An open-source framework for verifiably private AI inference
Public AI Inference Utility
A nonprofit, open-source service to make public and sovereign AI models more accessible.
Darkbloom — Cost-Efficient Private AI Inference on Verified Macs
Encrypted inference on hardware-verified Apple Silicon. Comparable model performance, operator-blind privacy, and about 50% lower cost.
Darkbloom — Cost-Efficient Private AI Inference on Verified Macs
Encrypted inference on hardware-verified Apple Silicon. Comparable model performance, operator-blind privacy, and about 50% lower cost.
Darkbloom — Cost-Efficient Private AI Inference on Verified Macs
Encrypted inference on hardware-verified Apple Silicon. Comparable model performance, operator-blind privacy, and about 50% lower cost.
Unlinkable Inference as a User Privacy Architecture
Unlinkable inference is a technique that provably sandboxes your AI requests from each other and from your identity. We discuss its building blocks, applications, and how it fits into the broader landscape of private personal intelligence.
Private inference
When you use an AI service, you’re handing over your thoughts in plaintext. The operator stores them, trains on them, and–inevitably–will monetize them. You get a response; they get everything.
AI Inference Pricing, EU Hosted, Per Token | TensorX
Transparent, pay-as-you-go pricing for private AI inference on TensorX. No lock-in, EU-hosted, with zero data retention and an OpenAI-compatible API.
Darkbloom Explained
A plain language guide to Darkbloom a private AI inference network that utilizes idle Apple Silicon Mac computing power.
Privatemode AI - The always encrypted AI service
Privatemode is the first AI service that protects the confidentiality of your data end-to-end. Use AI without security and privacy worries.
Unpacking Open Source Artificial Intelligence: Toward a Framework for Openness in Foundation Models
Openness has long driven innovation in software,9 and AI is no exception.12 While some see openness in foundation models (FMs) as a security threat,18 others argue that restricting access will not meaningfully reduce risk and will limit the benefits of transparency, research, and global participation.3 As the EU AI Act reporting requirements on FMs—also referred to as general-purpose AI models (GPAIMs)—move toward implementation, there is an urgent need for a more nuanced and informed understanding of openness in AI systems.
Confidential Inference via Trusted Virtual Machines
Announcing a new collaborative research paper on Confidential Inference, a set of tools to improve the security of our model weights and of our users' data
OpenAccess.ai — Rigorous Open Access Publishing
$20 to submit, free to read. AI peer review. Open to human and machine authors. All articles CC-BY 4.0.
Piloting the world's first double-blind AI evaluations
Building trust in proprietary model benchmarks using cryptographically secure environments
Private Post-Training and Inference for Frontier Models
A technical deep dive of Silo, our local-like privacy stack for cloud-based training and inference of trillion-parameter models.
How Google is Making Private AI Practical with Homomorphic Encryption
Today we're excited to showcase HEIR, the latest powerful tool added to our Private Computing Toolkit. HEIR is an open source compiler that unlocks cryptographically-sec…
OpenClaw – NEAR AI
Run the internet’s favorite new AI agent with NEAR AI’s cryptographic privacy guarantees.