Local & Private AI
Exploring language models, speech recognition, retrieval, and agents on locally controlled infrastructure.
Not every AI workload needs to run through a commercial cloud API.
Another area of my research focuses on running language models, speech recognition, embeddings, retrieval systems, and AI agents on locally controlled infrastructure.
The objective is to understand where local AI can provide advantages in:
- Privacy
- Data sovereignty
- Predictable operating costs
- Offline operation
- Reduced dependency on external APIs
- Enterprise data control
- Customization
This research includes experimenting with open-weight language models, local inference servers, speech-to-text systems, vector databases, retrieval-augmented generation, and GPU infrastructure.
This work is particularly relevant to projects such as Memora, where personal conversations and long-term memories may contain highly sensitive information.
Discuss this research
This initiative is evolving. Features and architectures described here are research directions, not claims of a production release. Detailed technical documentation and demonstrations will be added as available.
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