Show HN: Adaptive Runtime – AI agent layer, no GPU, crash recovery (github.com)

A new AI solution, Adaptive Runtime, has been introduced as a robust intelligence layer designed to address critical limitations in the lifecycle of AI agents. Unlike traditional frameworks that focus on model performance, Adaptive Runtime emphasizes runtime resilience by maintaining state, managing crash recovery, and enabling contextual decision-making without requiring GPUs. Key features include automatic persistence of agent memory to SQLite, resilient self-healing mechanisms, and adaptive confidence scoring for decision-making under uncertain conditions. This innovation is significant for the AI/ML community as it offers a lightweight and cost-effective solution that can run on minimal infrastructure, such as a $5 VPS or even Raspberry Pi. It alleviates common failures in production AI systems, such as loss of state after crashes, chaotic blind retries, and lack of contextual awareness. By integrating event handling through its Context, Confidence, Decision, State, and Recovery Engines, Adaptive Runtime transforms raw events into actionable insights, ensuring that AI agents can learn from past performance while continually adapting to changing scenarios. Thus, it opens the door for more reliable and efficient deployment of AI systems in real-world applications.