NumPy VS mitmproxy

Unmatched integration with ML/AI ecosystems through NumPy, TensorFlow, and PyTorch

> Let’s take a pause here for a second - the ‘CanIndex’ and ‘SupportsIndex’ from the looks are just “int”.

The PR for the change is https://github.com/numpy/numpy/pull/28913 - The details of files changed[0] shows the change was made in 'numpy/__init__.pyi'. Looking at the whole file[1] shows SupportsIndex is being imported from the standard library's typing module[2].

Where are you seeing SupportsIndex being defined as an int?

> I have a hard time dealing with these custom types because they are so obscure.

SupportsIndex is obscure, I agree, but it's not a custom type. It's defined in stdlib's typing module[2], and was added in Python 3.8.

[0]: https://github.com/numpy/numpy/pull/28913/files

[1]: https://github.com/charris/numpy/blob/c906f847f8ebfe0adec896...

[2]: https://docs.python.org/3/library/typing.html#typing.Support...

Know (or check) tells of older versions, such as the python sdk of OpenAI changing from a client with global state in v0.x.x, to a declared instance in v1.x.x, or numpy's change in how random generators are declared.

The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A…

AI starts with math and coding. You don’t need a PhD—just high school math like algebra and some geometry. Linear algebra (think matrices) and calculus (like slopes) help understand how AI models work. Python is the main language for AI, thanks to tools like TensorFlow and NumPy. If you know JavaScript from Vue.js, Python’s syntax is straightforward.

As is the case with most Python libraries, it is open-source and free-to-use, making it easily accessible by anyone willing to learn machine learning, and it is built upon other open-source libraries within Python, like SciPy for advanced scientific operations, NumPy for efficient numerical computations, Matplotlib for data visualization, and Cython for increased efficiency and speed, similar to that of C/C++.

We used mitmproxy. It’s lightweight, easy to run, and gives a clean log of every outbound request.

HTTP proxies like mitmproxy can intercept and export traffic as HAR

> if the rendering engine and network fetching were easily separable - and you could insert your own steps into that pipeline, you could do all sorts of neat stuff.

Can’t that be done relatively easily with https://mitmproxy.org/?

Enter mitmproxy - this beautiful Python program can act as a proxy and logs all network requests. It even comes with a devtool like web UI. Just what I needed.

Years ago, I set up https://mitmproxy.org on a Raspberry Pi and used it to get logs of every site that my kids would visit. I should be clear that monitoring/spying != parenting, but it definitely made me feel a little better to have some idea of what the kids are using the internet for.

From a technical perspective, it did exactly what you want. I had logs of full urls (not just domains). So, for example, I could view what they googled and when, if I wanted to anyway.

It did involve installing a certificate on the computer that they use, but there are how-to guides so setting everything up was simply a matter of following instructions.

The biggest drawback is that it noticeably slowed their internet. I imagine if I had run this on a more powerful computer it may have been better.

The bug issue was reproducible in the production setup, the logs/metrics were not so useful with the clues for the cause. So, I cloned the project code to my laptop and launched a Postgres instance via Docker Compose. Additionally, I started mitmproxy to be able to intercept and inspect HTTP requests on my machine, and created a template of the request to the Internal service API with my own SSN in Postman. My debugging setup was ready, so it was time to run and test the app.

So time to over-engineer this simple problem: since my gym uses EGym / Netpulse, it has Member Card NFC check-ins, which can be accessed via a private API that is called within their App. Using mitmproxy allowed me to quickly identify the check-in related endpoints and the auth mechanism.