Show HN: I replaced every function in a codebase with English

Tril converts every function in a repo into plain English descriptions, then runs and tests them — using an LLM as the interpreter instead of a runtime.

The concept: programming languages exist because machines couldn't understand human intent. LLMs can. So what happens if you remove the code entirely and just... describe what each function should do?

The tool replaces functions one by one, runs your test suite after each swap to confirm nothing breaks, and outputs a .md file. Then `tril run` spins up an HTTP server that sends each function's English description to Claude and returns the result.

Tested on a unit converter (JS) and a 625-line Python CLI tool — tests passed, results matched to 6 decimal places (fortunately).

This is mainly a thought experiment: can any code become plain natural language? Will it still work? Let's find out!

npm: npx @sliday/tril convert URL

GitHub: https://github.com/sliday/tril

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codingdave 16 hours ago | parent | next [-]

> We invented them because machines couldn't understand our intentions.

This isn't accurate as to why programming languages exist. If you want to see english sentences describing the details of code, as working code, just go look at cobol. Machines understand exactly what we tell them, and do exactly what we tell them. The problem is on our side - how do we make sure we are telling them the correct instructions, while giving ourselves syntax that is easy to work with?

Because of that, your entire premise is wrong. Why would I replace working code with English, just to send it through Claude, just to get back... working code? What does that even prove?

You really are just trying to prove that AI can turn pseudocode into code. It can. By all means, go write stuff to test it and prove it, but I don't think anyone questions this. The questions revolve around whether or not those results are secure and stable, not whether it produces working code.

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kulesh 15 hours ago | parent [-]

Fair point on COBOL—I oversimplified. Programming languages exist for precision and execution, not because machines can't parse English. That framing was sloppy.

But I push back on "the entire premise is wrong."

The interesting part isn't "AI can execute pseudocode"—nobody debates that. The point is the artifact: the .md output matters, not the runtime. A codebase where every function is readable English changes who can participate in a pull request, audit logic, or catch wrong assumptions. "Multiply by 9/5" vs "multiply by 1.8" is an editorial conversation, not a code review.

It's a proof of concept to show the artifact is executable, not a production proposal. It's slow (today), expensive, and non-deterministic - I said so in the post. The question is whether the intermediate representation (English) has value beyond performance? I believe that the loop is shortened here: there're no in-between element intent→weird non-human language→result, it becomes intent→result. No NEED to create synthetic procedures, explaining how the code works in plain language should give us the output.

An old person enters a bank, asks to open an account, speaks plainly in his native language, the teller clicks buttons, and the account is created. From the subjective perspective, there's no in-between interface: the old person had a though, than it got realized.

	▲	doc_ick 13 hours ago \| parent [-]
		Your loop is wrong, as it will go ‘intent -> llm(?) -> result’. Which doesn’t need to compile, but the llm its own black box of magic function that may or may not reproduce the same result based on intent. Also I would think the number of tokens would be concerning, as your example prompt looks 3x larger then the code snippet.

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doc_ick 13 hours ago | parent | prev [-]

A vibe coded thought experiment, interesting but likely not worth the tokens to duplicate work.

I just skimmed the fully vibe coded page and saw the quote “Machines now speak every human language better than most humans.”. Interesting, and maybe possible if languages never change.