Show HN: Rriftt_ai.h – A bare-metal, dependency-free C23 tensor engine (github.com)

The release of rriftt_ai.h marks a significant advancement in the deep learning landscape by offering a bare-metal, dependency-free tensor engine designed in C23. This innovative library allows developers to build, train, and run Transformer models without the burdensome overhead of traditional Python wrappers and complex toolchains. With its single-header format, users can simply drop it into their projects and compile, streamlining the development process by eliminating the need for external libraries and intricate build systems. This lightweight engine notably includes a full suite of neural network functionalities such as backpropagation, Cross-Entropy loss, and the AdamW optimizer, all contained within a memory arena that avoids dynamic memory allocation during execution. By retaining total control over memory allocation and offering features like Byte-Pair Encoding and various advanced algorithms like RoPE and SwiGLU, rriftt_ai.h presents an efficient and robust solution for developers looking to implement advanced AI capability without the typical complexities of modern frameworks. Its active development status and permissive licensing (MIT or Public Domain) further invite contributions and optimizations, promoting a collaborative environment within the AI/ML community.