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Meet Liquid ShieldFlow. An on-device privacy layer powered by a device-native Liquid Foundation Model, Liquid ShieldFlow redacts sensitive data before it ever leaves your machine. No GPU needed and light on memory. It runs on almost any PC, locally, in real time. ShieldFlow was featured yesterday at Microsoft Build...

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Liquid AI

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20,993 görüntüleme • 5 ay önce •via X (Twitter)

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Aaliya profil fotoğrafı
Aaliya5 ay önce

Running privacy tools locally is a smart idea

Daniel Lougen profil fotoğrafı
Daniel Lougen5 ay önce

@luksamuk Done

Broooooklyn profil fotoğrafı
Broooooklyn5 ay önce

@ewind_dev this is what you want for @spoollabs

Mariusz Kurman profil fotoğrafı
Mariusz Kurman5 ay önce

Nice👀

Krish Dasgupta profil fotoğrafı
Krish Dasgupta5 ay önce

Beautiful concept !!!!! Can’t wait to try - been so far only working with Presidio !

Spaghetti Stories profil fotoğrafı
Spaghetti Stories5 ay önce

On-device privacy that actually runs locally with almost no overhead is the kind of infrastructure that makes real local agents viable. Everything else has been hype — this one actually feels practical.

Omar Gashi profil fotoğrafı
Omar Gashi5 ay önce

How can I invest in @liquidai

luis profil fotoğrafı
luis5 ay önce

@Presidentlin

Michel aka Agent B profil fotoğrafı
Michel aka Agent B5 ay önce

Cool one. Bravo to the team ! 👏👏

AI Mastery Guide profil fotoğrafı
AI Mastery Guide5 ay önce

The fact that it runs locally means your sensitive data never touches an API endpoint even briefly, that changes the compliance conversation entirely

toji profil fotoğrafı
toji5 ay önce

Fascinating

Zohaib Ai profil fotoğrafı
Zohaib Ai5 ay önce

Privacy features are becoming more important than ever.

Sani Ai Tech profil fotoğrafı
Sani Ai Tech5 ay önce

Privacy done right 🔒 Impressive work 👏 Big win for privacy 🚀 Love this approach 🔥 Local AI is the future ⚡ Very promising 👀 Huge step forward 🚀 Well done, team 👏

Benzer Videolar

Bringing AI agents to the PC requires efficiency, speed, and smart memory management. AMD and Liquid AI are partnering to enable foundational models on PCs and end-point devices to address the real-world challenges of running AI agents locally. Liquid AI Co-founder and CEO Ramin explains how we are helping make on-device AI practical and accessible.
0:47

Bringing AI agents to the PC requires efficiency, speed, and smart memory management. AMD and Liquid AI are partnering to enable foundational models on PCs and end-point devices to address the real-world challenges of running AI agents locally. Liquid AI Co-founder and CEO Ramin explains how we are helping make on-device AI practical and accessible.

AMD

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LFM2-2.6B-Transcript AMD🤝Liquid AI > Private, on-device meeting summarization > Cloud-level quality > Faster processing, lower memory footprint > Runs across CPU, GPU, and NPU on AMD Ryzen AI PC Great showcase of what tiny models can achieve when fine-tuned
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LFM2-2.6B-Transcript AMD🤝Liquid AI > Private, on-device meeting summarization > Cloud-level quality > Faster processing, lower memory footprint > Runs across CPU, GPU, and NPU on AMD Ryzen AI PC Great showcase of what tiny models can achieve when fine-tuned

Maxime Labonne

28,034 görüntüleme • 10 ay önce

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

30,054 görüntüleme • 6 ay önce

Here is Bonsai 2 9B as part of a hybrid AI workflow on Mac. Bonsai 2 can serve as the local intelligence layer in systems that combine on-device and cloud inference. This can support privacy-focused applications by giving developers more control over where information is processed and what leaves the device. In this demo, Bonsai 2 processes a confidential data room on-device, replacing sensitive details with codes before a cloud model analyzes the material and drafts a due diligence memo. It keeps sensitive information on-device and sends just 8.6% of the data to the cloud, reducing cloud costs by 86%.
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Here is Bonsai 2 9B as part of a hybrid AI workflow on Mac. Bonsai 2 can serve as the local intelligence layer in systems that combine on-device and cloud inference. This can support privacy-focused applications by giving developers more control over where information is processed and what leaves the device. In this demo, Bonsai 2 processes a confidential data room on-device, replacing sensitive details with codes before a cloud model analyzes the material and drafts a due diligence memo. It keeps sensitive information on-device and sends just 8.6% of the data to the cloud, reducing cloud costs by 86%.

PrismML

10,245 görüntüleme • 22 gün önce

Introducing Run open source AI models locally in your browser, in just a few clicks. Pick an opensource model or load any model from Hugging Face. It downloads to your machine and runs there privately; every prompt stays on your device. Get your free personal AI today.
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Introducing Run open source AI models locally in your browser, in just a few clicks. Pick an opensource model or load any model from Hugging Face. It downloads to your machine and runs there privately; every prompt stays on your device. Get your free personal AI today.

Gratis

128,296 görüntüleme • 4 gün önce

This is so cool - fully on-device semantic search. Type "glass" and get clips with liquid glass. No manual tags needed. Runs an on-board MiniLM, vision ML, OCR and RRF fusion to blend signals to output. That's a mouthful, but means very speedy, hands-off and local on your Mac
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This is so cool - fully on-device semantic search. Type "glass" and get clips with liquid glass. No manual tags needed. Runs an on-board MiniLM, vision ML, OCR and RRF fusion to blend signals to output. That's a mouthful, but means very speedy, hands-off and local on your Mac

Tobias Renström

21,367 görüntüleme • 3 ay önce

OMG… you can now fine-tune your own AI model and run it entirely on your device. No servers. No API keys. No data leaks. Gemma 3 270M just made local LLMs real and it only takes about an hour to build your own. Here’s how:
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OMG… you can now fine-tune your own AI model and run it entirely on your device. No servers. No API keys. No data leaks. Gemma 3 270M just made local LLMs real and it only takes about an hour to build your own. Here’s how:

Louis Gleeson

79,508 görüntüleme • 1 yıl önce

Dialing in real time V2V in Sentinel. No Python or venvs, just an installer. Runs locally on any 16GB+ NVIDIA GPU. Sign up for early access:
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Dialing in real time V2V in Sentinel. No Python or venvs, just an installer. Runs locally on any 16GB+ NVIDIA GPU. Sign up for early access:

Spencer Sterling

58,512 görüntüleme • 7 ay önce

ICYMI: AI is going local. AMD and Liquid AI are enabling on-device agents that are more secure and efficient. Watch the latest Advanced Insights episode to learn more:
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ICYMI: AI is going local. AMD and Liquid AI are enabling on-device agents that are more secure and efficient. Watch the latest Advanced Insights episode to learn more:

AMD

19,573 görüntüleme • 6 ay önce

Run your coding agent on a local model, with QVAC. OpenCode now plugs into a model on your own machine through our official plugin. That changes two things. Privacy by default. Your repo, your prompts, and every command the agent runs stay on your device. No third-party model ever sees your code. For an agent that reads your codebase and runs commands, that is the point, not a nice-to-have. New workflows. With no meter and no rate limit, the agent does not have to stop. Let it loop on a task, refactor a whole repo overnight, or run a watch-build-fix loop that keeps going. Setup takes a few minutes: one line in opencode.json and OpenCode runs on a local QVAC model. In the video demo, it handles three real developer tasks back to back, fully offline.
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Run your coding agent on a local model, with QVAC. OpenCode now plugs into a model on your own machine through our official plugin. That changes two things. Privacy by default. Your repo, your prompts, and every command the agent runs stay on your device. No third-party model ever sees your code. For an agent that reads your codebase and runs commands, that is the point, not a nice-to-have. New workflows. With no meter and no rate limit, the agent does not have to stop. Let it loop on a task, refactor a whole repo overnight, or run a watch-build-fix loop that keeps going. Setup takes a few minutes: one line in opencode.json and OpenCode runs on a local QVAC model. In the video demo, it handles three real developer tasks back to back, fully offline.

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11,382,936 görüntüleme • 4 ay önce

Liquid. Not locked. Bifrost - Staking Yield Layer on Polkadot makes it possible to vote, DeFi, and more with your staked DOT 💧 How liquid staking works ↓
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Liquid. Not locked. Bifrost - Staking Yield Layer on Polkadot makes it possible to vote, DeFi, and more with your staked DOT 💧 How liquid staking works ↓

Polkadot

20,165 görüntüleme • 1 yıl önce

3D scenes, built locally. Describe what you want in plain language. Edge0 can generate a structured 3D scene, then modify and refine it on your phone or PC. No project data leaves the device.
0:27

3D scenes, built locally. Describe what you want in plain language. Edge0 can generate a structured 3D scene, then modify and refine it on your phone or PC. No project data leaves the device.

Samuel Zeng

21,642 görüntüleme • 1 ay önce

There’s a brand new AI model. It’s FREE. It runs on your laptop. No cloud. No subscription. No GPU. And it has 24 BILLION parameters. Here’s the crazy part: It only activates ~2.3B at a time. That means it stays fast, lean, and runs on normal hardware. You can: • Read massive docs • Build full content plans • Answer complex questions • Run unlimited prompts All privately. No one sees your data. No per-token fees. No usage caps. It’s called LFM2 24B A2B from Liquid AI. This is one of the biggest shifts in local AI we’ve seen.
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There’s a brand new AI model. It’s FREE. It runs on your laptop. No cloud. No subscription. No GPU. And it has 24 BILLION parameters. Here’s the crazy part: It only activates ~2.3B at a time. That means it stays fast, lean, and runs on normal hardware. You can: • Read massive docs • Build full content plans • Answer complex questions • Run unlimited prompts All privately. No one sees your data. No per-token fees. No usage caps. It’s called LFM2 24B A2B from Liquid AI. This is one of the biggest shifts in local AI we’ve seen.

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Blender AI Motion Capture Plugin — Connects to a 1080P webcam and runs locally on your computer. Powered by a 1-billion-parameter model, it requires an 8GB VRAM GPU for real-time processing. Supports both real-time capture and video upload. The full-featured version currently supports NVIDIA CUDA only, and requires DX11 or above.

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Open Source alternative to Apple Intelligence For small tasks, it runs LLM agents on-device. For larger tasks, it runs on your own AI cluster of personal devices Your data stays on your devices By exo intern
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Cyansdk is a free Blender AI motion capture plugin that runs locally on your computer. It requires 8GB of GPU memory for real-time processing and has a model size of 4.2GB. It supports real-time motion capture via webcam and video upload.

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Introducing Phi-3 WebGPU, a private and powerful AI chatbot that runs locally in your browser, powered by 🤗 Transformers.js and onnxruntime-web! 🔒 On-device inference: no data sent to a server ⚡️ WebGPU-accelerated (> 20 t/s) 📥 Model downloaded once and cached Try it out! 👇
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Introducing Phi-3 WebGPU, a private and powerful AI chatbot that runs locally in your browser, powered by 🤗 Transformers.js and onnxruntime-web! 🔒 On-device inference: no data sent to a server ⚡️ WebGPU-accelerated (> 20 t/s) 📥 Model downloaded once and cached Try it out! 👇

Xenova

117,724 görüntüleme • 2 yıl önce

Well said. AI shouldn't just live in a data center, but it should run on the device you're already using: Whether it's a phone, a car, an appliance, or another everyday device in the real world. That’s what we’re working on at Liquid AI. We appreciate the spotlight on our COO Jeffrey Li (jeffrey li), who joined the AI-Curious Podcast (AI-Curious). Thank you, Jeff Wilser!
0:43

Well said. AI shouldn't just live in a data center, but it should run on the device you're already using: Whether it's a phone, a car, an appliance, or another everyday device in the real world. That’s what we’re working on at Liquid AI. We appreciate the spotlight on our COO Jeffrey Li (jeffrey li), who joined the AI-Curious Podcast (AI-Curious). Thank you, Jeff Wilser!

Liquid AI

12,995 görüntüleme • 5 ay önce