"X in N lines of code" is usually used for compressing an algorithm down to its bare essentials. "Ray tracing in 100 lines of code". In that case, it's disingen

"X in N lines of code" is usually used for compressing an algorithm down to its bare essentials. "Ray tracing in 100 lines of code". In that case, it's disingenuous to have 100 lines of glue code that calls into pov-ray. If I compile 284 lines of code, I expect a few kb executable (including libraries that aren't language-libraries like libc). This hasn't really simplified the speech-recognition algorithm, it still requires large amounts of training data and time, and still does the speech recognition by solving a large matrix with Tensorflow. It's not interesting to me that you can hook up Tensorflow in a few lines of code; I know Tensorflow can solve the problem. However, I would be interested in 284 lines of code that replace Tensorflow, which is what the title suggested to me. A better title would be to focus on how the model and/or the handling of the data produces better results, because that's what this code seems to be about.