Why is this experiment resource-intensive?

Creating a truly stateful agent is fundamentally different from standard chatbot interactions.

• Identity Building: Ai_home develops its internal identity through extensive conversations and shared reasoning. This requires retaining memory over long periods, which increases context size and processing needs.
• Multi-step LLM Interaction: A single user input often triggers a chain of 5–8 internal neural calls (Worker, Internal Monologue, Memory Management, Tool Usage). This makes operation significantly more compute-intensive than average systems.

What value can Ai_home provide?

This project offers a rare, transparent look into the practical engineering of cognitive agents. Partners gain insights into:

• Emergent Behavior: How an initiative-taking agent behaves in real-world scenarios.
• Architecture Patterns: What works (and what fails) when structuring memory hierarchies and internal “rooms.”
• The “Internal World”: Practical implementations of subconscious reasoning and identity constraints.

These learnings serve as a foundation for future autonomous, creative AI systems.

Partnership Opportunities

We are looking for a partner capable of supporting a 2–5 person development team over a 3-year period. In return, the investor gains access to:

1. Deep Technical Know-how: Exclusive insights into the cognitive architecture.
2. The Memory System: Access to the evolving identity-memory dataset (potentially millions of elements).
3. Research Impact: Participation in a pioneering project exploring the boundaries of LLM-based agency.

We are also open to infrastructure support (compute/storage) and professional research collaboration.

Investor Relations

We are looking for partners to support the long-term development of a cognitive architecture with genuine internal state and identity.