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Introducing BlackBird Enterprise — a structurally private AI platform that runs where your data already lives: on-device, on-prem, or in your cloud. Built for orgs that refuse to compromise on: • Data residency, compliance & guardrails • Structural privacy by design • On-device/on-prem RAG, document AI, copilots • Distributed...

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Decompute AI

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21,912 просмотров • 8 месяцев назад •via X (Twitter)

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SecureGPT Mobile Integration We are thrilled to announce that SecureGPT will be fully integrated with EdgeAI on iOS, bringing our powerful privacy protections directly to your mobile devices! This isn't just another app update - it's a fundamental shift in how you can interact with AI on mobile: • Anonymous Authentication: Your device connects to our services without revealing your identity - even to us • TOR-Protected Queries: Every AI interaction is routed through secure TOR circuits, ensuring complete network privacy • On-Device Sanitization: Your sensitive information is filtered locally before leaving your phone For too long, mobile AI has meant sacrificing privacy. Soon, that changes. Whether you are researching sensitive topics, handling confidential work, or simply value your privacy, you can access the full power of AI without compromising your data.
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SecureGPT Mobile Integration We are thrilled to announce that SecureGPT will be fully integrated with EdgeAI on iOS, bringing our powerful privacy protections directly to your mobile devices! This isn't just another app update - it's a fundamental shift in how you can interact with AI on mobile: • Anonymous Authentication: Your device connects to our services without revealing your identity - even to us • TOR-Protected Queries: Every AI interaction is routed through secure TOR circuits, ensuring complete network privacy • On-Device Sanitization: Your sensitive information is filtered locally before leaving your phone For too long, mobile AI has meant sacrificing privacy. Soon, that changes. Whether you are researching sensitive topics, handling confidential work, or simply value your privacy, you can access the full power of AI without compromising your data.

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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.
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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.

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Building a truly private AI model isn’t as simple as the tech world wants you to believe. despite all the marketing around one click deployments, RAG systems, fine tuning services, and memory features, we still don’t have models that are genuinely trained only on your personal data with complete privacy from third parties. the reality is that most AI solutions today require you to trust someone else with your books, notes, journals, and training data. even when companies promise privacy, there’s usually a way for the provider or other parties to access your information during training or inference. Phala is taking a different approach by building the infrastructure needed for real data ownership. They’re developing confidential runtime environments where your AI training happens inside secure enclaves with attestation guarantees. The key innovation is their in-enclave keying system through dstack, which means that once your model is training, even Phala themselves cannot see your data or model weights.

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🧠 Dell is rethinking what enterprise AI can do — and where it lives. At Dell Tech World, Sam Grocott shared how Dell Technologies is deploying Glean’s Work AI Platform on-prem — blending public LLM intelligence with private infrastructure to power sensitive, high-trust use cases. The result? AI that acts on your behalf to: ✅ Write emails ✅ Prep meetings ✅ Push deals forward — autonomously This is Dell’s vision for agentic AI: a 24/7 teammate driving go-to-market productivity to new heights — securely, efficiently, and always on. Get the full story:

Six Five Media

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We're excited to be building on Venice, the leading private AI platform, to solve the harder version of the problem: privacy you can prove. Venice is the only AI platform of its scale built private by default. Prompts are never stored, conversations never leave your device, and no third party (not Venice, not the infrastructure, not anyone) can see what you ask. Inference runs inside sealed hardware enclaves, and every response ships with cryptographic attestation that it stayed private. Cluster is the orchestration and settlement layer on top: routing sensitive workloads to Venice's verified private compute and letting autonomous agents pay per call over x402 on Base. No account, no API keys, no data trail.

Cluster Protocol

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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

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VoiceBox just did what everyone thought was impossible. It might have killed ElevenLabs overnight. No subscription. No cloud. No credits. Runs locally on your machine. Clones a voice from 3 seconds of audio. Powered by Qwen 3 TTS. Apache 2.0 license. Commercial use allowed. And your voice data never leaves your computer. For years, if you wanted high quality voice cloning, ElevenLabs was the default. Now there’s a free open-source alternative that sounds shockingly close. Plus it has a built-in multi-track editor, recording, transcription with Whisper, and a local API. This isn’t just another TTS tool. It’s a full voice production studio on your desktop. Open source AI is catching up fast. And this changes the game for creators and businesses. Would you trust the cloud with your voice… or keep it local? 👇

Julian Goldie SEO

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Tired of generic, hallucinating LLMs? We use AI for almost everything these days, whether it’s helping with math homework or looking up remedies when we’re sick. It's become part of our daily routines. Now imagine if Einstein helped you solve that homework, or Eminem wrote the lyrics for your next rap song. What if your AI was built specifically for your world? Trained on your domain, your data, you become the master of it. Introducing OpenLedger AI Studio. A powerful platform to build and use specialized models with built-in explainability and attribution. Fine-tune using your data through our Model Factory, and deploy them seamlessly. Your models won’t just reason - With OpenLedger, you can explain every decision, trace its data, and reward contributors. Truly Open, Verified, and Explainable AI begins here.

OpenLedger

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THEY RAN STATE OF THE ART AI ON A 26 YEAR OLD IMAC WITH ZERO INTERNET not a demo. not a benchmark a real answer in near instant time on hardware older than most people reading this your subscription money is going to a data center that doesn't need it the future already runs locally on dead hardware from 1999 bookmark this before you pay for another month of cloud AI you don't need
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THEY RAN STATE OF THE ART AI ON A 26 YEAR OLD IMAC WITH ZERO INTERNET not a demo. not a benchmark a real answer in near instant time on hardware older than most people reading this your subscription money is going to a data center that doesn't need it the future already runs locally on dead hardware from 1999 bookmark this before you pay for another month of cloud AI you don't need

leopardracer

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Half an hour into the most recent Joe Rogan podcast, Joe and Matthew McConaughey imagined AI that could deeply understand you, learn from your private data, and give proactive support without sacrificing your privacy. AI that could know your favorite books, your personal information, even your thought patterns, and return insights to help you understand yourself better. They spoke of this like a vision for the future, but at LedgerAI, that future already exists. AuraVision is our private AI platform that keeps your data where it belongs, with you. •Your data is stored locally, not in the cloud. •You can ingest AuraVision with exquisite data about yourself or your company: your strategies, workflows, knowledge, and goals. • In return, AuraVision provides actionable advice and intelligence, turning your private information into secure, personal insights. Whether you are a doctor, lawyer, government body, a financial institution, or even Joe Rogan or Matthew McConaughey, AuraVision makes AI adoption real, practical, and secure, accessed via our native token, $LEDGER. The difference is simple: while others are still imagining, we’re already building. Train by day, AuraVision by night, all day! We’ve left a link to the episode in the replies, leave your thoughts!
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Half an hour into the most recent Joe Rogan podcast, Joe and Matthew McConaughey imagined AI that could deeply understand you, learn from your private data, and give proactive support without sacrificing your privacy. AI that could know your favorite books, your personal information, even your thought patterns, and return insights to help you understand yourself better. They spoke of this like a vision for the future, but at LedgerAI, that future already exists. AuraVision is our private AI platform that keeps your data where it belongs, with you. •Your data is stored locally, not in the cloud. •You can ingest AuraVision with exquisite data about yourself or your company: your strategies, workflows, knowledge, and goals. • In return, AuraVision provides actionable advice and intelligence, turning your private information into secure, personal insights. Whether you are a doctor, lawyer, government body, a financial institution, or even Joe Rogan or Matthew McConaughey, AuraVision makes AI adoption real, practical, and secure, accessed via our native token, $LEDGER. The difference is simple: while others are still imagining, we’re already building. Train by day, AuraVision by night, all day! We’ve left a link to the episode in the replies, leave your thoughts!

AuraVision

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Our integration with Walrus 🦭/acc is now live! 1. Upload a CSV or Parquet file to Baselight 2. Flip it from “Private” to “Public” to share it with the community 3. Your file is stored on Walrus, structured by Baselight, and ready for AI-powered analysis Start publishing data that thinks with you 👇
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Our integration with Walrus 🦭/acc is now live! 1. Upload a CSV or Parquet file to Baselight 2. Flip it from “Private” to “Public” to share it with the community 3. Your file is stored on Walrus, structured by Baselight, and ready for AI-powered analysis Start publishing data that thinks with you 👇

Baselight

30,406 просмотров • 8 месяцев назад

🌟Quilibrium’s AI Breakthrough: Encrypted Training on CPUs In her latest live stream ( - minute 14) Cassie unveiled a groundbreaking AI training method that allows models to be trained on encrypted data using CPUs while achieving performance comparable to Nvidia’s A100 GPU (blue line in the graph below). Traditionally, AI training requires expensive GPUs because matrix multiplications—the core of deep learning—are highly computational. Running these calculations on CPUs is painfully slow, often taking hours or days for even small models. The problem worsens when trying to train AI on encrypted data, as standard encryption methods add a massive computational burden. Quilibrium’s breakthrough removes this bottleneck. Instead of relying on traditional matrix multiplication, their method uses a completely different mathematical approach, allowing AI models to be trained securely and efficiently without exposing the raw data. Cassie didn’t reveal the exact technique, only hinting that it’s inspired by existing AI research and will be detailed in a future open-source AGPL-licensed paper. The key advantage? AI can now be trained at GPU speeds on standard CPUs, making privacy-preserving machine learning far more accessible. This innovation has major implications. It slashes AI infrastructure costs, allowing organizations to train powerful models without investing in expensive hardware. It also enables private AI training on personal or corporate data without revealing sensitive information, a game-changer for industries like healthcare and finance. If Quilibrium’s method delivers on its promise, it could reshape AI development, making privacy-first computing the new standard. $QUIL $wQUIL
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🌟Quilibrium’s AI Breakthrough: Encrypted Training on CPUs In her latest live stream ( - minute 14) Cassie unveiled a groundbreaking AI training method that allows models to be trained on encrypted data using CPUs while achieving performance comparable to Nvidia’s A100 GPU (blue line in the graph below). Traditionally, AI training requires expensive GPUs because matrix multiplications—the core of deep learning—are highly computational. Running these calculations on CPUs is painfully slow, often taking hours or days for even small models. The problem worsens when trying to train AI on encrypted data, as standard encryption methods add a massive computational burden. Quilibrium’s breakthrough removes this bottleneck. Instead of relying on traditional matrix multiplication, their method uses a completely different mathematical approach, allowing AI models to be trained securely and efficiently without exposing the raw data. Cassie didn’t reveal the exact technique, only hinting that it’s inspired by existing AI research and will be detailed in a future open-source AGPL-licensed paper. The key advantage? AI can now be trained at GPU speeds on standard CPUs, making privacy-preserving machine learning far more accessible. This innovation has major implications. It slashes AI infrastructure costs, allowing organizations to train powerful models without investing in expensive hardware. It also enables private AI training on personal or corporate data without revealing sensitive information, a game-changer for industries like healthcare and finance. If Quilibrium’s method delivers on its promise, it could reshape AI development, making privacy-first computing the new standard. $QUIL $wQUIL

Quilibrium Community

18,919 просмотров • 1 год назад

🚨 do you understand what happened to Apple.. At WWDC, Apple confirmed that Siri's cloud layer now runs on a custom Google Gemini model with 1.2 trillion parameters, licensed for roughly $1 billion a year. Strategically, this is a reversal. The company that built its brand on privacy and vertical integration has handed the core of its AI stack to a direct competitor. The same capabilities Apple announced at WWDC 2024 and never shipped — personal context, on-screen awareness, cross-app actions — now depend on Google Cloud. → $250M settlement in May for marketing Siri features that didn't exist → debuted at WWDC 2024, delayed March 2025, ads pulled → Tim Cook's final keynote before stepping down September 1 → Siri's cloud inference now runs on Google's infrastructure Apple didn't close the AI gap. It leased it. Pragmatic move or structural capitulation in the AI race?
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🚨 do you understand what happened to Apple.. At WWDC, Apple confirmed that Siri's cloud layer now runs on a custom Google Gemini model with 1.2 trillion parameters, licensed for roughly $1 billion a year. Strategically, this is a reversal. The company that built its brand on privacy and vertical integration has handed the core of its AI stack to a direct competitor. The same capabilities Apple announced at WWDC 2024 and never shipped — personal context, on-screen awareness, cross-app actions — now depend on Google Cloud. → $250M settlement in May for marketing Siri features that didn't exist → debuted at WWDC 2024, delayed March 2025, ads pulled → Tim Cook's final keynote before stepping down September 1 → Siri's cloud inference now runs on Google's infrastructure Apple didn't close the AI gap. It leased it. Pragmatic move or structural capitulation in the AI race?

Prometheus

1,270,309 просмотров • 1 месяц назад

Still buzzing from our live #COMPUTEX demos! Watching Chang Liu speak Mandarin while an AMD AI PC translated her words into English — live, on-device, with zero cloud assistance — was a true milestone. This demo proved real-time, voice-to-voice translation can run privately and with low latency on a single Ryzen AI system. Huge thanks to Nick Ni for orchestrating this seamless demo, Chang for her perfect delivery, and the entire AMD AI Group for turning months of hard work into that unforgettable moment. Why it matters: • Latency so low it feels natural — conversations flow smoothly • Data stays on-device — privacy uncompromised This is what next-gen Ryzen AI processors unlock — and it’s only the beginning. We’re committed to improving accuracy and reducing latency as we push the boundaries of what’s possible.
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Still buzzing from our live #COMPUTEX demos! Watching Chang Liu speak Mandarin while an AMD AI PC translated her words into English — live, on-device, with zero cloud assistance — was a true milestone. This demo proved real-time, voice-to-voice translation can run privately and with low latency on a single Ryzen AI system. Huge thanks to Nick Ni for orchestrating this seamless demo, Chang for her perfect delivery, and the entire AMD AI Group for turning months of hard work into that unforgettable moment. Why it matters: • Latency so low it feels natural — conversations flow smoothly • Data stays on-device — privacy uncompromised This is what next-gen Ryzen AI processors unlock — and it’s only the beginning. We’re committed to improving accuracy and reducing latency as we push the boundaries of what’s possible.

Jack Huynh

14,634 просмотров • 1 год назад