55,838 karma · joined May 4, 2018
Hire me to work on open source projects! I've been working on and using open source for about 20 years, principally on the Debian project.
https://bonedaddy.net/pabs3/about/resume/ https://bonedaddy.net/pabs3/about/#other_pages https://bonedaddy.net/pabs3/log/ https://bonedaddy.net/pabs3/about/#contact
https://wiki.openmoko.org/wiki/Neo_Freerunner https://wiki.openmoko.org/wiki/Applications https://wiki.openmoko.org/wiki/QtMoko
Before that Linux ran on the Zaurus too:
https://en.wikipedia.org/wiki/Sharp_Zaurus https://en.wikipedia.org/wiki/OpenZaurus
The Reproducible Builds project also wrote diffoscope, which goes quite far with helping identify where differences occur and how to fix them.
https://reproducible-builds.org/ https://diffoscope.org/ https://try.diffoscope.org/
https://sfconservancy.org/blog/2021/mar/25/install-gplv2/ https://sfconservancy.org/blog/2021/jul/23/tivoization-and-t...
https://www.bbc.com/news/stories-53285610 https://www.telstra.com.au/exchange/5g-health-concerns-and-c... https://www.abc.net.au/news/2020-08-03/5g-conspiracy-theory-...
https://archive.softwareheritage.org/browse/origin/directory...
The xz supply chain attacker hid their real identity, created fakes one and gained recognition over time in order to gain more access and add the backdoor. So TLAs and other bad actors at least are interested in gaining recognition.
https://sfconservancy.org/blog/2018/may/18/tesla-incomplete-... https://sfconservancy.org/blog/2019/oct/30/calling-all-tesla... https://sfconservancy.org/blog/2023/dec/21/tesla-no-source-c...
See also this interesting slide deck about the GPLv3 and cars, I expect that regulations would mean you could not drive cars with modified software (similar to what happens with solar inverters):
https://events19.linuxfoundation.org/wp-content/uploads/2017...
Also, Amazon were already contributing code back when these companies changed their licenses, the companies don't care about code contributions, just money.
You might need old binaries to build it, but shove those in a VM and you should be good to go. If they used Debian, then they could even publish the exact snapshot.debian.org date to download the binaries from, and which binaries.
Perhaps they had proprietary dependencies they couldn't get the code released for, but then you could port the source code to open equivalents.
I'm an external contractor for Software Heritage, not sure if they are currently working on it, but I think they would be an ideal organisation to play that role.
To submit the code, at minimum, you should review and fix the code diff, run the appropriate static analysis tools against it, write the pull request description and commit messages yourself, read the contribution guidelines, make sure everything matches that, disclose that you used AI and for what, and the prompts used.
The Open Source AI Definition (OSAID) is quite ridiculous, I prefer the Debian ML policy for defining freedoms around AI.
This can never be the case.
Both the licensing and source aspects of the Free Software movement are aspiring to create high level of equality of access to a [software] work between both the original author and far downstream recipients. Obviously full and universal equality is impossible because part of the work is only in the author's mind and not everyone can obtain and use computers, but approaching that as closely as possible is important and it is important to think about how to achieve a high level of equality for each work in each context. What is "source" in any given context is a choice the author makes about what level of access they want to pass on to others.
In the case of AI, weights can never be the preferred form for modification because of the equality of access issue. The people who trained the AI (and hide its training data/code but published the weights) will always have more access than the people who only have the weights. Just like a binary can almost never be the preferred form, because the authors have access to the source but we don't.
There are also many ways to bias the model and insert backdoors or other suboptimal behaviours into it during training data selection etc.