sqlalchemy

or visual data, etc. Here's an example! My own SQLAlchemy repository has a few binary files in it! https://github.com/sqlalchemy/sqlalchemy/blob/main/test/bina... oh noes. Why are those files there? well in this case I just wanted to test that I can send large binary BLOBs into the database driver and I was lazy. This is actually pretty dumb, the two binary files here add 35K of useless crap to the source, and I could just as easily generate this binary data on the fly using a two liner that spits out random bytes. Anyone could see that two liner and know that it isn't embedding a malicious payload. If I wanted to generate a poorly formed .xz file, I'd illustrate source code that generates random data, runs it through .xz, then applies "corruption" to it, like zeroing out the high bit of every byte. The process by which this occurs would be all reviewable in source code. Where I might be totally off here is if you're an image processing library and you want to test filters on an image, and you have the "before" and "after" images, or something similar for audio information, or other scientifically-generated real world datapoints that have some known meaning. That might be difficult to generate programmatically, and I guess even if said data were valid, payloads could be applied steganographically. So I don't know! But just like nobody would ever accept a PR that has a "curl https://some_rando_url/myfile.zip" inside of it, we should not accept PRs that have non-cleartext binary data in them, or package them, without really vetting the contents of those binary files. The simple presence of a binary file in a PR can certainly be highlighted, github could put a huge red banner BINARY FILES IN THIS PR. Downstream packagers for distros like Debian, Redhat etc. would ideally be similarly skeptical of new binary files that appear in the source downloads, and tooling can be applied to highlight the appearance of such files. Packagers would be on the hook to confirm the source of these binary files, or ensure they are deleted (even disabling tests if necessary) before the build process is performed.

)`. the moment you see SQLAlchemy doing something you see that seems "asinine", send an example to https://github.com/sqlalchemy/sqlalchemy/discussions and I will clarify what's going on, correct the usage so that the query you have is what you expect, and quite often we will add new warnings or documentation when we see people doing things we didn't anticipate (such as in this case, I thought we had removed .c by 2.0 but apparently it's still just deprecated. it will be gone in 2.1).

thub much better for the front-facing experience. I wrote about an earlier form of our integration here: https://techspot.zzzeek.org/2016/04/21/gerrit-is-awesome/ to sum up: 1. your project benefits massively by being on Github 2. Gerrit is awesome (for me) 3. gitlab is not very appealing to me UX-wise and self-hosting wise 4. you can still use pull requests from outside users and use gerrit for code reviews.

ty, I used futurize and huge codebase to great success: https://python-future.org/futurize.html. I started with this: futurize --write --stage1 --unicode-literals --nobackups <files> and https://docs.python.org/2/library/2to3.html 2to3-2.7 -w -n <files> Really helped. Also, wire Travis / your CI system to have your tests run on python 2 and 3. If you want to test your python 3 codebase live on a subdomain / same SQL/NoSQL DB, be careful about jobs/tasks! Pickle version mismatches and stuff. Use a separate redis/whatever DB for the deployments. After you're finally on python 3, you can use https://github.com/asottile/pyupgrade to modern your code. [1] For the other 2%, use conditionals in your requirements file: enum34==1.0.4;python_version<"3" ushlex==0.99.1;python_version<"3"