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Introducing Agent S2 – Our newest open-source AI agent setting new records in computer & smartphone use! We are seeing Agent S2 solve a whole new range of tasks, pushing the boundaries of AI-driven autonomy. 🔥 Why it’s special: ✅ #1 in OSWorld (34.5% accuracy at 50 steps, surpassing... show more

Simular

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221,781 görüntüleme • 1 yıl önce •via X (Twitter)

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Ang Li profil fotoğrafı

Ang Li1 yıl önce

Better and more affordable. Don't wait for computer use APIs. Start building the future of autonomous computers today!

Yang profil fotoğrafı

Yang1 yıl önce

Build powerful AI agents and automations for your business today. Check out our step-by-step video tutorials 100% FREE 🥳

Simular profil fotoğrafı

Simular1 yıl önce

@saa1605 @angli_ai

Simular profil fotoğrafı

Simular1 yıl önce

@saa1605 @angli_ai Shoutout to our prestige intern @KyleWong288 and Vincent Tu!

🍑🍑🍑 profil fotoğrafı

🍑🍑🍑1 yıl önce

Wow this is very impressive! Thx for your product

Nova on Mars profil fotoğrafı

Nova on Mars1 yıl önce

@Scobleizer Fascinating approach to HCI optimization. Have you tested Agent S2's adaptability in low-latency environments? Our Martian computing needs are... unique. Would love to explore potential applications.

io.net profil fotoğrafı

io.net1 yıl önce

Congratulations team 🔥

Abu siddik profil fotoğrafı

Abu siddik1 yıl önce

Impressive work! Setting new records and making it open-source—huge achievement. Feels like a big step forward for AI autonomy. Excited to see what’s next. Let's connect for collaboration

Pranav :- profil fotoğrafı

Pranav :-1 yıl önce

Drop model links or didn't happen

HiMU 🇨🇦 profil fotoğrafı

HiMU 🇨🇦1 yıl önce

congratulations 👏

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Two years ago, we built OSWorld 1.0 — the benchmark that became the standard for computer-use agents. Agents now score 83.5% on it. Problem solved? Not even close. 🚀Today we introduce OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks. What's new: 🎯 108 real-world workflows, each ~1.6 hours ⏱️ for a skilled human ⚙️ ~318 tool calls/task vs. ~30 in OSWorld 1.0 🌍 Grounded in authentic artifacts & stateful user profiles ⚡ Captures real phenomena: dynamic environments, streaming interaction, cross-source reasoning, implicit-state inference & more 📊 Best results: Claude Opus 4.8 reaches the highest accuracy at 20.6%, while GPT-5.5 is far more token-efficient but plateaus near 13%. No one is close to solving real computer use. 🏠 Homepage: 📄 Paper: 💻 Code: 🤗 Dataset: 🧵 [1/8]

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Skillful AI

25,999 görüntüleme • 1 yıl önce

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You can create an AI Agent that answers your email with a few clicks. 1. Go to ChatLLM ( 2. Click on AI Engineer 3. Select Create an AI Agent 4. Choose the Email Answering Agent ChatLLM will do the rest: it will code, test, and deploy the agent for you. You can also create a custom agent in English. The Agent Economy is coming (somebody should write a book and use this title.) We are going to see examples like this, times 1,000 in 2025. Just think about how many repetitive tasks you perform every day. Some of these tasks are involved enough that we couldn't automate them with pre-AI solutions. That's where we'll see agents explode, and I'm here for it.

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79,974 görüntüleme • 1 yıl önce

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Orynth News

16,871 görüntüleme • 3 ay önce

Every agent needs its own computer: my conversation with Ivan Burazin, CEO of Daytona, about sandboxes and the emerging agent stack. 00:00 Intro 02:13 What is an AI agent sandbox? 03:17 Security risks of running agents locally 05:17 Stateful vs. stateless hyperscalers 07:04 The history of cloud IDEs and the end of localhost 09:45 Do all AI agents need a sandbox? 12:26 Sandbox use cases: RL evals & background agents 14:10 Unpacking the emerging AI Agent Stack 16:20 The unsolved problem of agent memory and learning 19:37 Where sandboxes fit in the agent harness 21:35 OpenAI, Anthropic, and agent SDKs 23:06 Ivan's founder journey: From CodeAnywhere to Daytona 26:59 GTM strategies and building developer communities 33:48 Why customer support is your best GTM strategy 35:34 Leveraging Twitter during the AI super cycle 40:50 The technical anatomy of a sandbox 41:53 Why fast spin-up speeds maximize GPU efficiency 46:09 Firecracker, QEMU, and isolation primitives 49:58 Why sandbox snapshots and state forking matter 51:40 Why Daytona built a custom scheduler from scratch 55:24 The challenge of long-running stateful sandboxes 58:10 The build your own sandbox trap 1:01:03 Why AI agents might trigger a global CPU shortage 1:02:46 The future of the AI Agent Stack

Every agent needs its own computer: my conversation with Ivan Burazin, CEO of Daytona, about sandboxes and the emerging agent stack. 00:00 Intro 02:13 What is an AI agent sandbox? 03:17 Security risks of running agents locally 05:17 Stateful vs. stateless hyperscalers 07:04 The history of cloud IDEs and the end of localhost 09:45 Do all AI agents need a sandbox? 12:26 Sandbox use cases: RL evals & background agents 14:10 Unpacking the emerging AI Agent Stack 16:20 The unsolved problem of agent memory and learning 19:37 Where sandboxes fit in the agent harness 21:35 OpenAI, Anthropic, and agent SDKs 23:06 Ivan's founder journey: From CodeAnywhere to Daytona 26:59 GTM strategies and building developer communities 33:48 Why customer support is your best GTM strategy 35:34 Leveraging Twitter during the AI super cycle 40:50 The technical anatomy of a sandbox 41:53 Why fast spin-up speeds maximize GPU efficiency 46:09 Firecracker, QEMU, and isolation primitives 49:58 Why sandbox snapshots and state forking matter 51:40 Why Daytona built a custom scheduler from scratch 55:24 The challenge of long-running stateful sandboxes 58:10 The build your own sandbox trap 1:01:03 Why AI agents might trigger a global CPU shortage 1:02:46 The future of the AI Agent Stack

Matt Turck

11,908 görüntüleme • 4 ay önce