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Comment by tptacek

1 year ago

I use LLMs for three things:

* To catch passive voice and nominalizations in my writing.

* To convert Linux kernel subsystems into Python so I can quickly understand them (I'm a C programmer but everyone reads Python faster).

* To write dumb programs using languages and libraries I haven't used much before; for instance, I'm an ActiveRecord person and needed to do some SQLAlchemy stuff today, and GPT 4o (and o1) kept me away from the SQLAlchemy documentation.

OpenAI talks about o1 going head to head with PhDs. I could care less. But for the specific problem we're talking about on this subthread: o1 seems materially better.

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tptacek

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throwup238  1 year ago

> * To convert Linux kernel subsystems into Python so I can quickly understand them (I'm a C programmer but everyone reads Python faster).

Do you have an example chat of this output? Sounds interesting. Do you just dump the C source code into the prompt and ask it to convert to Python?

  • tptacek  1 year ago

    No, ChatGPT is way cooler than that. It's already read every line of kernel code ever written. I start with a subsystem: the device mapper is a good recent example. I ask things like "explain the linux device mapper. if it was a class in an object-oriented language, what would its interface look like?" and "give me dm_target as a python class". I get stuff like:

        def linear_ctr(target, argc, argv):
        print("Constructor called with args:", argc, argv)
        # Initialize target-specific data here
        return 0
     
    def linear_dtr(target):
        print("Destructor called")
        # Clean up target-specific data here
     
    def linear_map(target, bio):
        print("Mapping I/O request")
        # Perform mapping here
        return 0
     
    linear_target = DmTarget(name="linear", version=(1, 0, 0), module="dm_mod")
    linear_target.set_ctr(linear_ctr)
    linear_target.set_dtr(linear_dtr)
    linear_target.set_map(linear_map)
     
    info = linear_target.get_info()
    print(info)

    (A bunch of stuff elided). I don't care at all about the correctness of this code, because I'm just using it as a roadmap for the real Linux kernel code. The example use case code is an example of something GPT 4o provides that I didn't even know I wanted.