symbolic VS cocalc

https://stackoverflow.com/questions/62154145/error-python-ipc-popen2-undefined-near-line-62-column-15-when-running-octave and https://github.com/cbm755/octsympy/issues/424 seems really similar to the problem you're having.

I'm experiencing issues with the octave symbolic package very similar to those found here. This issue seems to have been fixed and merged here.

I used this link (https://github.com/cbm755/octsympy/releases) to download the bundle package. After downloading, I put them in the same directory and typed "pkg install symbolic-win-py-bundle-2.9.0.tar.gz" in the command prompt in octave as the directions said and this error message came up:

I have some unit tests for billing and subscription code for my company that started breaking in CI today due to the leap day: https://github.com/sagemathinc/cocalc/commit/8575029c2b76787...

1. I'm integrating ChatGPT extensively into https://CoCalc.com. This integration makes a lot of sense, because cocalc is a platform in which relatively inexperienced students use Jupyter notebooks, linux terminals and Latex. So far, the most popular feature by far is a "Help me fix this" button that appears above stacktraces in Jupyter notebooks.

2. One software engineering challenges is that ChatGPT often outputs code in markdown blocks. I've had to emphasize in prompts that it should explicitly mark the language. I then got inspired to make it possible to evaluate in place the code that appears in these blocks using a Jupyter kernel, and spent a week making that work (so, e.g., if you type a question into the chatgpt box on the landing page at https://cocalc.com, and code appears in the output, often you can just evaluate it right there). There seem to be endless surprises and challenges though. For example, a few minutes ago I realized that sometimes the giant tracebacks one gets when using Python in Jupyter notebooks are so big (even doing simple things with matplotlib) that they end up resulting in too much truncation: https://github.com/sagemathinc/cocalc/issues/6634

3. I'm mostly using GPT-3.5-turbo rather than GPT4, even though I have a GPT4 api key. Aside from costs, GPT4 takes about 4x as long, which often just feels too long for my use case. The average time for a complete response from GPT-3.5 for my application is about 8 seconds, versus over 30s for GPT4.

Github's implementation is really lazy. There are many much better approaches to precisely this problem. E.g., Jupyter notebooks implement one that has matured in the wild over a decade. There's this very flexible markdown-it plugin that implements anther https://github.com/goessner/markdown-it-texmath, and my version of it here https://github.com/sagemathinc/cocalc/blob/master/src/packag... which I rewrote in typescript with a focus on the same semantics as Jupyter has, but for CoCalc, and I've been working on using unifiedjs to provide more general latex for Markdown (not just formulas) here https://github.com/sagemathinc/cocalc/pull/5982 Parsing math is much easier if you use a plugin to an existing markdown parser, rather than trying to do some hack outside of that (which is what Github probably does, and also what Jupyter does).