AbëONE: Relational AI That Learns Your Cognitive Patterns
We're building AbëONE at Bravëtto—an AI system designed around a fundamentally different premise than current LLMs.
Core innovations:
1. *Persistent Relational Memory* — Not just conversation history, but learned patterns of how individual users think, communicate, and solve problems.
2. *Emotional Intelligence Layer* — Affective computing that recognizes emotional states and adapts responses accordingly. Not sentiment analysis—actual emotional context awareness.
3. *Neuromorphic Architecture* — Event-driven processing that mimics biological neural networks. Result: 60% lower energy consumption than traditional transformer models.
4. *User Evolution Tracking* — The system doesn't just remember what you said; it tracks how your needs and patterns evolve over time.
The technical challenge: How do you build memory that's meaningful without being creepy? Our approach uses client-side preference stores with user-controlled retention.
Early benchmarks:
- 40% improvement in emotional context recognition vs GPT-4
- 60% lower power consumption
- 85% user preference in A/B tests for "feels like it knows me"
Looking for feedback from the community. What are the technical challenges you see in building truly relational AI?
*For developers who want to build the future:* We're actively looking for collaborators—engineers, researchers, and builders who want to be part of something that matters. DM or email [email protected].
www.bravetto.com
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AbëONE: Relational AI That Learns Your Cognitive Patterns · HackerTrans
We're building AbëONE at Bravëtto—an AI system designed around a fundamentally different premise than current LLMs.
Core innovations:
1. *Persistent Relational Memory* — Not just conversation history, but learned patterns of how individual users think, communicate, and solve problems.
2. *Emotional Intelligence Layer* — Affective computing that recognizes emotional states and adapts responses accordingly. Not sentiment analysis—actual emotional context awareness.
3. *Neuromorphic Architecture* — Event-driven processing that mimics biological neural networks. Result: 60% lower energy consumption than traditional transformer models.
4. *User Evolution Tracking* — The system doesn't just remember what you said; it tracks how your needs and patterns evolve over time.
The technical challenge: How do you build memory that's meaningful without being creepy? Our approach uses client-side preference stores with user-controlled retention.
Early benchmarks: - 40% improvement in emotional context recognition vs GPT-4 - 60% lower power consumption - 85% user preference in A/B tests for "feels like it knows me"
Looking for feedback from the community. What are the technical challenges you see in building truly relational AI?
*For developers who want to build the future:* We're actively looking for collaborators—engineers, researchers, and builders who want to be part of something that matters. DM or email [email protected].
www.bravetto.com