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hof
Framework that joins data models, schemas, code generation, and a task engine. Language and technology agnostic.
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I've been using https://cuelang.org for any configuration / yaml like generation. This link has a GH search with two discussions that talk about Rego: https://github.com/cue-lang/cue/search?q=rego&type=discussio...
I wrote https://github.com/hofstadter-io/hof to use this concept "at scale", i.e. inputting & outputting multiple files & dirs. The main idea was to generate common code across the stack from a single-source-of-truth. Today it inputs CUE only, which has all the things needed to validate the incoming data and also contains the templates, so `hof gen` takes the same args as `cue export`. It uses diff3 so that you can regenerate the output after modifying the input or the generated content, which is something I needed so that when I fill in the generated API handler func, and then change the design a bit, that I can keep the manual work.
I've been using https://cuelang.org for any configuration / yaml like generation. This link has a GH search with two discussions that talk about Rego: https://github.com/cue-lang/cue/search?q=rego&type=discussio...
I wrote https://github.com/hofstadter-io/hof to use this concept "at scale", i.e. inputting & outputting multiple files & dirs. The main idea was to generate common code across the stack from a single-source-of-truth. Today it inputs CUE only, which has all the things needed to validate the incoming data and also contains the templates, so `hof gen` takes the same args as `cue export`. It uses diff3 so that you can regenerate the output after modifying the input or the generated content, which is something I needed so that when I fill in the generated API handler func, and then change the design a bit, that I can keep the manual work.
I've been using https://cuelang.org for any configuration / yaml like generation. This link has a GH search with two discussions that talk about Rego: https://github.com/cue-lang/cue/search?q=rego&type=discussio...
I wrote https://github.com/hofstadter-io/hof to use this concept "at scale", i.e. inputting & outputting multiple files & dirs. The main idea was to generate common code across the stack from a single-source-of-truth. Today it inputs CUE only, which has all the things needed to validate the incoming data and also contains the templates, so `hof gen` takes the same args as `cue export`. It uses diff3 so that you can regenerate the output after modifying the input or the generated content, which is something I needed so that when I fill in the generated API handler func, and then change the design a bit, that I can keep the manual work.