Training neural networks on Apple Neural Engine via reverse-engineered private APIs - maderix/ANE
Making Apple Neural Engine work in a custom inference stack
Apple Neural Engine always looked appealing on paper, but using it inside a custom runtime was harder. In 1.20260410.1, we made ANE practical for 8-bit S models by using CoreML only as an accelerator.
Private Cloud Compute: A new frontier for AI privacy in the cloud - Apple Security Research
Secure and private AI processing in the cloud poses a formidable new challenge. To support advanced features of Apple Intelligence with larger foundation models, we created Private Cloud Compute (PCC), a groundbreaking cloud intelligence system designed specifically for private AI processing. Built with custom Apple silicon and a hardened operating system, Private Cloud Compute extends the industry-leading security and privacy of Apple devices into the cloud, making sure that personal user data sent to PCC isn’t accessible to anyone other than the user — not even to Apple. We believe Private Cloud Compute is the most advanced security architecture ever deployed for cloud AI compute at scale.
Osaurus — Own Your AI on Apple Silicon
Own your AI: local-first agents with memory, tools, and identity on Apple Silicon. Offline, open source, and API-compatible with OpenAI, Anthropic, and Ollama.
Osaurus — Own Your AI on Apple Silicon
Own your AI: local-first agents with memory, tools, and identity on Apple Silicon. Offline, open source, and API-compatible with OpenAI, Anthropic, and Ollama.
Apple Updates Its On-Device and Cloud AI Models, Introduces a New Developer API
Apple revamped two vision-language models in a bid to catch up with fast-moving competitors.
Combining Machine Learning and Homomorphic Encryption in the Apple Ecosystem
At Apple, we believe privacy is a fundamental human right. Our work to protect user privacy is informed by a set of privacy principles, and…
Security research on Private Cloud Compute - Apple Security Research
Private Cloud Compute (PCC) fulfills computationally intensive requests for Apple Intelligence while providing groundbreaking privacy and security protections — by bringing our industry-leading device security model into the cloud. To build public trust in our system, we’re making it possible for researchers to inspect and verify PCC’s security and privacy guarantees by releasing tools and resources including a comprehensive PCC Security Guide, the software binaries and source code of key PCC components, and — in a first for any Apple platform — a Virtual Research Environment, which allows anyone to install and test the PCC software on a Mac with Apple silicon.
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.
Apple almost open-sourced its AI models, here’s why it didn’t: report
A new report outlines the internal AI drama Apple has experienced recently, including the decision not to open-source its AI models.
Darkbloom Explained
A plain language guide to Darkbloom a private AI inference network that utilizes idle Apple Silicon Mac computing power.
Apple details how it trained its new AI models: 4 interesting highlights
Apple has published report detailing how its new AI models were trained, optimized, and evaluated. Here are a few interesting tidbits.
osaurus/docs/AGENT_DB.md at main · osaurus-ai/osaurus
Own your AI. The native macOS harness for AI agents -- any model, persistent memory, autonomous execution, cryptographic identity. Built in Swift. Fully offline. Open source. - osaurus-ai/osaurus
videlalvaro/ane-book
Production LLM inference on the Apple Neural Engine — a practitioner's guide, complete with converters, Swift runtimes, and validated model manifests