I have to ask the obvious question: how do you know the unminified code is semantically equivalent to the minified code? If someone knows how to verify LLM code transformations for semantic fidelity then I'd like to know because I think that would qualify as a major breakthrough for programming languages and semantics.
OpenAI is good at unminifying code
241–250 of 321 posts
Re: OpenAI is good at unminifying code
#242This is an example of superior intellectual performance to humans. There’s no denying it. This task is intellectual. Does not involve rote memorization. There are not tons and tons of data pairs on the web of minimized code and unminified code for llms to learn from. The llm understands what it is unminifying and it is in general superior to humans on this regard. But only in this specific subject.
Yeah, ok. Now count the number of Rs in this word.
Re: OpenAI is good at unminifying code
#243Earlier quoted context omitted.
Because of how trivial that step is, it's likely pretty easy to just take lots of code and minify it. Then you have the training data you need to learn to generate full code from minified code. If your goal is to generate additional useful training data for your LLM, it could make sense to actually do that.
I suspect, but definitely do not know, that all the coding aspects of llms work something like this. It’s such a fundamentally different problem from a paragraph, which should never be the same as any other paragraph. Seems to me that coding is a bit more like the game of go, where an absolute score can be used to guide learning. Seed the system with lots and lots of leetcode examples from reality, and then train it…
If you're able to generate minified code from all the code you can find on the internet, you end up with a very large training set. Of course in some scenarios you won't know what the original variable names were, but you would expect to be able to get something very usable out of it. These things, where you can deterministically generate new and useful training data, you would expect to be used.
Re: OpenAI is good at unminifying code
#244This is an example of superior intellectual performance to humans. There’s no denying it. This task is intellectual. Does not involve rote memorization. There are not tons and tons of data pairs on the web of minimized code and unminified code for llms to learn from. The llm understands what it is unminifying and it is in general superior to humans on this regard. But only in this specific subject.
I think I’d agree with your statement, in the same sense that a chess simulator or AlphaGo are superior to human intellect for their specific problem spaces.
LLMs are very good at a surprisingly broad array of semi-structured-text-to-semi-structured-text transformations, particularly within the manifold of text that is widely available on the internet.
It just so happens that lots of code is widely available on the internet, so LLMs tend to outperform on coding tasks. There’s also lots of marketing copy, general “encyclopedic” knowledge, news, human commentary, and entertainment artifacts (scripts, lyrics, etc). LLMs traverse those spaces handily as well. The capabilities of AI ultimately boil down to their underlying dataset and its quality.
Re: OpenAI is good at unminifying code
#245Earlier quoted context omitted.
It's just a rule of thumb, like anything else. In most code, "why" is the hard part; I see that you are incrementing that account by a penny from out of the blue, but why? When you are in code where "what" is the hard part, like an implementation of a book algorithm or some tricky performance optimization, then by all means comment that. Really all this rule amounts to is // Increment by a penny accountValue += 1 is…
I do agree that documenting the why is way more important than the how/what. But having a short comment to summarize a block of code like: // Parse the filename and remove the extension let fext_re = Regex::new(r"(.\*)\.(.+)$").unwrap(); let page_cap = fext_re.captures(fname).unwrap(); let page_base_filename = page_cap.get(1).unwrap().as_str(); Is still useful. Instead of having to read the next few line of code, I a…
If you didn't name you variables "fext_re" or "page_cap" you wouldn't need that comment to explain what the code does.
Re: OpenAI is good at unminifying code
#246I'm sure there's some number greater than zero of developers who are upset because they use minification as a means of obfuscation.
Reminds me of the tool that was provided in older versions of ColdFusion that would "encrypt" your code. It was a very weak algorithm, and didn't take long for someone to write a decrypter. Nevertheless some people didn't like this, because they were using this tool, thinking it was safe for selling their code without giving access to source. (In the late 90s/early 2000s before open source was the overwhelming default)
Re: OpenAI is good at unminifying code
#247This is an example of superior intellectual performance to humans. There’s no denying it. This task is intellectual. Does not involve rote memorization. There are not tons and tons of data pairs on the web of minimized code and unminified code for llms to learn from. The llm understands what it is unminifying and it is in general superior to humans on this regard. But only in this specific subject.
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Re: OpenAI is good at unminifying code
#248Author of HumanifyJS here! I've created specifically a LLM based tool for this, which uses LLMs on AST level to guarantee that the code keeps working after the unminification step: https://github.com/jehna/humanify
Does it work with huge files? I'm talking about something like 50k lines. Edit: I'm currently trying it with a mere 1.2k JS file (openai mode) it's only 70% done after 20 minutes. Even if it works therodically with 50k LOC file, I don't think you should try.
It has this in the README
> Large files may take some time to process and use a lot of tokens if you use ChatGPT. For a rough estimate, the tool takes about 2 tokens per character to process a file:
> echo "$((2 * $(wc -c So for refrence: a minified bootstrap.min.js would take about $0.5 to un-minify using ChatGPT.
> Using humanify local is of course free, but may take more time, be less accurate and not possible with your existing hardware.
Re: OpenAI is good at unminifying code
#249This is an example of superior intellectual performance to humans. There’s no denying it. This task is intellectual. Does not involve rote memorization. There are not tons and tons of data pairs on the web of minimized code and unminified code for llms to learn from. The llm understands what it is unminifying and it is in general superior to humans on this regard. But only in this specific subject.
> Does not involve rote memorization. There are not tons and tons of data pairs on the web of minimized code and unminified code for llms to learn from.
GPT-4 has consumed more code than your entire lineage ever will and understands the inherent patterns between code and minified versions. Recognizing the abstract shape of code sans variable names and mapping in some human readable variable names from a similar pattern you've consumed from the vast internet doesn't seem farfetched.
Re: OpenAI is good at unminifying code
#250Author of HumanifyJS here! I've created specifically a LLM based tool for this, which uses LLMs on AST level to guarantee that the code keeps working after the unminification step: https://github.com/jehna/humanify
Would it be difficult to add a 'rename from scratch' feature? I mean a feature that takes normal code (as opposed to minified code) and (1) scrubs all the user's meaningful names, (2) chooses names based on the algorithm and remaining names (ie: the built-in names). Sometimes when I refactor, I do this manually with an LLM. It is useful in at least two ways: it can reveal better (more canonical) terminology for names…
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