • 26 дней назад

🌟Our latest LangChain Academy course – Deep Agents with LangGraph – is now live!🌟 Many agents today follow the same simple pattern: run in a loop, call tools. That architecture works well enough, but it breaks down as tasks get more complex. Today, companies of all sizes – from startups to large enterprises – are building their own Deep Agents. These agents dive deeper. They’re able to plan complex tasks and carry them out over longer time horizons. There are four key features that set Deep Agents apart from regular agents: 1. Planning – keeps agents on track 2. File system – allows agents to offload context 3. Sub-agents – act as focused specialists 4. Prompting – provides agents with detailed instructions Our latest LangChain Academy course, Deep Agents with LangGraph, shows you how to combine these pieces with LangGraph to orchestrate long-running, multi-agent workflows. Big thanks to community member Dmitry Labazkin for helping us shape this course with his contributions! Enroll for free ➡️
2:11

🌟Our latest LangChain Academy course – Deep Agents with LangGraph – is now live!🌟 Many agents today follow the same simple pattern: run in a loop, call tools. That architecture works well enough, but it breaks down as tasks get more complex. Today, companies of all sizes – from startups to large enterprises – are building their own Deep Agents. These agents dive deeper. They’re able to plan complex tasks and carry them out over longer time horizons. There are four key features that set Deep Agents apart from regular agents: 1. Planning – keeps agents on track 2. File system – allows agents to offload context 3. Sub-agents – act as focused specialists 4. Prompting – provides agents with detailed instructions Our latest LangChain Academy course, Deep Agents with LangGraph, shows you how to combine these pieces with LangGraph to orchestrate long-running, multi-agent workflows. Big thanks to community member Dmitry Labazkin for helping us shape this course with his contributions! Enroll for free ➡️

LangChain

63,349 просмотров • 10 месяцев назад

We’re excited to release an interactive guide highlighting the definitive set of principles for building AI agents 🔥 Based on the popular 12-Factor agents repo by dex. We packaged the principles into an interactive website and Colab notebook with working code examples, so that you can incorporate these principles into your agent application in minutes. As a refresher - some of the principles include getting structured outputs from tools, state management, checkpointing, human-in-the-loop, error handling, composing smaller agents into bigger ones, and more. This is amazing work by Laurie Voss and should be required reading for anyone looking to get started building agents. Website: Notebook:
1:05

We’re excited to release an interactive guide highlighting the definitive set of principles for building AI agents 🔥 Based on the popular 12-Factor agents repo by dex. We packaged the principles into an interactive website and Colab notebook with working code examples, so that you can incorporate these principles into your agent application in minutes. As a refresher - some of the principles include getting structured outputs from tools, state management, checkpointing, human-in-the-loop, error handling, composing smaller agents into bigger ones, and more. This is amazing work by Laurie Voss and should be required reading for anyone looking to get started building agents. Website: Notebook:

Jerry Liu

33,740 просмотров • 1 год назад

AI agents are evolving and becoming more proactive. From cloud-based to on-device, these systems can run continuously, handle tasks, and keep sensitive data local. New Liquid Foundation Models bring agents directly to your laptop and phone, even while you are offline. Watch the latest episode of Advanced Insights to learn more about our partnership with Liquid AI:
1:20

AI agents are evolving and becoming more proactive. From cloud-based to on-device, these systems can run continuously, handle tasks, and keep sensitive data local. New Liquid Foundation Models bring agents directly to your laptop and phone, even while you are offline. Watch the latest episode of Advanced Insights to learn more about our partnership with Liquid AI:

AMD

29,951 просмотров • 3 месяцев назад

Telecom infrastructure is becoming a distributed AI compute layer, unlocking smarter cities and more efficient operations. NVIDIA AI-RAN and NVIDIA Metropolis Blueprint for video search and summarization enable AI agents to run continuously at the edge. These agents analyze real-time data streams from connected sensors over 5G to power live monitoring and infrastructure inspection. ▶️ Watch the full video to learn more:
0:44

Telecom infrastructure is becoming a distributed AI compute layer, unlocking smarter cities and more efficient operations. NVIDIA AI-RAN and NVIDIA Metropolis Blueprint for video search and summarization enable AI agents to run continuously at the edge. These agents analyze real-time data streams from connected sensors over 5G to power live monitoring and infrastructure inspection. ▶️ Watch the full video to learn more:

NVIDIA AI

11,078 просмотров • 3 месяцев назад

Revenue agents are the main course. Data enrichment is on the house. B2B orgs spend $3.1B a year for employees to find contact and company data. Now agents need it too. So we made a call: enrichment is a cost of doing agentic work, not a product to mark up. It's infrastructure that revenue agents need to run. Today, we’re opening up our in-house data collection and enrichment infrastructure to all. No extra contracts. No credits to count. Just revenue, served.
0:43

Revenue agents are the main course. Data enrichment is on the house. B2B orgs spend $3.1B a year for employees to find contact and company data. Now agents need it too. So we made a call: enrichment is a cost of doing agentic work, not a product to mark up. It's infrastructure that revenue agents need to run. Today, we’re opening up our in-house data collection and enrichment infrastructure to all. No extra contracts. No credits to count. Just revenue, served.

Rox

182,964 просмотров • 23 дней назад

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇
0:59

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇

Zeb Evans

425,244 просмотров • 6 месяцев назад

When we ask an agent to "fix this bug," we're approving hundreds of actions we'll never see. These agents have our production credentials; and increasingly, they run in the background while we work on something else. Engineers are spinning up multiple Claude Code sessions in parallel, and ClawdBot hit >100k GitHub stars in weeks by letting agents handle email, workflows, even car purchases. The shift to agents without constant supervision is here. Today we're launching MintMCP : governance for AI agents.
2:28

When we ask an agent to "fix this bug," we're approving hundreds of actions we'll never see. These agents have our production credentials; and increasingly, they run in the background while we work on something else. Engineers are spinning up multiple Claude Code sessions in parallel, and ClawdBot hit >100k GitHub stars in weeks by letting agents handle email, workflows, even car purchases. The shift to agents without constant supervision is here. Today we're launching MintMCP : governance for AI agents.

Jiquan Ngiam

470,221 просмотров • 5 месяцев назад

Anthropic Managed Agents team: "Fable 5 - is our best model for self-improving agent systems. add /loops, dynamic workflows, dreaming - and you're unstoppable." in 13 minutes, the Anthropic team shows how to build self-improving agent systems with Fable 5 from scratch. Worth more than a $500 agent-building course. Live from the Anthropic stage in Japan. Unpublished
13:23

Anthropic Managed Agents team: "Fable 5 - is our best model for self-improving agent systems. add /loops, dynamic workflows, dreaming - and you're unstoppable." in 13 minutes, the Anthropic team shows how to build self-improving agent systems with Fable 5 from scratch. Worth more than a $500 agent-building course. Live from the Anthropic stage in Japan. Unpublished

Movez

67,091 просмотров • 1 месяц назад

🚨 Announcing Abacus AI SuperComputer - Build Any Cloud Service With A Single Prompt - build or host any cloud app, API or service - spin up local LLMs - always on agents including Hermes/Claw - use cloud storage and databases - work with Claude or Codex - scale to billions of AI agents Simply chat with the super computer to build, host or run anything!!
0:44

🚨 Announcing Abacus AI SuperComputer - Build Any Cloud Service With A Single Prompt - build or host any cloud app, API or service - spin up local LLMs - always on agents including Hermes/Claw - use cloud storage and databases - work with Claude or Codex - scale to billions of AI agents Simply chat with the super computer to build, host or run anything!!

Bindu Reddy

33,188,775 просмотров • 2 месяцев назад

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!
2:17

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

127,724 просмотров • 1 год назад

🚀 New LangChain Academy Course: Building Reliable Agents 🚀 Shipping agents to production is hard. Traditional software is deterministic – when something breaks, you check the logs and fix the code. But agents rely on non-deterministic models. Add multi-step reasoning, tool use, and real user traffic, and building reliable agents becomes far more complex than traditional system design. The goal of this course is to teach you how to take an agent from first run to production-ready system through iterative cycles of improvement. You’ll learn how to do this with LangSmith, our agent engineering platform for observing, evaluating, and deploying agents. Enroll for free ➡️
2:02

🚀 New LangChain Academy Course: Building Reliable Agents 🚀 Shipping agents to production is hard. Traditional software is deterministic – when something breaks, you check the logs and fix the code. But agents rely on non-deterministic models. Add multi-step reasoning, tool use, and real user traffic, and building reliable agents becomes far more complex than traditional system design. The goal of this course is to teach you how to take an agent from first run to production-ready system through iterative cycles of improvement. You’ll learn how to do this with LangSmith, our agent engineering platform for observing, evaluating, and deploying agents. Enroll for free ➡️

LangChain

30,744 просмотров • 5 месяцев назад

Introducing 🦀 CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents 🦀 CRAB provides an end-to-end and easy-to-use framework to build multimodal agents, operate environments, and create benchmarks to evaluate them, featuring three key components: - 🔀 Cross-environment support - agents can operate tasks in 📱 Android and 💻 Ubuntu. - 🕸️ Graph evaluator - provides a fine-grain evaluation metric for agents. - 🤖 Task generation - composes subtasks to automatically generate tasks. By connecting all devices to agents, 🦀CRAB unlocks greater capabilities for human-like tasks than ever before. Use 🦀 CRAB to benchmark your multimodal agents! - 👨‍💻 Check out the repository: - 📝 Read the paper: - 🌐 Find out more via the project page: - 🐫 Join our community:
1:05

Introducing 🦀 CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents 🦀 CRAB provides an end-to-end and easy-to-use framework to build multimodal agents, operate environments, and create benchmarks to evaluate them, featuring three key components: - 🔀 Cross-environment support - agents can operate tasks in 📱 Android and 💻 Ubuntu. - 🕸️ Graph evaluator - provides a fine-grain evaluation metric for agents. - 🤖 Task generation - composes subtasks to automatically generate tasks. By connecting all devices to agents, 🦀CRAB unlocks greater capabilities for human-like tasks than ever before. Use 🦀 CRAB to benchmark your multimodal agents! - 👨‍💻 Check out the repository: - 📝 Read the paper: - 🌐 Find out more via the project page: - 🐫 Join our community:

CAMEL-AI.org

72,661 просмотров • 2 лет назад

We're excited to announce our partnership with Coinbase 🛡️ to bring Tavily to x402, the open protocol for internet-native agentic payments. With x402, agents can discover and use Tavily web search at runtime without an API key. Agents use a Base wallet to pay per-request and get instant results. The next billion agents will discover, pay for, and use online services fully autonomously. We’re live on and we’re just getting started. More in the comments.
0:20

We're excited to announce our partnership with Coinbase 🛡️ to bring Tavily to x402, the open protocol for internet-native agentic payments. With x402, agents can discover and use Tavily web search at runtime without an API key. Agents use a Base wallet to pay per-request and get instant results. The next billion agents will discover, pay for, and use online services fully autonomously. We’re live on and we’re just getting started. More in the comments.

Tavily

59,986 просмотров • 2 месяцев назад

Anthropic Managed Agents team: "Fable 5 is our best model for running self-improving agent systems. Add /loops, dynamic workflows, dreaming and you are unstoppable" in 13-minutes, Anthropic team shows how to build self-improving agent systems with Fable 5 from scratch. Worth more than a $500 agent building course. Live from the last Anthropic stage in Japan. Unpublished.
13:23

Anthropic Managed Agents team: "Fable 5 is our best model for running self-improving agent systems. Add /loops, dynamic workflows, dreaming and you are unstoppable" in 13-minutes, Anthropic team shows how to build self-improving agent systems with Fable 5 from scratch. Worth more than a $500 agent building course. Live from the last Anthropic stage in Japan. Unpublished.

Codez

470,349 просмотров • 1 месяц назад