I've just released a new version of Aista Magic Cloud that allows you to use natural language to create Hyperlambda backend code, and its accuracy is at 90% after only two days of experimenting with the thing. To appreciate this feat, I suggest you watch the following YouTube video.
The way it works is that you start out with a base model. I found Curie to give me the best result. Then you supply OpenAI's API with a bunch of questions and answers. This is the most critical part since the better your questions and answers are, the better your end result will become. I found that extrapolating my questions with variables allowed the model to be able to recognize variables, and substitute these, composing larger blocks of code from smaller blocks more naturally. An example of the above could be, for instance, the following questions, with code snippets solving your questions.