Загрузка видео...

Не удалось загрузить видео

На главную

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...

liquidai's profile picture

Liquid AI

32,047 subscribers

20,762 просмотров • 1 месяц назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

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.
1:23

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.

CYANPUPPETS

58,611 просмотров • 1 год назад

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! 👇
1:26

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

117,700 просмотров • 2 лет назад

Sam Altman recently said: “GPT-OSS has strong real-world performance comparable to o4-mini—and you can run it locally on your phone.” Many believed running a 20B-parameter model on mobile devices was still years away. At Nexa AI, we’ve built our foundation on deep on-device AI technology—turning that vision into reality. Today, GPT-OSS is running fully local on mobile devices through our app, Nexa Studio. Real performance on Snapdragon Gen 5: - 17 tokens/sec decoding speed - < 3 seconds Time-to-First-Token Developers can now use NexaSDK to build their own local AI apps powered by GPT-OSS. What this unlocks: - Real reasoning models running locally - The next wave of AI Agent use cases—without cloud limits - True data privacy: your data never leaves your device Appreciation to OpenAI Sam Altman Greg Brockman and the entire leadership team for pushing the boundaries of the open-source AI community. Thanks to Qualcomm for the partnership, and to Manoj Khilnani and Chun-Po (Jerry) Chang for the incredible collaboration.
0:31

Sam Altman recently said: “GPT-OSS has strong real-world performance comparable to o4-mini—and you can run it locally on your phone.” Many believed running a 20B-parameter model on mobile devices was still years away. At Nexa AI, we’ve built our foundation on deep on-device AI technology—turning that vision into reality. Today, GPT-OSS is running fully local on mobile devices through our app, Nexa Studio. Real performance on Snapdragon Gen 5: - 17 tokens/sec decoding speed - < 3 seconds Time-to-First-Token Developers can now use NexaSDK to build their own local AI apps powered by GPT-OSS. What this unlocks: - Real reasoning models running locally - The next wave of AI Agent use cases—without cloud limits - True data privacy: your data never leaves your device Appreciation to OpenAI Sam Altman Greg Brockman and the entire leadership team for pushing the boundaries of the open-source AI community. Thanks to Qualcomm for the partnership, and to Manoj Khilnani and Chun-Po (Jerry) Chang for the incredible collaboration.

NEXA AI

197,453 просмотров • 9 месяцев назад

AMD on local AI: "this is the smallest AI development system in the world, capable of running models up to 200 billion parameters locally, not connected to anything" 128GB of unified memory shared by CPU, GPU, and NPU fits in your hand. Runs a model larger than GPT-3 with no internet $3,999 once. roughly $16 a month in power Cloud equivalent: up to $750 a month The question was never whether local AI could compete on power It was whether it could compete on price. Now it does
1:00

AMD on local AI: "this is the smallest AI development system in the world, capable of running models up to 200 billion parameters locally, not connected to anything" 128GB of unified memory shared by CPU, GPU, and NPU fits in your hand. Runs a model larger than GPT-3 with no internet $3,999 once. roughly $16 a month in power Cloud equivalent: up to $750 a month The question was never whether local AI could compete on power It was whether it could compete on price. Now it does

plutos

151,966 просмотров • 1 месяц назад

Here comes AutoClaw. We offer a new solution to run OpenClaw locally on your own machine. - Download and start immediately. No API key required. - Bring any model you like, or use GLM-5-Turbo, optimized for tool calling and multi-step tasks. - Fully local. Your data never leaves your machine. We're giving data control back to Claw users. Meet AutoClaw → Join the conversation →
0:48

Here comes AutoClaw. We offer a new solution to run OpenClaw locally on your own machine. - Download and start immediately. No API key required. - Bring any model you like, or use GLM-5-Turbo, optimized for tool calling and multi-step tasks. - Fully local. Your data never leaves your machine. We're giving data control back to Claw users. Meet AutoClaw → Join the conversation →

Z.AI

566,249 просмотров • 4 месяцев назад

Your cloud agent runs $10–20 a day. This box runs OpenAI's 120B model for $0. Same workload. It just sits in a closet on AMD silicon. A Minisforum mini PC, Strix Halo, 128GB unified memory. The model lives entirely on-device. 56 tokens a second. No API key. Nothing leaves the network. Cloud only handles the exceptions now. Claude or OpenAI by routing, not by default. Everyone calls this "free." Wrong word. It's not free. It's owned. The bill just stops existing. The cloud was never the only option. It was the default nobody bothered to question.
0:21

Your cloud agent runs $10–20 a day. This box runs OpenAI's 120B model for $0. Same workload. It just sits in a closet on AMD silicon. A Minisforum mini PC, Strix Halo, 128GB unified memory. The model lives entirely on-device. 56 tokens a second. No API key. Nothing leaves the network. Cloud only handles the exceptions now. Claude or OpenAI by routing, not by default. Everyone calls this "free." Wrong word. It's not free. It's owned. The bill just stops existing. The cloud was never the only option. It was the default nobody bothered to question.

RetroChainer

13,925 просмотров • 1 месяц назад

liquid has problem? a complaint from abdullah (liquid user) about founder liquid (Franklyn Wang) take advantage him from a leaked message between founder liquid and former cm liquid (alex) content about they discussed additional bonuses for those who reached trading volume milestones reward $1,000 for $5m volume that information was leaked to Abdullah, and a friend of his made almost $9m volume when abdullah went back to claim reward, the founder liquid said that reward $5m volume = $1,000 just for "KOL" he offers abdullah $500 for almost $9m volume, and after some debate is $600 and then $800 and when abdullah asked again, he said it was just an idea and hadn't been confirmed, but in a previous message he said it was only for "KOL" he also threatened to block abdullah, which forced the abdullah to accept the $800 reward for almost $9m volume a leaked message from a group revealed that the founder liquid actually confirmed he would pay $1,000 for the $5m volume, but he didn't want to pay too much to abdullah furthermore, founder liquid is exploiting abdullah, who spoke about the affiliate lead, and founder liquid said "get 100 users" after he finished, he was blocked by liquid founder, then used many accounts to harass them but received no response more, abdullah contributed greatly to liquid, at the time founder Liquid acknowledged that he was on the team and was an affiliate, and he said that abdullah was very helpful after all the info and evidence was provided to me, i contacted founder liquid to work on this issue but all the responses simply stated that no promises had been made and placed all the blame on abdullah he said that abdullah had cheated many people and provided them with low-quality, and that liquid only earned $30 from abdullah work but he forgot he said abdullab was "very helpful", and even told him to go earn $20m volume daily lmao one piece of unsubstantiated information is that he offered abdullah $10 per referral, but when abdullah earned over 100 referral, he disappeared founder liquid has also banned Abdullah on all platforms and also banned abdullah on discord, he also hasn't answered my two most recent questions ggs
0:40

liquid has problem? a complaint from abdullah (liquid user) about founder liquid (Franklyn Wang) take advantage him from a leaked message between founder liquid and former cm liquid (alex) content about they discussed additional bonuses for those who reached trading volume milestones reward $1,000 for $5m volume that information was leaked to Abdullah, and a friend of his made almost $9m volume when abdullah went back to claim reward, the founder liquid said that reward $5m volume = $1,000 just for "KOL" he offers abdullah $500 for almost $9m volume, and after some debate is $600 and then $800 and when abdullah asked again, he said it was just an idea and hadn't been confirmed, but in a previous message he said it was only for "KOL" he also threatened to block abdullah, which forced the abdullah to accept the $800 reward for almost $9m volume a leaked message from a group revealed that the founder liquid actually confirmed he would pay $1,000 for the $5m volume, but he didn't want to pay too much to abdullah furthermore, founder liquid is exploiting abdullah, who spoke about the affiliate lead, and founder liquid said "get 100 users" after he finished, he was blocked by liquid founder, then used many accounts to harass them but received no response more, abdullah contributed greatly to liquid, at the time founder Liquid acknowledged that he was on the team and was an affiliate, and he said that abdullah was very helpful after all the info and evidence was provided to me, i contacted founder liquid to work on this issue but all the responses simply stated that no promises had been made and placed all the blame on abdullah he said that abdullah had cheated many people and provided them with low-quality, and that liquid only earned $30 from abdullah work but he forgot he said abdullab was "very helpful", and even told him to go earn $20m volume daily lmao one piece of unsubstantiated information is that he offered abdullah $10 per referral, but when abdullah earned over 100 referral, he disappeared founder liquid has also banned Abdullah on all platforms and also banned abdullah on discord, he also hasn't answered my two most recent questions ggs

0xviet

37,942 просмотров • 2 месяцев назад

AMD CEO LISA SU HELD A MINI PC ON STAGE THAT RUNS A 235B MODEL AND REPLACES YOUR $440/MONTH AI STACK amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on one piece of silicon. cpu and gpu share 128gb of unified memory, no separate graphics card needed the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months install ollama, pull the model, point claude code at localhost. same interface, nothing leaves the machine, nothing costs per request bookmark this and read the article below
0:59

AMD CEO LISA SU HELD A MINI PC ON STAGE THAT RUNS A 235B MODEL AND REPLACES YOUR $440/MONTH AI STACK amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on one piece of silicon. cpu and gpu share 128gb of unified memory, no separate graphics card needed the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months install ollama, pull the model, point claude code at localhost. same interface, nothing leaves the machine, nothing costs per request bookmark this and read the article below

starmex

979,264 просмотров • 1 месяц назад

What Lisa Su actually held on stage: A mini PC the size of a lunchbox running Qwen3-235B locally, with no cloud and no discrete GPU Inside: the Ryzen AI Max+ 395, 128GB unified memory, 110GB usable as VRAM on Linux The first x86 chip that handles 200+ billion parameters on a single die AMD claims it beats the RTX 5080 by several times on memory-bound models — because the 5080 simply cannot fit them $1,400 to $2,500 once. cloud bills run $200 to $400 a month It pays for itself in a few months, then costs nothing per request This is not a faster GPU. it is the first real argument that your AI does not belong in someone else's data center
0:31

What Lisa Su actually held on stage: A mini PC the size of a lunchbox running Qwen3-235B locally, with no cloud and no discrete GPU Inside: the Ryzen AI Max+ 395, 128GB unified memory, 110GB usable as VRAM on Linux The first x86 chip that handles 200+ billion parameters on a single die AMD claims it beats the RTX 5080 by several times on memory-bound models — because the 5080 simply cannot fit them $1,400 to $2,500 once. cloud bills run $200 to $400 a month It pays for itself in a few months, then costs nothing per request This is not a faster GPU. it is the first real argument that your AI does not belong in someone else's data center

plutos

1,235,872 просмотров • 1 месяц назад

Some folks asked if this offline local, no internet can ever connected, real-time speech to text device requires me to speak very slowly. In the prior video, I was reading what I was saying. Here I am just speaking in my normal mumble. This is designed to build AI models based on everything I heard as speech, like videos, radio, phone (with permission), conversations to be memorized by my local AI. It should be clear that this device takes up all of my conversations for about 10 hours per day on a single battery charge. This is what I am building for you, if you want it. No, there is no monthly subscription on the to access the context of your life, and there better not be, ever. It just will take me some time.
3:21

Some folks asked if this offline local, no internet can ever connected, real-time speech to text device requires me to speak very slowly. In the prior video, I was reading what I was saying. Here I am just speaking in my normal mumble. This is designed to build AI models based on everything I heard as speech, like videos, radio, phone (with permission), conversations to be memorized by my local AI. It should be clear that this device takes up all of my conversations for about 10 hours per day on a single battery charge. This is what I am building for you, if you want it. No, there is no monthly subscription on the to access the context of your life, and there better not be, ever. It just will take me some time.

Brian Roemmele

72,280 просмотров • 8 месяцев назад

running a 405-billion-parameter model used to mean a $30,000 GPU rack. one developer made it run on a single 8GB card the kind that's been sitting in gaming laptops for years. it's called AirLLM, and the trick is almost stupidly simple: instead of loading the whole model, it keeps one layer on the GPU at a time and streams the rest through. / Llama 3.1 405B on 8GB / DeepSeek-V3, all 671B of it, on about 12GB / no quantization, no distillation, no pruning the full-precision model / works with Llama, Qwen, DeepSeek, Mistral, Gemma, and more it's Apache 2.0 and already past 22,600 stars. → bookmark it.
0:21

running a 405-billion-parameter model used to mean a $30,000 GPU rack. one developer made it run on a single 8GB card the kind that's been sitting in gaming laptops for years. it's called AirLLM, and the trick is almost stupidly simple: instead of loading the whole model, it keeps one layer on the GPU at a time and streams the rest through. / Llama 3.1 405B on 8GB / DeepSeek-V3, all 671B of it, on about 12GB / no quantization, no distillation, no pruning the full-precision model / works with Llama, Qwen, DeepSeek, Mistral, Gemma, and more it's Apache 2.0 and already past 22,600 stars. → bookmark it.

dunik

148,663 просмотров • 16 дней назад

Perplexity CEO Aravind Srinivas on the biggest threat to the data center industry: It's not competition. It's not regulation. It's decentralisation. "The biggest threat to a data center is if the intelligence can be packed locally on a chip that's running on the device and then there's no need to inference all of it on like one centralized data center." He outlines how this could work in practice. Personalisation doesn't necessarily require on-device model training. Retrieval augmented generation, tool calls, and local data can already tailor AI to individual users. But the real unlock? Test time training. Aravind Srinivas describes a future where AI lives on your device, watches how you work and gradually automates your repetitive tasks. "Imagine we crack test time training where the AI watches tasks you repeatedly do on your local system, adapts to you over time and starts automating a lot of the things you do." The key insight: in this model, the intelligence belongs to you. It's your data, your device, your personalised AI brain. And if that future arrives, the economics of centralised infrastructure start to collapse. "That really disrupts the whole data center industry. It doesn't make sense to spend all this money, 500 billion, 5 trillion, whatever on building all the centralized data centers across the world that do a lot of the intelligence workloads for people." The companies spending trillions on centralised infrastructure may want to rethink where intelligence actually needs to live.
1:15

Perplexity CEO Aravind Srinivas on the biggest threat to the data center industry: It's not competition. It's not regulation. It's decentralisation. "The biggest threat to a data center is if the intelligence can be packed locally on a chip that's running on the device and then there's no need to inference all of it on like one centralized data center." He outlines how this could work in practice. Personalisation doesn't necessarily require on-device model training. Retrieval augmented generation, tool calls, and local data can already tailor AI to individual users. But the real unlock? Test time training. Aravind Srinivas describes a future where AI lives on your device, watches how you work and gradually automates your repetitive tasks. "Imagine we crack test time training where the AI watches tasks you repeatedly do on your local system, adapts to you over time and starts automating a lot of the things you do." The key insight: in this model, the intelligence belongs to you. It's your data, your device, your personalised AI brain. And if that future arrives, the economics of centralised infrastructure start to collapse. "That really disrupts the whole data center industry. It doesn't make sense to spend all this money, 500 billion, 5 trillion, whatever on building all the centralized data centers across the world that do a lot of the intelligence workloads for people." The companies spending trillions on centralised infrastructure may want to rethink where intelligence actually needs to live.

Big Brain AI

90,102 просмотров • 5 месяцев назад

CLAUDE TOOK A FULL PC GAME, REBUILT IT FROM THE GROUND UP, AND GOT IT RUNNING NATIVELY ON ANDROID IN 6 HOURS a guy pointed claude at slay the spire 2, a game he owns on steam, and it ported the whole thing to his phone almost completely on its own here's what claude did: > it pulled apart the game and recompiled it to native arm, no emulator, wine, or proton used > wrote the code, ran the build loop, and debugged it directly on the device itself > added full touch controls that feel like a real mobile game, not awkward on screen buttons > and he even built a tool on top to sync save files between his desktop and phone a couple prompts and 6 hours. it handled the extraction, the cross compile, the building and the on device debugging by itself. fully playable, not a single crash yet
0:39

CLAUDE TOOK A FULL PC GAME, REBUILT IT FROM THE GROUND UP, AND GOT IT RUNNING NATIVELY ON ANDROID IN 6 HOURS a guy pointed claude at slay the spire 2, a game he owns on steam, and it ported the whole thing to his phone almost completely on its own here's what claude did: > it pulled apart the game and recompiled it to native arm, no emulator, wine, or proton used > wrote the code, ran the build loop, and debugged it directly on the device itself > added full touch controls that feel like a real mobile game, not awkward on screen buttons > and he even built a tool on top to sync save files between his desktop and phone a couple prompts and 6 hours. it handled the extraction, the cross compile, the building and the on device debugging by itself. fully playable, not a single crash yet

Om Patel

28,504 просмотров • 1 месяц назад

Microsoft just made expensive GPUs useless. And it runs on your 5-year-old laptop. BitNet B1.58 crushes models 10x bigger using 96% less energy. Here's what changed: → Uses ternary weights (only -1, 0, +1) instead of full precision floats → 0.4GB memory usage (runs on a phone, no GPU needed) → 29 milliseconds per response (real-time fast) → Beats Llama 3 and Gemma on benchmarks while using almost no power → Free on HuggingFace, MIT license (download and run today) No API costs. No cloud bills. No data leaving your machine. ChatGPT charges per token. This is free forever. Save this video. Want the SOP? DM me. 💬
8:57

Microsoft just made expensive GPUs useless. And it runs on your 5-year-old laptop. BitNet B1.58 crushes models 10x bigger using 96% less energy. Here's what changed: → Uses ternary weights (only -1, 0, +1) instead of full precision floats → 0.4GB memory usage (runs on a phone, no GPU needed) → 29 milliseconds per response (real-time fast) → Beats Llama 3 and Gemma on benchmarks while using almost no power → Free on HuggingFace, MIT license (download and run today) No API costs. No cloud bills. No data leaving your machine. ChatGPT charges per token. This is free forever. Save this video. Want the SOP? DM me. 💬

Julian Goldie SEO

76,615 просмотров • 6 месяцев назад

QVAC SDK 0.14.0 is live. This release makes the on-device stack faster on mobile, ships the developer-agent path, and takes local text-to-speech to 31 languages. Main highlights: - OpenCode and OpenClaw. The first official OpenCode plugin, plus a maintained OpenClaw compatibility path, both built on managed mode and qvac serve. Point a coding agent at a local model with far less setup and far fewer surprises. - Brain-computer interface transcription, on the SDK. Take recorded neural signal data and decode it into text, fully on-device, no cloud. Stream it in chunks through a simple API. In 0.14 it runs GPU-accelerated on iOS. - Text to Speech in 31 languages with our Supertonic3 upgrade. VOICE AND SPEECH - Supertonic3 multilingual TTS, 5 languages to 31. - Chatterbox and Supertonic now run on the Android GPU, with lower memory use (especially on iOS), quantized s3gen Chatterbox support, and a fix for Chatterbox occasionally emitting random speech. - Whisper transcription now runs on the iOS GPU. Parakeet runs on the Android GPU, with steadier real-time streaming. VISION AND OCR - VLM multi-tile batching: high-resolution Pan and Scan images are encoded in one pass instead of tile by tile, for faster vision throughput. - OCR on ggml (EasyOCR and DocTR) reaches full speed parity with the onnx path, across Metal, OpenCL, and Vulkan. PLATFORM AND RELIABILITY - Dynamic compute backends on Linux: one build picks the right backend at runtime, and opens the door to ROCm and CUDA support without per-backend builds. - Thinking tokens are kept out of the model context, so reasoning no longer fills the KV cache. SDK 0.14.0 is now leaner and faster to start. Let’s build.
1:51

QVAC SDK 0.14.0 is live. This release makes the on-device stack faster on mobile, ships the developer-agent path, and takes local text-to-speech to 31 languages. Main highlights: - OpenCode and OpenClaw. The first official OpenCode plugin, plus a maintained OpenClaw compatibility path, both built on managed mode and qvac serve. Point a coding agent at a local model with far less setup and far fewer surprises. - Brain-computer interface transcription, on the SDK. Take recorded neural signal data and decode it into text, fully on-device, no cloud. Stream it in chunks through a simple API. In 0.14 it runs GPU-accelerated on iOS. - Text to Speech in 31 languages with our Supertonic3 upgrade. VOICE AND SPEECH - Supertonic3 multilingual TTS, 5 languages to 31. - Chatterbox and Supertonic now run on the Android GPU, with lower memory use (especially on iOS), quantized s3gen Chatterbox support, and a fix for Chatterbox occasionally emitting random speech. - Whisper transcription now runs on the iOS GPU. Parakeet runs on the Android GPU, with steadier real-time streaming. VISION AND OCR - VLM multi-tile batching: high-resolution Pan and Scan images are encoded in one pass instead of tile by tile, for faster vision throughput. - OCR on ggml (EasyOCR and DocTR) reaches full speed parity with the onnx path, across Metal, OpenCL, and Vulkan. PLATFORM AND RELIABILITY - Dynamic compute backends on Linux: one build picks the right backend at runtime, and opens the door to ROCm and CUDA support without per-backend builds. - Thinking tokens are kept out of the model context, so reasoning no longer fills the KV cache. SDK 0.14.0 is now leaner and faster to start. Let’s build.

QVAC

23,973,950 просмотров • 1 месяц назад

The free UE5.5 AI motion capture plugin is compatible with Unity, Blender, and VMC. It supports both real-time motion capture via webcam and video uploads to generate 3D motion data. Powered by a 1-billion-parameter motion model running locally on your computer, it requires 8GB of VRAM for real-time processing. Compatible with Nvidia, AMD, and Intel GPUs, it can leverage NVIDIA CUDA to significantly boost graphics computation efficiency.
1:25

The free UE5.5 AI motion capture plugin is compatible with Unity, Blender, and VMC. It supports both real-time motion capture via webcam and video uploads to generate 3D motion data. Powered by a 1-billion-parameter motion model running locally on your computer, it requires 8GB of VRAM for real-time processing. Compatible with Nvidia, AMD, and Intel GPUs, it can leverage NVIDIA CUDA to significantly boost graphics computation efficiency.

CYANPUPPETS

17,842 просмотров • 1 год назад

Data encryption is now live on Veilnet. Available in @veilnet/mcp-server@0.3.0. Any blob — file, dataset, config, recovery codes, contract draft, research note, agent memory — can now be encrypted on your device, shipped to Veilnet as ciphertext, and fetched back later without the decryption key ever leaving your machine. Three new MCP tools are live: data_store({ label, data }) encrypt + store data on Veilnetdata_fetch({ id }) fetch + decrypt locallydata_list() list stored ids With labels kept encrypted, the flow is simple: user data → encrypted locally → ciphertext stored on Veilnet → key remains on user device → agent fetches when needed → local decryption onlyVeilnet sees encrypted blobs. Your AI agent gets persistent private memory and secure storage.The provider does not need the raw data. This works in Claude Code, Claude Desktop, Cursor, Continue, and Cline. Install: npm i -g @veilnet/mcp-server Bootstrap your key at: → Agent tab Then ask any MCP-aware agent:“store this encrypted on Veilnet.”Docs:docs.veilnet.toPrivate prompts protected what users ask. Private MCP protected what agents run. Wallet Context protected what agents read. Encrypted data storage now protects what agents remember and reuse. All powered by $VEILNET
0:29

Data encryption is now live on Veilnet. Available in @veilnet/[email protected]. Any blob — file, dataset, config, recovery codes, contract draft, research note, agent memory — can now be encrypted on your device, shipped to Veilnet as ciphertext, and fetched back later without the decryption key ever leaving your machine. Three new MCP tools are live: data_store({ label, data }) encrypt + store data on Veilnetdata_fetch({ id }) fetch + decrypt locallydata_list() list stored ids With labels kept encrypted, the flow is simple: user data → encrypted locally → ciphertext stored on Veilnet → key remains on user device → agent fetches when needed → local decryption onlyVeilnet sees encrypted blobs. Your AI agent gets persistent private memory and secure storage.The provider does not need the raw data. This works in Claude Code, Claude Desktop, Cursor, Continue, and Cline. Install: npm i -g @veilnet/mcp-server Bootstrap your key at: → Agent tab Then ask any MCP-aware agent:“store this encrypted on Veilnet.”Docs:docs.veilnet.toPrivate prompts protected what users ask. Private MCP protected what agents run. Wallet Context protected what agents read. Encrypted data storage now protects what agents remember and reuse. All powered by $VEILNET

Veilnet

11,761 просмотров • 1 месяц назад

Introducing Solode Neo — a personal AI node designed to run and host autonomous AI agents at home or in the office, always on, and entirely under your control. Your AI runs while you sleep. Wake up to compiled research. Automated emails. Organized files. All from a simple message on Telegram or your preferred messaging app. Just plug it in, connect to Wi-Fi, and start working. No coding. No cloud. No limits. Solode Neo comes pre-configured with OpenClaw🦞 and a curated selection of LLMs. It’s built for continuous 24/7 agentic workloads, with all inference, voice processing, and data handling running locally by default. No data leaves your device unless you choose to enable it. The PC put computing on every desk. Now, Solode Neo is here to put autonomous AI in every home, office, and living space. Hardware infrastructure is just the beginning: the full stack is coming. Official pricing at US$399. Early bird pricing at US$369 for waitlist members. Be the first to bring personal AI home: #SolodeNeo #AgenticAI #DigitalInfrastructure #SOLAI $SLAI
0:37

Introducing Solode Neo — a personal AI node designed to run and host autonomous AI agents at home or in the office, always on, and entirely under your control. Your AI runs while you sleep. Wake up to compiled research. Automated emails. Organized files. All from a simple message on Telegram or your preferred messaging app. Just plug it in, connect to Wi-Fi, and start working. No coding. No cloud. No limits. Solode Neo comes pre-configured with OpenClaw🦞 and a curated selection of LLMs. It’s built for continuous 24/7 agentic workloads, with all inference, voice processing, and data handling running locally by default. No data leaves your device unless you choose to enable it. The PC put computing on every desk. Now, Solode Neo is here to put autonomous AI in every home, office, and living space. Hardware infrastructure is just the beginning: the full stack is coming. Official pricing at US$399. Early bird pricing at US$369 for waitlist members. Be the first to bring personal AI home: #SolodeNeo #AgenticAI #DigitalInfrastructure #SOLAI $SLAI

SOLAI Limited

22,407 просмотров • 3 месяцев назад

GOOGLE'S NEW AI FITS IN YOUR POCKET AND THINKS LIKE A NESTING DOLL Google just dropped Gemma 3n, an AI that runs right on your phone with no Wi-Fi needed. It reads text, watches video, hears audio, and even talks back all at once. Powered by “MatFormer,” it’s basically an AI inside an AI inside an AI. Oh and it runs at 60 frames per second while using less memory than your favorite mobile game. Source: rowancheung
0:57

GOOGLE'S NEW AI FITS IN YOUR POCKET AND THINKS LIKE A NESTING DOLL Google just dropped Gemma 3n, an AI that runs right on your phone with no Wi-Fi needed. It reads text, watches video, hears audio, and even talks back all at once. Powered by “MatFormer,” it’s basically an AI inside an AI inside an AI. Oh and it runs at 60 frames per second while using less memory than your favorite mobile game. Source: rowancheung

Mario Nawfal

179,423 просмотров • 1 год назад