My early contributions to https://github.com/jart/cosmopolitan were focused towards getting a single-file Python executable. I wanted my Python scripts to run on both Windows and Linux, and now they do. To try out Python, you can:
Adding pure-Python libraries just means downloading the wheel and adding files to the binary using the zip command:
./python.com -m pip download Click
mkdir -p Lib && cd Lib
unzip ../click*.whl
cd ..
zip -qr ./python.com Lib/
./python.com # can now import click
Cosmopolitan Libc provides some nice APIs to load arguments at startup, like cosmo_args() [1], if you'd like to run the Python binary as a specific program. For example, you could set the startup arguments to `-m datasette`.
My 4-core computer's probably building Cosmopolitan Libc in the background + the Python binary is 2.7 unoptimized. This blog post is originally from 2021.
Hey, this is my post from 2021, when I was testing Python2.7 and Python3.6 with Cosmopolitan Libc on an old 4-thread Haswell. It's now a lot easier to build Python (and gcc, gnu coreutils, curl etc.), and the binaries are faster, multi-threaded, and quite convenient to use. There are lots of interesting directions to explore when it comes to building software with Cosmopolitan Libc.
> new methods of communication with the compiler have been established.
From what I understand, this appears to a be separate binary from GCC/Clang that does static analysis and outputs C99.
Can this be a GCC plugin? I know we can write plugins that are activated when a specific macro is provided, and the GCC plugin event list allows intercepting the AST at every function declaration/definition. Unless you're rewriting the AST substantially, I feel this could be a compiler plugin. I'd like to know a bit more about what kinds of AST transformations/checks are run as part of Cake.
> Instead, many have deeply underestimated LLMs, saying that after all they were nothing more than somewhat advanced Markov chains, capable, at most, of regurgitating extremely limited variations of what they had seen in the training set. Then this notion of the parrot, in the face of evidence, was almost universally retracted.
I'd like to see this evidence, and by that I don't mean someone just writing a blog post or tweeting "hey I asked an LLM to do this, and wow". Is there a numerical measurement, like training loss or perplexity, that quantifies "outside the training set"? Otherwise, I find it difficult to take statements like the above seriously.
LLMs can do some interesting things with text, no doubt. But these models are trained on terabytes of data. Can you really guarantee "there is no part of my query that is in the training set, not even reworded"? Perhaps we can grep through the training set every time one of these claims are made.
Adding pure-Python libraries just means downloading the wheel and adding files to the binary using the zip command:
Cosmopolitan Libc provides some nice APIs to load arguments at startup, like cosmo_args() [1], if you'd like to run the Python binary as a specific program. For example, you could set the startup arguments to `-m datasette`.
[1]: https://github.com/jart/cosmopolitan/commit/4e9566cd3328626d...