Core Viewpoint - The article emphasizes that while domestic computing infrastructure has improved, the real challenge for developers lies in the usability of these systems, particularly in the context of AI development, where the existing software ecosystem remains heavily reliant on established foreign tools and frameworks [1][2]. Group 1: Current State of AI Development - The AI landscape is vibrant with numerous models being released, yet the underlying software ecosystem's maturity is a significant bottleneck for deployment efficiency [11][12]. - The development of high-performance operators (算子) is crucial as they serve as the "translators" between AI algorithms and hardware, impacting inference speed, energy consumption, and compatibility [13][14]. Group 2: KernelCAT Introduction - KernelCAT is introduced as a local AI agent designed to accelerate computing and facilitate model migration, capable of handling both specialized tasks and general software engineering duties [17]. - Unlike traditional tools, KernelCAT combines intelligent code understanding and optimization with operational research algorithms to automate parameter tuning, significantly reducing the time and effort required for optimization [21][22]. Group 3: Performance and Competitive Edge - In tests, KernelCAT demonstrated superior performance compared to both open-source and commercial operators, achieving execution times as low as 0.0077 ms for 1M scale tasks, which translates to acceleration ratios exceeding 200% [26]. - KernelCAT's unique approach allows it to optimize operators effectively, showcasing its potential to compete with established solutions in the market [25][27]. Group 4: Ecosystem Challenges - The article highlights that over 90% of significant AI training tasks currently run on NVIDIA GPUs, with a developer ecosystem that includes over 5.9 million users and more than 400 operators, indicating a substantial barrier for domestic alternatives [28][30]. - The success of NVIDIA is attributed to its comprehensive control over software and algorithms, underscoring the importance of a mature ecosystem for hardware performance to be fully realized [32]. Group 5: Future Directions - KernelCAT represents a shift towards building self-evolving computational foundations, moving away from reliance on existing ecosystems to developing capabilities that can adapt and grow independently [39]. - The article concludes with an invitation for users to experience KernelCAT, indicating its ongoing development and potential for broader adoption in the industry [40].