# QSEE Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/qsee
> Markdown URL: https://aitinkerers.org/technologies/qsee.md
> Technology record last updated: 2026-02-27T15:13:10Z
> Generated: 2026-09-22T14:39:24Z

QSEE provides a hardware-isolated Trusted Execution Environment (TEE) that secures sensitive operations (like biometric processing and key management) on Snapdragon chipsets.

QSEE operates as a dedicated secure world within Snapdragon processors, physically isolating high-stakes processes from the primary Android OS. It hosts Trustlets to handle specific security tasks: processing fingerprint data for Qualcomm Sense ID, managing Widevine L1 DRM for 4K streaming, and securing RSA keys via the Keymaster HAL. By running a proprietary microkernel (built on ARM TrustZone technology), QSEE ensures that even a compromised mobile kernel cannot access the secure memory or hardware-backed credentials. This architecture provides a robust root of trust for millions of mobile devices.

- Official technology site: https://www.qualcomm.com/products/features/security
- Public AI Tinkerers demos and talks: 3
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Encrypted Inferences: A 5‑Minute FHE with FHE‑Studio and FHE Model Marketplace](https://toronto.aitinkerers.org/talks/rsvp_tT7xsSECN2Y)

I’ll present https://fhe-studio.com/, an open-source development environment designed to make encrypted data structures, algorithms, and AI workflows practical for real-world use. We will go through coding in cipher patterns I published at: https://towardsdatascience.com/coding-in-cipher-encrypted-data-structures-and-algorithms-dd99e584a655/ The session will include a live demonstration of building and running computations directly on encrypted data, followed by an overview of an AI Marketplace https://aimarketplace.01com.com/ that enables publishing FHE-based models and running encrypted inference without exposing plaintext data to model owners, infrastructure providers, or operators

- Event context: AI Tinkerers Toronto - January 2026 Meetup at Google! — 2026-01-29 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_tT7xsSECN2Y

### [Stateful AI Without Vendor Lock-In](https://amsterdam.aitinkerers.org/talks/rsvp_S03vGD_udWI)

This talk presents the architecture and live technical demonstration of Intangle, a stateful AI system built around a self-learning memory layer called the Coherence Engine. I will walk through how we securely persist, scope, and retrieve long-term conversational memory while orchestrating an agentic mesh across multiple MCP-compatible model providers. The session will focus on concrete implementation details: encrypted memory graphs, agent governance, execution inside Trusted Execution Environments, and cross-vendor context portability. All examples will be within our production environment and demonstrated live, without slides.

- Event context: Trust & Security in AI Demo Night — Amsterdam Edition — 2025-12-16 — Amsterdam
- Public talk page: https://amsterdam.aitinkerers.org/talks/rsvp_S03vGD_udWI

### [Beyond Public Data: Unlocking AI's Next Frontier with Secure Private Data](https://waterloo.aitinkerers.org/talks/rsvp_cXgF867zQU8)

This session will provide a technical deep dive into building secure data access infrastructure that enables AI systems to train on private data without compromising privacy or security. We'll walk through the complete architecture of a decentralized data access system that uses Trusted Execution Environments (TEEs), encryption, and blockchain technology to create a trustless environment for data access. You'll leave with practical knowledge of the technical challenges in private data aggregation and working code examples you can adapt for your own projects.

- Event context: AI Tinkerers - Waterloo April Meetup — 2025-04-21 — Waterloo
- Public talk page: https://waterloo.aitinkerers.org/talks/rsvp_cXgF867zQU8

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