Research
Hyperplane Labs focuses on foundational infrastructure challenges that limit AI's ability to serve as a genuine collaborator in personal computing.
Memory Systems
Current AI systems lack persistent, contextual memory. We research architectures that allow AI to maintain and reason over long-term context while preserving privacy and computational efficiency.
Secure Agent Runtimes
As AI agents become more autonomous, secure execution environments are critical. We develop runtime systems that enable AI agency while maintaining strict security boundaries and user control.
Reinforcement Learning Environments
AI agents require rich, persistent environments to learn complex behaviors. We research RL environments that enable agents to develop genuine understanding through interaction with realistic, long-horizon tasks in personal computing contexts.
Privacy-First Machine-Human Collaboration
AI systems need access to personal data to be useful, but this creates privacy risks. We explore techniques that enable AI-human collaboration without compromising data sovereignty.
European AI Infrastructure
We contribute to developing European standards and infrastructure for AI deployment, reducing dependence on non-European platforms while ensuring GDPR compliance by design.