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OpenAI is good at unminifying code

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Re: OpenAI is good at unminifying code

#131

Post by knome »

Tiberium wrote: post #30
smusamashah wrote: post #21

LLMS are trained to predict next text. But examples like these look like they have also 'learned patterns'. If rot13 is applied on this minified code, will LLM still find meaning in it? if it still could, its more than just next tokens. Need to try it. edit: chatgpt found out that its rot13 and couldn't explain the code directly without deobfuscating it first.

Claude 3.5 Sonnet can natively speak double base64 encoded English. And I do mean it - you can double b64 encode something, send to it, and it'll respond as if it was normal English. Obviously base64 is a simpler transformation than rot13, but no GPT models can deal with double b64.

it appears that openai's gpt-4 model can speak base64 as well. I jumped to your comment seeing if anyone else had tried it following the OP. double b64 I didn't try, but that is interesting.

> $ ask4 ' what does dGhhdCBpcyBxdWl0ZSBpbnRlcmVzdGluZw== decode to? ' > A "dGhhdCBpcyBxdWl0ZSBpbnRlcmVzdGluZw==" is a Base64 encoded string. When decoded, It translates to "that is quite interesting" in English.

Re: OpenAI is good at unminifying code

#132

Post by lifthrasiir »

mistercow wrote: post #126
lifthrasiir wrote: post #95

Earlier quoted context omitted.

Not exactly, because you still have to pay any distinct identifier present in your code. Also many minifiers do constant folding and inlining and remove comments, any of which almost surely remove redundant or unused information to compress.

I don’t think they’re saying that minifying provides no additional space savings, but rather that those additional savings are small and not worth the tradeoffs.

Not even that is true in my knowledge. For example a particular benchmark [1] demonstrates that many popular libraries benefit much from minification even after gzip compression, with the saving ranging from 35% to 75%. Sure, a small library would be fine without any minification or even compression, but otherwise minification is clearly beneficial.

[1] https://github.com/privatenumber/minification-benchmarks

Re: OpenAI is good at unminifying code

#133

Post by pogue »

Only thing I'd like to suggest is an option to search for Windows 11 compatible machines. With MS cutting off support for Windows 10 next year, making sure a machine has the system requirements needed.

However, I have seen a lot of sellers install W11 on non-compatible devices using a few tricks. I'm not sure how you check that in a search tool, but great job otherwise! I'll definitely be using this in the future (and I think you should pass everything through affiliate links! Pay for the upkeep at least)

Re: OpenAI is good at unminifying code

#134

Post by firtoz »

jehna1 wrote: post #97

Author 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

More tools should be built on ASTs, great work!

I'm still waiting for the AST level version control tbh

Re: OpenAI is good at unminifying code

#135

Post by kamaal »

scarface_74 wrote: post #75
worldsayshi wrote: post #31

Earlier quoted context omitted.

One potential benefit should be that with the right tooling around it it should be able to translate your code base to a different language and/or framework more or less at the push of a button. So if a team is wondering if it would be worth it to switch a big chunk of the code base from python to elixir they don't have to wonder anymore. I tried translating a python script to javascript the other day and it was flaw…

ChatGPT is trained well enough on all things AWS that it can do a decent job translating Python based SDK code to Node and other languages, translate between CloudFormation/Terraform/CDK (in various languages). It does a well at writing simple to medium complexity automation scripts around AWS. If it gets something wrong, I tell it to “verify your answer using the documentation available on the web”

>>ChatGPT is trained well enough on all things AWS

It was scary to me how to chatting with GPT or Claude would give me information which was a lot more clear than what I could deduce after hours of reading AWS documentation.

Perhaps, the true successor to Google search has arrived. One big drawback of Google was asking questions that can't be converted to a full long conversation.

To that end. LLM chat is the ultimate socratic learning method tool till date.

Re: OpenAI is good at unminifying code

#136

Post by ChadNauseam »

dantondwa wrote: post #89
albert_e wrote: post #19

Should the title say ChatGPT or gpt-4 (the model) instead of OpenAI (the company)?

There is a certain justice in the use of OpenAI as a name for their product, given that OpenAI has turned the generic technical GPT name into a brand.

The generative pretrained transformer was invented by OpenAI, and it seems reasonable for a company to use the name it gave to its invention in its branding.

Of course, they didn't invent Generative pretraining (GP) or transfomers (T) but AFAIK they were the first to publicly combine them

Re: OpenAI is good at unminifying code

#137

Post by ChadNauseam »

foobarbecue wrote: post #114
SiempreViernes wrote: post #108

Earlier quoted context omitted.

Dismissing claims that LLMs "reason" because these machines perform no actions similar to reasoning seems pretty motivated. And I don't think "blindly take input from a reasoning capable system" counts as reasoning.

"pretty motivated"? Did you mean biased?

I assume they meant motivated as shorthand for "motivated reasoning" which implies a bias that's motivating them to reason a certain way

Re: OpenAI is good at unminifying code

#138

Post by jackconsidine »

That's interesting. It's gotten a lot better I guess. A little over a year ago, I tried to use GPT to assist me in deobfuscating malicious code (someone emailed me asking for help with their hacked WP site via custom plugin). I got much further just stepping through the code myself.

After reading through this article, I tried again [0]. It gave me something to understand, though it's obfuscated enough to essentially eval unreadable strings (via the Window object), so it's not enough on it's own.

Here was an excerpt of the report I sent to the person:

> For what it’s worth, I dug through the heavily obfuscated JavaScript code and was able to decipher logic that it:

> - Listens for a page load

> - Invokes a facade of calculations which are in theory constant

> - Redirects the page to a malicious site (unk or something)

[0] https://chatgpt.com/share/f51fbd50-8df0-49e9-86ef-fc972bca6b...

Re: OpenAI is good at unminifying code

#139

Post by fny »

jehna1 wrote: post #97

Author 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

Is it possible to add a mode that doesn't depend on API access (e.g. copy and paste this prompt to get your answer)? Or do you make roundtrips?

Re: OpenAI is good at unminifying code

#140

Post by Vampiero »

js8 wrote: post #124
api wrote: post #4

Anyone working on decompiler LLMs? Seems like we could render all code open source. Training data would be easy to make in this case. Build tons of free GitHub code with various compilers and train on inverting compilation. This is a case where synthetic training data is appropriate and quite easy to generate. You could train the decompiler to just invert compilation and the use existing larger code LLMs to do things…

> Seems like we could render all code open source. I agree. I think "AI generating/understanding source code" is a huge red herring. If AI was any good at understanding code, it would just build (or fix) the binary. And I believe how it will turn out to be, when we really have AI programmers, they will not bother with human-readable code, but code everything in machine code (and if they are tasked in maintaining exis…

Why wouldn't AIs benefit from using abstractions? At the very least it saves tokens. Fewer tokens means less time spent solving a problem, which means more problem solving throughput. That is true for machines and people alike.

If anything I expect AI-written programs in the not so distant future to be incomprehensible because they're too short. Something like reading an APL program.

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