This is aligned with our mission to improve the privacy of individuals globally through an uncompromising position on where data should be stored, and in minimizing the impact such storage should have on the global population.
Happy Europe Day! 👋
It’s been an interesting year so far, as we see a significant push to building in sovereign European data centers.
We’re working with a number of sustainability-focused compute providers to make building in Europe as easy as building in the US, (and more cost-effective).
🧩 Privacy-Preservation with Smarter Automotive Experiences by @fillip.pro @ Wasm I/O 2025
▶️ Video: youtu.be/rh9De686BSM
🔗 Slides: 2025.wasm.io/slides/priva... #wasmio25
Exploring the missing piece of AI with WebAssembly at #Wasmio25: training.
Around 6-years of server-side WebAssembly, and WebNN (privacy) work has led me back to the edge.
We’ve come along way and I’m amazed at what the community has built, (and which I’ve had the benefit of building on).
Boulevard crossing in San Jose.
16 people sat drinking on either side of a a moving vehicle, facing each other, where they’re all peddling.
Victory Salute protest statue at San Jose University.
Attending GTC this week where Nvidia, Jaguar Landrover, Zenseact, and Volvo are demoing robotics and self-driving cars.
I’m showcasing how to build safer AI solutions by running training on-device, locally in a secure PDS, whilst collecting zero data.
There’s no better time for safety-by-design.
Next week we’ll be following the GTC talks up with a high-level overview of our blueprint for privacy-preserving AI, and how it stretches far beyond our automotive use cases, into every facet of our lives through portable, secure wallets, enabled through modern edge-based WebAssembly. #wasmio25
Attending the Nvidia GTC event this week where we’re focused on the blueprints for building smarter and more sustainable in-cabin experiences for EVs.
We’re using federated learning, digital twins, and a lot of WebAssembly to orchestrate privacy-preserving intelligence training workloads.
In the long term there are zero reasons to be sharing data abroad.
In the near future we need to be rearchitecting approaches to data usage to reduce the dependency on data sharing for improving services and research capabilities.
Privacy enhancing technologies are largely being underutilized.
This project uses an in-development Naamio Space runtime, which stores data in a Secure Enclave and enables it to be used for training and inference of AI models on-device using WebAssembly and gRPC for privacy-first polyglot AI.