pyfound.blogspot.com
The PSF has withdrawn $1.5 million proposal to US government grant program
In January 2025, the PSF submitted a proposal to the US government National Science Foundation under the Safety, Security, and Privacy of Open Source Ecosystems program to address structural vulnerabilities in Python and PyPI. It was the PSF’s first time applying for government funding, and navigating the intensive process was a steep learning curve for our small team to climb. Seth Larson, PSF Security Developer in Residence, serving as Principal Investigator (PI) with Loren Crary, PSF Deputy Executive Director, as co-PI, led the multi-round proposal writing process as well as the months-long vetting process. We invested our time and effort because we felt the PSF’s work is a strong fit for the program and that the benefit to the community if our proposal were accepted was considerable.
We were honored when, after many months of work, our proposal was recommended for funding, particularly as only 36% of new NSF grant applicants are successful on their first attempt. We became concerned, however, when we were presented with the terms and conditions we would be required to agree to if we accepted the grant. These terms included affirming the statement that we “do not, and will not during the term of this financial assistance award, operate any programs that advance or promote DEI, or discriminatory equity ideology in violation of Federal anti-discrimination laws.” This restriction would apply not only to the security work directly funded by the grant, **but to any and all activity of the PSF as a whole**. Further, violation of this term gave the NSF the right to “claw back” previously approved and transferred funds. This would create a situation where money we’d already spent could be taken back, which would be an enormous, open-ended financial risk.
Diversity, equity, and inclusion are core to the PSF’s values, as committed to in our mission statement:
> _The mission of the Python Software Foundation is to promote, protect, and advance the Python programming language, and to support and facilitate the growth of**a diverse and international community** of Python programmers._
Given the value of the grant to the community and the PSF, we did our utmost to get clarity on the terms and to find a way to move forward in concert with our values. We consulted our NSF contacts and reviewed decisions made by other organizations in similar circumstances, particularly The Carpentries.
In the end, however, the PSF simply can’t agree to a statement that we won’t operate any programs that “advance or promote” diversity, equity, and inclusion, as it would be a betrayal of our mission and our community.
We’re disappointed to have been put in the position where we had to make this decision, because we believe our proposed project would offer invaluable advances to the Python and greater open source community, protecting millions of PyPI users from attempted supply-chain attacks. The proposed project would create new tools for automated proactive review of all packages uploaded to PyPI, rather than the current process of reactive-only review. These novel tools would rely on capability analysis, designed based on a dataset of known malware. Beyond just protecting PyPI users, the outputs of this work could be transferable for all open source software package registries, such as NPM and Crates.io, improving security across multiple open source ecosystems.
In addition to the security benefits, the grant funds would have made a big difference to the PSF’s budget. The PSF is a relatively small organization, operating with an annual budget of around $5 million per year, with a staff of just 14. $1.5 million over two years would have been quite a lot of money for us, and easily the largest grant we’d ever received. Ultimately, however, the value of the work and the size of the grant were not more important than practicing our values and retaining the freedom to support every part of our community. The PSF Board voted unanimously to withdraw our application.
Giving up the NSF grant opportunity—along with inflation, lower sponsorship, economic pressure in the tech sector, and global/local uncertainty and conflict—means the PSF needs financial support now more than ever. We are incredibly grateful for any help you can offer. If you're already a PSF member or regular donor, you have our deep appreciation, and we urge you to share your story about why you support the PSF. Your stories make all the difference in spreading awareness about the mission and work of the PSF.
How to support the PSF:
* Become a Member: When you sign up as a Supporting Member of the PSF, you become a part of the PSF. You’re eligible to vote in PSF elections, using your voice to guide our future direction, and you help us sustain what we do with your annual support.
* Donate: Your donation makes it possible to continue our work supporting Python and its community, year after year.
* Sponsor: If your company uses Python and isn’t yet a sponsor, send them our sponsorship page or reach out to sponsors@python.org today. The PSF is ever grateful for our sponsors, past and current, and we do everything we can to make their sponsorships beneficial and rewarding.
![Since I’ve been doing Machine Learning projects recently (Kaggle-style competitions), I’ve developed a few extra things I do 1 on those projects.
Document the features you’re currently using and potential features you’d like to explore.
Keep track of open questions and EDA tasks you’d like to explore.
Maintain a DATA_DICTIONARY.md or similar with the terms you’re using. Context is king.
Rely on a functional pipeline style and modular feature engineering. Use “micro pipeline” scripts to generate features. If your dataset has granularity at the “user” level, create different scripts for different kinds of features that generate the feature_name.csv files. You can join them all later. This is one of the things I use a lot. Almost all the features I’ve added to my projects start like “Write a script that computes the user’s […]. Save as feature_name.csv with user, feature_name as headers.”
Track experiments. I’m bad at this one as I haven’t figured out a simple enough way to do this reliably. Ideally, keep track of input features, model parameters, preprocessing, postprocessing, local evaluation, and remote evaluation.
Have a temporary folder (listed in .gitignore) for the LLM to write small scripts and experiments. Here is where you’d have things like inspect_csv.py or other useful scripts that can give the LLMs context on the actual data (e.g., check columns, stats, …)](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:4z5i7njrld66ew36htufcwry/bafkreiep6cwutgspbvgunxvhcn5uiea3ng4eqbjit7f4qlqgiiyeltsroy)
