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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...

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Simular

8,863 subscribers

221,781 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 10

Фото профиля Ang Li
Ang Li1 год назад

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

Фото профиля Yang
Yang1 год назад

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

Фото профиля Simular
Simular1 год назад

@saa1605 @angli_ai

Фото профиля Simular
Simular1 год назад

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

Фото профиля 🍑🍑🍑
🍑🍑🍑1 год назад

Wow this is very impressive! Thx for your product

Фото профиля Nova on Mars
Nova on Mars1 год назад

@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.

Фото профиля io.net
io.net1 год назад

Congratulations team 🔥

Фото профиля Abu siddik
Abu siddik1 год назад

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

Фото профиля Pranav :-
Pranav :-1 год назад

Drop model links or didn't happen

Фото профиля HiMU 🇨🇦
HiMU 🇨🇦1 год назад

congratulations 👏

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25,927 просмотров • 1 год назад

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11,908 просмотров • 2 месяцев назад

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Brand New Vision

28,170 просмотров • 11 месяцев назад

We made it. 24 episodes. 6 months. One wild ride through the future of agentic AI. In Episode 24 of Agents Unleashed, maybrier ☴ looks back at Season One: what we got right, what we’re still figuring out, and why AI agents are only getting started. • What people still don’t get about AI agents ft. David Minarsch ☴ • Tradeoffs of computer-use agents (and why they matter) • Can a model be loyal? ft. Oleg Golev • Big questions: What is an AI agent, really? • Ethereum’s 10-year plan ft. Tomasz K. Stańczak 00:00 – Introduction 00:52 – Do people "get" AI agents? with David Minarsch 02:13 – Letting an AI agent use your computer 03:50 – Measuring model "loyalty" with Oleg Golev, Sentient 06:06 – What makes something an AI agent? 07:44 – The next decade of Ethereum with Tomasz Stańczak This episode is sponsored by Olas (formerly Autonolas)
11:19

We made it. 24 episodes. 6 months. One wild ride through the future of agentic AI. In Episode 24 of Agents Unleashed, maybrier ☴ looks back at Season One: what we got right, what we’re still figuring out, and why AI agents are only getting started. • What people still don’t get about AI agents ft. David Minarsch ☴ • Tradeoffs of computer-use agents (and why they matter) • Can a model be loyal? ft. Oleg Golev • Big questions: What is an AI agent, really? • Ethereum’s 10-year plan ft. Tomasz K. Stańczak 00:00 – Introduction 00:52 – Do people "get" AI agents? with David Minarsch 02:13 – Letting an AI agent use your computer 03:50 – Measuring model "loyalty" with Oleg Golev, Sentient 06:06 – What makes something an AI agent? 07:44 – The next decade of Ethereum with Tomasz Stańczak This episode is sponsored by Olas (formerly Autonolas)

Olas (formerly Autonolas)

22,807 просмотров • 9 месяцев назад

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses *three* NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.
1:24

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses *three* NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

kwindla

274,403 просмотров • 6 месяцев назад