jehna1

I'd bet that the product is not the book, it's the venue: a publisher would pay a lot of ¥ to get a new book to a place where people see their book, not to mention to a place where there's just one book in display and people probably queue to see that specific book.

Super, thank you for adding the link! It really helps to get people to find the tool

Better yet, it already does have support for local LLMs! You can use them via `humanify local`

On structural level it's exactly 1-1: HumanifyJS only does renames, no refactoring. It may come up with better names for variables than the original code though.

At the moment I haven't found good ways of measuring the quality between different models. Please share if you have any ideas!

For small scripts I've found the output to be very similar between small local models and GPT-4o (judging by a human eye).

I'm using both a custom Babel plugin and LLMs to achieve this.

Babel first parses the code to AST, and for each variable the tool:

1. Gets the variable name and surrounding scope as code

2. Asks the LLM to come up with a good name for the given variable name, by looking at the scope where the variable is

3. Uses Babel to make the context-aware rename to AST based on the LLM's response

It does work with any sized file, although it is quite slow if you're using the OpenAI API. HumanifyJS works so it processes each variable name separately, and keeps the context size manageable for an LLM.

I'm currently working on parallelizing the rename process, which should give orders of magnitude faster processing times for large files.

For each variable:

1. It asks the LLM to write a description of what the variable does

2. It asks for a good variable name based on the description from 1.

3. It uses a custom Babel plugin to do a scope-aware rename

This way the LLM only decides the name, but the actual renaming is done with traditional and reliable tools.

There is a fully local mode that does not use ChatGPT at all – everything happens on your local machine.

API access of ChatGPT mode is needed as there are many round trips and it uses advanced API-only tricks to force the LLM output.