The biggest thing PyPy adds is JIT compilation. This is precisely what the project to add JIT to CPython is working on these days. It's still early days for the project, but by 3.15 there's a good chance we'll see some really great speedups in some cases.
It's worth noting that PyPy devs are in the loop, and their insights so far have been invaluable.
That approach sometimes does work, but usually very poorly and often not at all.
It can work very well when the higher-up is well informed and does have deep technical experience and understanding. Steve Jobs and Elon Musk are great, well-known examples of this. They've also provided great examples of the same approach mostly failing when applied outside of their areas of deep expertise and understanding.
> You can make meaningful statistical claims about this with just two subjects
With just two subjects, there is a very high chance that the results of your testing are overly specific to those two subjects, and do not hold for most of the population.
With only 8 subjects, as in this study, that is still very true.
For example, people like the one person the mosquitos were extremely attracted to could be one in 10, one in 100 or one in a million. In the latter case, the findings are much, much less meaningful.
That's true even for the findings about specific genes affecting attraction. What if that one person is an extreme outlier and the mechanism causing the results isn't relevant for 99.9999% of the human population?
> Can you elaborate on which specific statistical claim you think they should not make?
For example, the claim that "Highly attractive people have higher levels of carboxylic acids on their skin" does not seem to be well enough backed by evidence in this case. If they wrote, "the one/three attractive people we tested had higher levels of carboxylic acids on their skin", I'd have no complaints.
I'm very skeptical of these results: The sample size is much too small!
They started with only 64 individuals. Of those only ONE was found to be especially attractive to mosquitoes, and only TWO especially unattractive. They then used the scents from these three individuals, and 5 others who did not participate in the initial testing, for the following phases of research.
Also, of the 5 additional people, two were found to be especially attractive, and one unattractive. This is extremely inconsistent with the results from the first phase.
Now, they did a ton of work and show very interesting results. But these problems make all of their results very questionable in my mind.
Yes, followup research could shine more light on this to validate their results (or not), but I wish they would make less grand declarations about the meaningfulness of their results. I bet this is going to make the news around the world... and if it later turns out to be inaccurate, it's only going to further reduce the public's trust in science.
For a specific doc fix, a PR is indeed the way to go. If it's not a simple fix, creating an issue on the tracker may also be called for. Doing so does help and is appreciated, even if it sometimes takes a long time to be addressed by a core dev.
Otherwise, one could help in ways like I mentioned previously: reading existing issues, checking if they are still relevant and commenting accordingly, reviewing PRs and patches, etc.
Python is almost entirely developed and maintained by volunteers. The increasing backlog of issues and PRs is recognized as a problem.
Thanks to a generous donation, the PSF has recently begun employing one "developer in residence", Łukasz Langa. He is specifically tasked with tackling this backlog, and has been doing a mighty good job so far.
Still, with over 1,000 open PRs and many times more open issues, we (the Python devs) could use more helping hands. For example, anyone can confirm that bugs reproduce, review PRs, or test fixes, and those are all meaningful help (when done thoughtfully and thoroughly.)
I, and most core devs, volunteer happily and ask for nothing in return. If you think the situation outlined in the parent post isn't great and begs improvement, you're welcome to help!
At least for the Python project, there are more than enough inherent "roadblocks" for new contributors to overcome. Work is being actively done to remove some of these, or at least make overcoming them easier.
Python is well into the process of transitioning from bugs.python.org to GitHub issues. See PEP 581[1] for the rationale. Ezio Melotti is leading the project, and one may follow the progress on the psf/gh-migration GitHub repo[2] where the work is being managed (including specifically the Projects tab).
Hopefully, that should help make things smoother and more accessible for potential contributors.
Most of these libraries focus on calculating distance metrics between pairs of strings, or finding the nearest match to a string among many candidates.
On the other hand, fuzzysearch is used for finding near matches to a string within much longer strings or texts. Common use cases are fuzzy searches in DNA sequences and long texts such as books and articles.
I'm looking for great high-school Math teachers for a project. If you wouldn't mind sparing a few moments, please get in touch with me via email (gmail with my username) so I can give you some more details.
It's worth noting that PyPy devs are in the loop, and their insights so far have been invaluable.