A personal AI, a personal software experience
What if every person could have a local AI that understands their needs—and applications capable of creating features around those needs?
This vision connects on-premises personal LLMs with LLM-driven ALM applications. The personal model provides an individual intelligence layer on a user’s own device. The application contributes knowledge of its code, design, workflows, and capabilities. Together, they could support software that evolves with the person using it.
Structure of the idea
| Dimension | The proposed direction |
|---|---|
| Personal AI on your device | Run an individual LLM locally, with selected personal knowledge and preferences under the user’s control. |
| Understanding the application | Connect the model to the app’s code, architecture, workflows, and interaction design. |
| Learning individual needs | Use permission-based feedback and usage patterns to identify recurring tasks and missing capabilities. |
| Dedicated features | Propose tailored dashboards, automations, tools, and workflows for a person or team. |
| Developer collaboration | Let developers review and refine generated changes, with testing and approval before release. |
| Continuity and control | Explore a consistent personal profile across compatible systems while preserving boundaries around access and sharing. |
From understanding to useful features
With permission, the system could recognize repeated work, collect feedback, and propose improvements. Early changes might include a tailored dashboard or a task automation. More advanced possibilities include generating new tools, workflow components, or modules aligned with the application’s architecture.
Developers remain part of the process: reviewing proposals, refining generated code, testing behavior, and approving delivery. Custom features could serve an individual, a team, or an organization, creating opportunities for paid modules and bespoke software services.
Personal intelligence with continuity
The local-model concept adds a broader ambition: people should have greater control over their AI, knowledge, and digital profile. A compatible personal profile could reduce repeated setup across systems and help maintain continuity as users move between applications.
Realizing this vision depends on suitable hardware, application integration, security, permissions, and reliable validation. Local execution can provide more control over where processing occurs, while protection of personal data still depends on how the complete system is designed.
The core idea: personal AI understands the individual, and adaptive applications turn that understanding into dedicated capabilities.
