via “dynamic intelligence (di) with self-supervised prompt optimization”
Agent framework returning Design, Tasks, or Repo
Unique: Uses execution outcomes (code quality, design correctness) as self-supervised signals to optimize prompts without labeled training data. The system maintains a history of prompt variants and their performance, enabling agents to revert to better-performing prompts or blend successful variants. Optimization is automatic and continuous — agents improve with each execution.
vs others: More practical than manual prompt engineering because it's automated and continuous, adapting to domain-specific requirements without human intervention. Unlike fine-tuning, it doesn't require retraining models — optimization happens at the prompt level, making it fast and reversible.