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To the OP: you're getting a lot of discouraging comments here. But I had the same idea as you and just didn't take the time. I don't know if it's actually a use

To the OP: you're getting a lot of discouraging comments here. But I had the same idea as you and just didn't take the time. I don't know if it's actually a useful idea or not, but I really do feel it has potential to be useful. As I see it, it's about creating a high-level language that is just concise enough to define what you're doing, without requiring more detail than necessary for the logic, and that can be transpired by GPT into any target language one would want. Recognizing that some fixing of the output may be necessary. So I'm glad you're doing it and I'll keep checking it out to see where you go with it (if indeed you do feel like continuing). Good luck.

ericelliott 4y ago
You're just seeing people's knee jerk reaction to new ideas here. What you don't see in these comments is that SudoLang went viral overnight. Tens of thousands of people heard about it and the blog post comments are overwhelmingly positive. As a programming assistant, SudoLang is already saving me time producing traditional software in JavaScript, but the real unlock is defining unambiguous requirements for complex programs that run directly on the LLM: Chatbots or productivity tools that require NLU - programs with enough complexity that a freeform natural language prompt just doesn't express easily (see the examples folder). SudoLang meaningfully extends our ability to clearly communicate complex ideas with LLMs, and it's a language that LLMs and programmers and beginners learning to code all intuitively understand. It opens up a middle ground between natural language and formally structured language and that does in fact open up new possibilities. The people comparing the syntax of one-liners are missing the point. If you can clearly communicate the idea in natural language, just do that. In fact, that recommendation is right inside the style guide. But you can also do things like declaratively communicate the structure of objects for talking with REST APIs, clearly specify behaviors and requirements, etc.. things that need to be clearly specified and not just invented/improvised/hallucinated by the LLM. SudoLang's infinitely inferable standard function library with modifiers can be easily and declaratively chained to radically simplify chain-of-thought reasoning: a process that helps LLMs think about tasks more carefully and produce better results. It's OK that lots of people don't see the value, yet. It often takes time to warm up to new ideas. When I first started using GPT-3 in 2020 nobody believed me when I said it could code. Today, coders who employ AI tools are 2x faster than those who don't (this stat comes from a study on GitHub copilot comparing 95 devs working on the same task). Thanks for your encouraging remarks.

ericelliott 4y ago

You're just seeing people's knee jerk reaction to new ideas here. What you don't see in these comments is that SudoLang went viral overnight. Tens of thousands of people heard about it and the blog post comments are overwhelmingly positive. As a programming assistant, SudoLang is already saving me time producing traditional software in JavaScript, but the real unlock is defining unambiguous requirements for complex programs that run directly on the LLM: Chatbots or productivity tools that require NLU - programs with enough complexity that a freeform natural language prompt just doesn't express easily (see the examples folder). SudoLang meaningfully extends our ability to clearly communicate complex ideas with LLMs, and it's a language that LLMs and programmers and beginners learning to code all intuitively understand. It opens up a middle ground between natural language and formally structured language and that does in fact open up new possibilities. The people comparing the syntax of one-liners are missing the point. If you can clearly communicate the idea in natural language, just do that. In fact, that recommendation is right inside the style guide. But you can also do things like declaratively communicate the structure of objects for talking with REST APIs, clearly specify behaviors and requirements, etc.. things that need to be clearly specified and not just invented/improvised/hallucinated by the LLM. SudoLang's infinitely inferable standard function library with modifiers can be easily and declaratively chained to radically simplify chain-of-thought reasoning: a process that helps LLMs think about tasks more carefully and produce better results. It's OK that lots of people don't see the value, yet. It often takes time to warm up to new ideas. When I first started using GPT-3 in 2020 nobody believed me when I said it could code. Today, coders who employ AI tools are 2x faster than those who don't (this stat comes from a study on GitHub copilot comparing 95 devs working on the same task). Thanks for your encouraging remarks.