More than 25,000 Python developers from nearly 200 countries took the 7th annual Python Developers Survey between November 2023 and February 2024, with 85% saying Python was their main language.

Some interesting findings:

• Though Python 2 reached "end-of-life" status in April of 2020, last year's survey found 7% of respondents were still using Python 2. This year's survey found that number has finally dropped... to 6%.
  
  "Almost half of Python 2 holdouts are under 21 years old," the survey results point out, "and a third are students. Perhaps courses are still using Python 2?"

• Meanwhile, 73% are using one of the last three versions of Python (3.10, 3.11, or 3.12)

• "The share of developers using Linux as their development environment has decreased through the years: compared with 2021, it's dropped by 8 percentage points." [The graphic is a little confusing, showing 55% using Linux, 55% using Windows, 29% on MacOS, 2% on BSD, and 1% on "Other."]

• Visual Studio Code is the most popular IDE (22%), followed by Jupyter Notebook (20%) and Vim (17%). The next-most popular IDEs were PyCharm Community Edition (13%), JupyterLab (12%), NotePad++ (11%) and Sublime Text (9%). Interestingly, just 23% of the 25,000 respondents said they only used one IDE, with 38% saying they used two, 21% using three, and 19% using four or more. [The annual survey is a collaboration between the Python Software Foundation and JetBrains.]

• 37% said they'd contributed to open-source projects within the last year. (77% of those contributed code, while 38% contributed documentation, 35% contributed governance/leadership/maintainer duties, and 33% contributed tests...)

• For "age range," nearly one-third (32%) said 21-29 (with another 8% choosing 18-20). Another 33% said 30-39, while 16% said 40-49, 7% said 50-59, and 3% chose "60 or older."
  
  49% of respondents said they had less than two years of programming experience, with 33% saying "less than 1 year" and 16% saying "1-2 years." (34% of developers also said they practiced collaborative development.)

And here's how the 25,000 developers answered the question: how long have you been programming in Python?

• Less than 1 year: 25%
• 1-2 years: 16%
• 3-5 years: 26%
• 6-10 years: 19%
• 11+ years: 13%

So what are they doing with Python? Among those who'd said Python was their main language:

• Data analysis: 44%
• Web development: 44%
• Machine learning: 34%
• Data engineering: 28%
• Academic research: 26%
• DevOps / Systems administration / Writing automation scripts 26%
• Programming of web parsers / scrapers / crawlers: 25%

62% were "fully employed by a company," while the next-largest category was "student" (12%) with another 5% in "working student". There were also categories for "self-employed" (6%), "freelancer" (another 6%), and "partially employed by a company" (4%). Another 4% said they were unemployed.

In other news, the Python Software Foundation board has also "decided to invest more in connecting and serving the global Python community" by hosting monthly "office hours" on their Discord channel.

Phoronix's Michael Larabel benchmarked AMD's latest Ryzen 9 9950X in several different Linux distros and found that the Zen 5 chip performs up to 16% faster with the Intel-optimized Clear Linux distro. Here's an excerpt from the report: The Linux distributions for this round of testing on the AMD Ryzen 9 9950X included Arch Linux, CachyOS, Clear Linux, Fedora Workstation 40, Ubuntu 24.04 LTS, and a recent daily snapshot of Ubuntu 24.10 in its current development form. Intel's Clear Linux is the one most interesting for looking at on the new AMD Zen 5 hardware. While there hasn't been so much Clear Linux news in recent times, it remains the most well optimized x86_64 Linux distribution out of the box. Clear Linux makes use of compiler function multi versioning, performance-minded defaults, aggressive compiler CFLAGS/CXXFLAGS defaults, optional AVX-512 usage for more libraries, and many other patches and optimizations in the name of delivering the greatest x86_64 Linux performance. And while not Intel's focus, it works typically on AMD hardware too. [...]

Using the same Ryzen 9 9950X system, all of these Linux distributions were tested in their default / out-of-the-box state. [...] When taking the geometric mean of 59 benchmarks run across all of the Linux distributions on this AMD Ryzen 9 9950X system, Intel's Clear Linux easily took the crown. Ubuntu 24.04 LTS -- which was used for all of the Ryzen 9000 series Linux testing so far on Phoronix -- was the slowest. Tapping Intel's Clear Linux netted a 16% improvement on top of the performance offered by Ubuntu 24.04 LTS! Ubuntu 24.04 with the Ryzen 9000 series was already looking great generationally, but as shown today the performance can be even better with further software optimizations.

The Arch Linux powered CachyOS that is tuned out-of-the-box with a similar aim to Clear Linux also performed great. CachyOS was 7% faster than Ubuntu 24.04 LTS based on the geo mean and 3% faster than upstream Arch Linux itself. For different workloads though the CachyOS advantage over Arch Linux varied from a minimal difference to quite significant advantages. From the performance of PHP and Python scripts atop Clear Linux to compiling various server and HPC minded software, Intel's Clear Linux -- and a commendable second place for CachyOS -- were showing that even greater performance can be achieved on the AMD Ryzen 9 9950X. Even for devoted Ubuntu Linux users, these results did show some nice advantages of the upcoming Ubuntu 24.10 release over Ubuntu 24.04 LTS thanks to the GCC 14 compiler. Ubuntu 24.10 performance is also still subject to change since the current daily ISOs haven't yet moved past the Linux 6.8 kernel while Ubuntu 24.10 in October will be shipping with Linux 6.11.

The Washington Post reviews a new book about Microsoft's 68-year-old co-founder Bill Gates: "He's not the Messiah, he's a very naughty boy." That immortal line from Monty Python's Life of Brian kept running through my head as I was reading "Billionaire, Nerd, Savior, King: Bill Gates and His Quest to Shape Our World," by Anupreeta Das, a reporter at the New York Times... which often feels like an extended list of all the major and minor complaints that Das could find not only about Gates but also about billionaires, nerds and the broader practice of philanthropy...

[T]he philanthropist who played a central role in the spectacularly successful fight against diseases like HIV/AIDS; the environmentalist whose net-zero vision has led him to create a multibillion-dollar nuclear-power company — that man barely makes an appearance in this book... Rather than weigh Gates's accomplishments against his failures, Das focuses on his personal weaknesses — his unpleasant management style, his extramarital affairs and, especially, his association with the convicted sex offender Jeffrey Epstein, who is featured extensively throughout, including in the beginning of the book's introduction and in a 12-page section that leads off the chapter titled "Cancel Bill." Frustratingly, Das sheds little new light on the Gates-Epstein relationship, beyond suggesting that Epstein first attracted the billionaire by indicating that he might be able to get Gates his coveted Nobel Peace Prize. While I and others have reported that a $2 million donation from Gates to the MIT Media Lab was thought of within MIT as being Epstein money, for instance, Das will go only so far as to say that "the donation may or may not have been at Epstein's recommendation."
The Guardian also notes that the Gates Foundation and the Gateses "have prevented millions of deaths, pumping billions of dollars into fighting Aids, tuberculosis and malaria around the world." They co-founded Gavi, the Vaccine Alliance, which vaccinated half the world's children... [During the pandemic] the Gates-backed Covax partnership was spearheading the global vaccination effort, procuring more than 1bn doses for people in poorer countries. But this doesn't seem to wash with Das, who reports that the foundation is "bigfooting", "neocolonial", "antidemocratic", and "top down", and sees it as an egotistical way for Bill to charity-wash his reputation... The penultimate chapter is titled Cancel Bill, and that's what the whole book feels like: an appeal to public opinion to write Gates off. As yet, and in the context of what other American billionaires do and get away with, it seems a little unfair.

America's National Institute of Standards and Technology (NIST) has released a new open-source software tool called Dioptra for testing the resilience of machine learning models to various types of attacks.

"Key features that are new from the alpha release include a new web-based front end, user authentication, and provenance tracking of all the elements of an experiment, which enables reproducibility and verification of results," a NIST spokesperson told SC Media: Previous NIST research identified three main categories of attacks against machine learning algorithms: evasion, poisoning and oracle. Evasion attacks aim to trigger an inaccurate model response by manipulating the data input (for example, by adding noise), poisoning attacks aim to impede the model's accuracy by altering its training data, leading to incorrect associations, and oracle attacks aim to "reverse engineer" the model to gain information about its training dataset or parameters, according to NIST.

The free platform enables users to determine to what degree attacks in the three categories mentioned will affect model performance and can also be used to gauge the use of various defenses such as data sanitization or more robust training methods.

The open-source testbed has a modular design to support experimentation with different combinations of factors such as different models, training datasets, attack tactics and defenses. The newly released 1.0.0 version of Dioptra comes with a number of features to maximize its accessibility to first-party model developers, second-party model users or purchasers, third-party model testers or auditors, and researchers in the ML field alike. Along with its modular architecture design and user-friendly web interface, Dioptra 1.0.0 is also extensible and interoperable with Python plugins that add functionality... Dioptra tracks experiment histories, including inputs and resource snapshots that support traceable and reproducible testing, which can unveil insights that lead to more effective model development and defenses.
NIST also published final versions of three "guidance" documents, according to the article. "The first tackles 12 unique risks of generative AI along with more than 200 recommended actions to help manage these risks. The second outlines Secure Software Development Practices for Generative AI and Dual-Use Foundation Models, and the third provides a plan for global cooperation in the development of AI standards."

Thanks to Slashdot reader spatwei for sharing the news.

Stack Overflow says over 65,000 developers took their annual survey — and "For the first time this year, we asked if developers felt AI was a threat to their job..."

Some analysis from The New Stack: Unsurprisingly, only 12% of surveyed developers believe AI is a threat to their current job. In fact, 70% are favorably inclined to use AI tools as part of their development workflow... Among those who use AI tools in their development workflow, 81% said productivity is one of its top benefits, followed by an ability to learn new skills quickly (62%). Much fewer (30%) said improved accuracy is a benefit. Professional developers' adoption of AI tools in the development process has risen rapidly, going from 44% in 2023 to 62% in 2024...

Seventy-one percent of developers with less than five years of experience reported using AI tools in their development process, as compared to just 49% of developers with 20 years of experience coding... At 82%, [ChatGPT] is twice as likely to have been used than GitHub Copilot. Among ChatGPT users, 74% want to continue using it.
But "only 43% said they trust the accuracy of AI tools," according to Stack Overflow's blog post, "and 45% believe AI tools struggle to handle complex tasks."

More analysis from The New Stack: The latest edition of the global annual survey found full-time employment is holding steady, with over 80% reporting that they have full-time jobs. The percentage of unemployed developers has more than doubled since 2019 but is still at a modest 4.4% worldwide... The median annual salary of survey respondents declined significantly. For example, the average full-stack developer's median 2024 salary fell 11% compared to the previous year, to $63,333... Wage pressure may be the result of more competition from an increase in freelancing.

Eighteen percent of professional developers in the 2024 survey said they are independent contractors or self-employed, which is up from 9.5% in 2020. Part-time employment has also risen, presenting even more pressure on full-time salaries... Job losses at tech companies have contributed to a large influx of talent into the freelance market, noted Stack Overflow CEO Prashanth Chandrasekar in an interview with The New Stack. Since COVID-19, he added, the emphasis on remote work means more people value job flexibility. In the 2024 survey, only 20% have returned to full-time in-person work, 38% are full-time remote, while the remainder are in a hybrid situation. Anticipation of future productivity growth due to AI may also be creating uncertainty about how much to pay developers.
Two stats jumped out for Visual Studio magazine: In this year's big Stack Overflow developer survey things are much the same for Microsoft-centric data points: VS Code and Visual Studio still rule the IDE roost, while .NET maintains its No. 1 position among non-web frameworks. It's been this way for years, though in 2021 it was .NET Framework at No. 1 among IDEs, while the new .NET Core/.NET 5 entry was No. 3. Among IDEs, there has been less change. "Visual Studio Code is used by more than twice as many developers than its nearest (and related) alternative, Visual Studio," said the 2024 Stack Overflow Developer survey, the 14th in the series of massive reports.
Stack Overflow shared some other interesting statistics:

• "Javascript (62%), HTML/CSS (53%), and Python (51%) top the list of most used languages for the second year in a row... [JavaScript] has been the most popular language every year since the inception of the Developer Survey in 2011."

• "Python is the most desired language this year (users that did not indicate using this year but did indicate wanting to use next year), overtaking JavaScript."

• "The language that most developers used and want to use again is Rust for the second year in a row with an 83% admiration rate. "

• "Python is most popular for those learning to code..."

• "Technical debt is a problem for 62% of developers, twice as much as the second- and third-most frustrating problems for developers: complex tech stacks for building and deployment."

Thursday Go's long-time tech lead Russ Cox made an announcement: Starting September 1, Austin Clements will be taking over as the tech lead of Go: both the Go team at Google and the overall Go project. Austin is currently the tech lead for what we sometimes call the "Go core", which encompasses compiler toolchain, runtime, and releases. Cherry Mui will be stepping up to lead those areas.

I am not leaving the Go project, but I think the time is right for a change... I will be shifting my focus to work more on Gaby [or "Go AI bot," an open-source contributor agent] and Oscar [an open-source contributor agent architecture], trying to make useful contributions in the Go issue tracker to help all of you work more productively. I am hopeful that work on Oscar will uncover ways to help open source maintainers that will be adopted by other projects, just like some of Go's best ideas have been adopted by other projects. At the highest level, my goals for Oscar are to build something useful, learn something new, and chart a path for other projects. These are the same broad goals I've always had for our work on Go, so in that sense Oscar feels like a natural continuation.
The post notes that new tech lead Austin Clements "has been working on Go at Google since 2014" (and Mui since 2016). "Their judgment is superb and their knowledge of Go and the systems it runs on both broad and deep. When I have general design questions or need to better understand details of the compiler, linker, or runtime, I turn to them." It's important to remember that tech lead — like any position of leadership — is a service role, not an honorary title. I have been leading the Go project for over 12 years, serving all of you, and trying to create the right conditions for all of you to do your best work. Large projects like Go absolutely benefit from stable leadership, but they can also benefit from leadership changes. New leaders bring new strengths and fresh perspectives. For Go, I think 12+ years of one leader is enough stability; it's time for someone new to serve in this role.

In particular, I don't believe that the "BDFL" (benevolent dictator for life) model is healthy for a person or a project. It doesn't create space for new leaders. It's a single point of failure. It doesn't give the project room to grow. I think Python benefited greatly from Guido stepping down in 2018 and letting other people lead, and I've had in the back of my mind for many years that we should have a Go leadership change eventually....

I am going to consciously step back from decision making and create space for Austin and the others to step forward, but I am not disappearing. I will still be available to talk about Go designs, review CLs, answer obscure history questions, and generally help and support you all in whatever way I can. I will still file issues and send CLs from time to time, I have been working on a few potential new standard libraries, I will still advocate for Go across the industry, and I will be speaking about Go at GoLab in Italy in November...

I am incredibly proud of the work we have all accomplished together, and I am confident in the leaders both on the Go team at Google and in the Go community. You are all doing remarkable work, and I know you will continue to do that.

The Python Software Foundation's board "was alerted to a defect in our bylaws that exposes the Foundation to an unbounded financial liability," according to a blog post Friday: Specifically, Bylaws Article XIII as originally written compels the Python Software Foundation to extend indemnity coverage to individual Members (including our thousands of "Basic Members") in certain cases, and to advance legal defense expenses to individual Members with surprisingly few restrictions. Further, the Bylaws compel the Foundation to take out insurance to cover these requirements, however, insurance of this nature is not actually available to 501(c)(3) nonprofit corporations such as the Python Software Foundation to purchase, and thus it is impossible in practice to comply with this requirement.

In the unlikely but not impossible event of the Foundation being called upon to advance such expenses, the potential financial burden would be virtually unlimited, and there would be no recourse to insurance. As this is an existential threat to the Foundation, the Board has agreed that it must immediately reduce the Foundation's exposure, and has opted to exercise its ability to amend the Bylaws by a majority vote of the Board directors, rather than by putting it to a vote of the membership, as allowed by Bylaws Article XI.

Acting on legal advice, the full Board has voted unanimously to amend its Bylaws to no longer extend an offer to indemnify, advance legal expenses, or insure Members when they are not serving at the request of the Foundation. The amended Bylaws still allow for indemnification of a much smaller set of individuals acting on behalf of the PSF such as Board Members and officers, which is in line with standard nonprofit governance practices and for which we already hold appropriate insurance.
Another blog post notes "the recent slew of conversations, initially kicked off in response to a bylaws change proposal, has been pretty alienating for many members of our community." - After the conversation on PSF-Vote had gotten pretty ugly, forty-five people out of ~1000 unsubscribed. (That list has since been put on announce-only)

- We received a lot of Code of Conduct reports or moderation requests about the PSF-vote mailing list and the discuss.python.org message board conversations. (Several reports have already been acted on or closed and the rest will be soon).

- PSF staff received private feedback that the blanket statements about "neurodiverse people", the bizarre motives ascribed to the people in charge of the PSF and various volunteers and the sideways comments about the kinds of people making reports were also very off-putting.

Long-time Slashdot theodp says this "provocative" blog post by former Google engineer Avery Pennarun — now the CEO/founder of Tailscale — is "a call to take back the Internet from its centralized rent-collecting cloud computing gatekeepers."

Pennarun writes: I read a post recently where someone bragged about using Kubernetes to scale all the way up to 500,000 page views per month. But that's 0.2 requests per second. I could serve that from my phone, on battery power, and it would spend most of its time asleep. In modern computing, we tolerate long builds, and then Docker builds, and uploading to container stores, and multi-minute deploy times before the program runs, and even longer times before the log output gets uploaded to somewhere you can see it, all because we've been tricked into this idea that everything has to scale. People get excited about deploying to the latest upstart container hosting service because it only takes tens of seconds to roll out, instead of minutes. But on my slow computer in the 1990s, I could run a perl or python program that started in milliseconds and served way more than 0.2 requests per second, and printed logs to stderr right away so I could edit-run-debug over and over again, multiple times per minute.

How did we get here?

We got here because sometimes, someone really does need to write a program that has to scale to thousands or millions of backends, so it needs all that stuff. And wishful thinking makes people imagine even the lowliest dashboard could be that popular one day. The truth is, most things don't scale, and never need to. We made Tailscale for those things, so you can spend your time scaling the things that really need it. The long tail of jobs that are 90% of what every developer spends their time on. Even developers at companies that make stuff that scales to billions of users, spend most of their time on stuff that doesn't, like dashboards and meme generators.

As an industry, we've spent all our time making the hard things possible, and none of our time making the easy things easy. Programmers are all stuck in the mud. Just listen to any professional developer, and ask what percentage of their time is spent actually solving the problem they set out to work on, and how much is spent on junky overhead.
Tailscale offers a "zero-config" mesh VPN — built on top of WireGuard — for a secure network that's software-defined (and infrastructure-agnostic). "The problem is developers keep scaling things they don't need to scale," Pennarun writes, "and their lives suck as a result...."

"The tech industry has evolved into an absolute mess..." Pennarun adds at one point. "Our tower of complexity is now so tall that we seriously consider slathering LLMs on top to write the incomprehensible code in the incomprehensible frameworks so we don't have to."

Their conclusion? "Modern software development is mostly junky overhead."

This week the Computer Science Teachers Association conference kicked off Tuesday in Las Vegas, writes long-time Slashdot reader theodp.

And the "TeachAI" education initiative teamed with the Computer Science Teachers Association to release three briefs "arguing that K-12 computer science education is more important than ever in an age of AI." From the press release: "As AI becomes increasingly present in the classroom, educators are understandably concerned about how it might disrupt the teaching of core CS skills like programming. With these briefs, TeachAI and CSTA hope to reinforce the idea that learning to program is the cornerstone of computational thinking and an important gateway to the problem-solving, critical thinking, and creative thinking skills necessary to thrive in today's digitally driven world. The rise of AI only makes CS education more important."

To help drive home the point to educators, the 39-page Guidance on the Future of Computer Science Education in an Age of AI (penned by five authors from nonprofits CSTA and Code.org) includes a pretty grim comic entitled Learn to Program or Follow Commands. In the panel, two high school students who scoff at the idea of having to learn to code and instead use GenAI to create their Python apps wind up getting stuck in miserable warehouse jobs several years later as a result where they're ordered about by an AI robot.
"The rise of AI only makes CS education more important," according to the group's press release, "with early research showing that people with a greater grasp of underlying computing concepts are able to use AI tools more effectively than those without." A survey by the group also found that 80% of teachers "agree that core concepts in CS education should be updated to emphasize topics that better support learning about AI."

But I'd be curious to hear what Slashdot's readers think. Share your thoughts and opinions in the comments.

Should children still be taught to code in the age of AI?

An anonymous reader shared this report from InfoWorld: Rust has leaped to its highest position ever in the monthly Tiobe index of language popularity, scaling to the 13th spot this month, with placement in the top 10 anticipated in an upcoming edition. Previously, Rust has never gone higher than 17th place in the Tiobe Programming Index. Tiobe CEO Paul Jansen attributed Rust's ascent in the just-released July index to a February 2024 U.S. White House report recommending Rust over C/C+ for safety reasons. He also credited the growing community and ecosystem support for the language. "Rust is finally moving up."
The article adds that these rankings are based on "the number of skilled engineers worldwide, courses, and third-party vendors pertaining to languages, examining websites such as Google, Amazon, Wikipedia, and more than 20 others to determine the monthly numbers."

1. Python
2. C++
3. C
4. Java
5. C#
6. JavaScript
7. Go
8. Visual Basic
9. Fortran
10. SQL

Interestingly, Rust has just moved into the top ten on the rival rankings from the rival Pypl Popularity of Programming Language index (which according to the article "assesses how often languages are searched on in Google.")

1. Python
2. Java
3. JavaScript
4. C#
5. C/C++
6. R
7. PHP
8. TypeScript
9. Swift
10. Rust

snydeq shares a report from CSO Online, written by Lucian Constantin: A personal GitHub access token with administrative privileges to the official repositories for the Python programming language and the Python Package Index (PyPI) was exposed for over a year. The access token belonged to the Python Software Foundation's director of infrastructure and was accidentally included in a compiled binary file that was published as part of a container image on Docker Hub. [...] The incident shows that scrubbing access tokens from source code only, which some development tools do automatically, is not enough to prevent potential security breaches. Sensitive credentials can also be included in environment variables, configuration files and even binary artifacts as a result of automated build processes and developer mistakes. "Although we encounter many secrets that are leaked in the same manner, this case was exceptional because it is difficult to overestimate the potential consequences if it had fallen into the wrong hands -- one could supposedly inject malicious code into PyPI packages (imagine replacing all Python packages with malicious ones), and even to the Python language itself," researchers from security firm JFrog, who found and reported the token, wrote in a report.

Workers at delivery company Shipt "found that their paychecks had become...unpredictable," according to an article in IEEE Spectrum. "They were doing the same work they'd always done, yet their paychecks were often less than they expected. And they didn't know why...."

The article notes that "Companies whose business models rely on gig workers have an interest in keeping their algorithms opaque." But "The workers showed that it's possible to fight back against the opaque authority of algorithms, creating transparency despite a corporation's wishes." On Facebook and Reddit, workers compared notes. Previously, they'd known what to expect from their pay because Shipt had a formula: It gave workers a base pay of $5 per delivery plus 7.5 percent of the total amount of the customer's order through the app. That formula allowed workers to look at order amounts and choose jobs that were worth their time. But Shipt had changed the payment rules without alerting workers. When the company finally issued a press release about the change, it revealed only that the new pay algorithm paid workers based on "effort," which included factors like the order amount, the estimated amount of time required for shopping, and the mileage driven. The company claimed this new approach was fairer to workers and that it better matched the pay to the labor required for an order. Many workers, however, just saw their paychecks dwindling. And since Shipt didn't release detailed information about the algorithm, it was essentially a black box that the workers couldn't see inside.

The workers could have quietly accepted their fate, or sought employment elsewhere. Instead, they banded together, gathering data and forming partnerships with researchers and organizations to help them make sense of their pay data. I'm a data scientist; I was drawn into the campaign in the summer of 2020, and I proceeded to build an SMS-based tool — the Shopper Transparency Calculator [written in Python, using optical character recognition and Twilio, and running on a home server] — to collect and analyze the data. With the help of that tool, the organized workers and their supporters essentially audited the algorithm and found that it had given 40 percent of workers substantial pay cuts...

This "information asymmetry" helps companies better control their workforces — they set the terms without divulging details, and workers' only choice is whether or not to accept those terms... There's no technical reason why these algorithms need to be black boxes; the real reason is to maintain the power structure... In a fairer world where workers have basic data rights and regulations require companies to disclose information about the AI systems they use in the workplace, this transparency would be available to workers by default.
The tool's creator was attracted to the idea of helping a community "control and leverage their own data," and ultimately received more than 5,600 screenshots from over 200 workers. 40% were earning at least 10% less — and about 33% were earning less than their state's minimum wage. Interestingly, "Sharing data about their work was technically against the company's terms of service; astoundingly, workers — including gig workers who are classified as 'independent contractors' — often don't have rights to their own data...

"[O]ur experiment served as an example for other gig workers who want to use data to organize, and it raised awareness about the downsides of algorithmic management. What's needed is wholesale changes to platforms' business models... The battles that gig workers are fighting are the leading front in the larger war for workplace rights, which will affect all of us. The time to define the terms of our relationship with algorithms is right now."

Thanks to long-time Slashdot reader mspohr for sharing the article.

"After sixteen years since the introduction of Python 3, the Fedora project announces that Python 2.7, the last of the Python 2 series, will be retired," according to long-time Slashdot reader slack_justyb.

From the announcement on the Fedora changes page: The python2.7 package will be retired without replacement from Fedora Linux 41. There will be no Python 2 in Fedora 41+ other than PyPy. Packages requiring python2.7 on runtime or buildtime will have to deal with the retirement or be retired as well.
"This also comes with the announcement that GIMP 3 will be coming to Fedora 41 to remove any last Python 2 dependencies," adds slack_justyb. GIMP 2 was originally released on March 23, 2004. GIMP will be updated to GIMP 3 with Python 3 support. Python 2 dependencies of GIMP will be retired.
Python 2's end of life was originally 2015, but was extended to 2020. The Python maintainers close with this: The Python maintainers will no longer regularly backport security fixes to Python 2.7 in RHEL, due to the the end of maintenance of RHEL 7 and the retirement of the Python 2.7 application stream in RHEL 8. We provided this obsolete package for 5 years beyond its retirement date and will continue to provide it until Fedora 40 goes end of life. Enough has been enough.

IEEE Spectrum (the IEEE's official publication) asks the question. "How does an AI code generator compare to a human programmer?" A study published in the June issue of IEEE Transactions on Software Engineering evaluated the code produced by OpenAI's ChatGPT in terms of functionality, complexity and security. The results show that ChatGPT has an extremely broad range of success when it comes to producing functional code — with a success rate ranging from anywhere as poor as 0.66 percent and as good as 89 percent — depending on the difficulty of the task, the programming language, and a number of other factors. While in some cases the AI generator could produce better code than humans, the analysis also reveals some security concerns with AI-generated code.
The study tested GPT-3.5 on 728 coding problems from the LeetCode testing platform — and in five programming languages: C, C++, Java, JavaScript, and Python. The results? Overall, ChatGPT was fairly good at solving problems in the different coding languages — but especially when attempting to solve coding problems that existed on LeetCode before 2021. For instance, it was able to produce functional code for easy, medium, and hard problems with success rates of about 89, 71, and 40 percent, respectively. "However, when it comes to the algorithm problems after 2021, ChatGPT's ability to generate functionally correct code is affected. It sometimes fails to understand the meaning of questions, even for easy level problems," said Yutian Tang, a lecturer at the University of Glasgow. For example, ChatGPT's ability to produce functional code for "easy" coding problems dropped from 89 percent to 52 percent after 2021. And its ability to generate functional code for "hard" problems dropped from 40 percent to 0.66 percent after this time as well...

The researchers also explored the ability of ChatGPT to fix its own coding errors after receiving feedback from LeetCode. They randomly selected 50 coding scenarios where ChatGPT initially generated incorrect coding, either because it didn't understand the content or problem at hand. While ChatGPT was good at fixing compiling errors, it generally was not good at correcting its own mistakes... The researchers also found that ChatGPT-generated code did have a fair amount of vulnerabilities, such as a missing null test, but many of these were easily fixable.
"Interestingly, ChatGPT is able to generate code with smaller runtime and memory overheads than at least 50 percent of human solutions to the same LeetCode problems..."

Long-time Slashdot reader theodp shared this warning from a long-time AI researcher arguing that data science "is due" for a reckoning over whether results can be reproduced. "Few technological revolutions came with such a low barrier of entry as Machine Learning..." Unlike Machine Learning, Data Science is not an academic discipline, with its own set of algorithms and methods... There is an immense diversity, but also disparities in skill, expertise, and knowledge among Data Scientists... In practice, depending on their backgrounds, data scientists may have large knowledge gaps in computer science, software engineering, theory of computation, and even statistics in the context of machine learning, despite those topics being fundamental to any ML project. But it's ok, because you can just call the API, and Python is easy to learn. Right...?

Building products using Machine Learning and data is still difficult. The tooling infrastructure is still very immature and the non-standard combination of data and software creates unforeseen challenges for engineering teams. But in my views, a lot of the failures come from this explosive cocktail of ritualistic Machine Learning:

- Weak software engineering knowledge and practices compounded by the tools themselves;
- Knowledge gap in mathematical, statistical, and computational methods, encouraged black boxing API;
- Ill-defined range of competence for the role of data scientist, reinforced by a pool of candidates with an unusually wide range of backgrounds;
- A tendency to follow the hype rather than the science.


- What can you do?

- Hold your data scientists accountable using Science.
- At a minimum, any AI/ML project should include an Exploratory Data Analysis, whose results directly support the design choices for feature engineering and model selection.
- Data scientists should be encouraged to think outside-of-the box of ML, which is a very small box - Data scientists should be trained to use eXplainable AI methods to provide context about the algorithm's performance beyond the traditional performance metrics like accuracy, FPR, or FNR.
- Data scientists should be held at similar standards than other software engineering specialties, with code review, code documentation, and architectural designs.
The article concludes, "Until such practices are established as the norm, I'll remain skeptical of Data Science."

Last month TIOBE announced its estimate that the four most popular programming languages were:

1. Python
2. C
3. C++
4. Java

But this month C++ "overtook" C for the first time, TIOBE announced, becoming (according to the same methodology) the #2 most popular programming language, with C dropping to #3. " C++ has never been that high in the TIOBE index," says TIOBE Software CEO Paul Jansen in the announcement, "whereas C has never been that low."

1. Python
2. C++
3. C
4. Java

C++ started a new life as of 2011 with its consistent 3 yearly updates. Although most compilers and most engineers can't take up with this pace, it is considered a success to see the language evolve.

The main strengths of C++ are its performance and scalability. Its downside is its many ways to get things done, i.e. its rich idiom of features, which is caused by its long history and aim for backward compatibility.

C++ is heavily used in embedded systems, game development and financial trading software, just to name a few domains.
There's different rankings from the rival PYPL index of programming language popularity. It lumps C and C++ together to award them a collective ranking (#5). But unlike TIOBE, it shows Java [and JavaScript and C#] all being more popular (with Python still the #1 most popular language).

Of course, statistical anomalies could be also skewing the results. Visual Basic also lost two ranks in popularity in the last month, according to TIOBE, dropping from the #7 position to the #9 position (now falling just behind Go and SQL). This becomes the first time that Go has risen as high as #7, according to TIOBE's announcement — with Rust also reaching an all-time high of #17...

Friday the Python Software Foundation published several blog posts about this year's "Python Language Summit" May 15th (before PyCon US), which featured talks and discussions by core developers, triagers, and Python implementation maintainers.

There were several lightning talks. One talk came from the maintainer of the PyO3 project, offering Rust bindings for the Python C API (which requires mapping Rust concepts to Python — leaving a question as to how to map Rust's error-handling panic! macro). There was a talk on formalizing the PEP prototype process, and a talk on whether the Python team should have a more official presence in the Apple App Store (and maybe the Google Play Store). One talk suggested changing the formatting of error messages for assert statements, and one covered a "highly experimental" project to support structured data sharing between Python subinterpreters. One talk covered Python's "unsupported build" warning and how it should behave on platforms beyond Python's officially supported list.

Python Foundation blog posts also covered some of the longer talks, including one on the idea of using type annotations as a mechanism for transformers. One talk covered the new interactive REPL interpreter coming to Python 3.13.

And one talk focused on Python's security model after the xz-utils backdoor: Pablo Galindo Salgado, Steering Council member and the release manager for Python 3.10 and 3.11, brought this topic to the Language Summit to discuss what could be done to improve Python's security model... Pablo noted the similarities shared between CPython and xz-utils, referencing the previous Language Summit's talk on core developer burnout, the number of modules in the standard library that have one or zero maintainers, the high ratio of maintainers to source code, and the use of autotools for configuration. Autotools was used by [xz's] Jia Tan as part of the backdoor, specifically to obscure the changes to tainted release artifacts. Pablo confirmed along with many nods of agreement that indeed, CPython could be vulnerable to a contributor or core developer getting secretly malicious changes merged into the project.

For multiple reasons like being able to fix bugs and single-maintainer modules, CPython doesn't require reviewers on the pull requests of core developers. This can lead to "unilateral action", meaning that a change is introduced into CPython without the review of someone besides the author. Other situations like release managers backporting fixes to other branches without review are common.
Much discussion ensued about the possibility of altering workflows (including pull request reviews), identity verification, and the importance of post-incident action plans. Guido van Rossum suggested a "higher bar" for granting write access, but in the end "Overall it was clear there is more discussion and work to be done in this rapidly changing area."

In another talk, Hugo van Kemenade, the newly announced Release Manager for Python 3.14 and 3.15, "started the Language Summit with a proposal to change Python's versioning scheme. The perception of Python using semantic versioning is a source of confusion for users who don't expect backwards incompatible changes when upgrading to new versions of Python. In reality almost all new feature releases of Python include backwards incompatible changes such as the removal of "dead batteries" where PEP 594 marked 19 modules for removal in Python 3.13. Calendar Versioning (CalVer) encompasses a wide array of different versioning schemes that have one property in common: using the release date as part of a release's version... Hugo offered multiple proposed versioning schemes, including:

- Using the release year as minor version (3.YY.micro, "3.26.0")
- Using the release year as major version (YY.0.micro, "26.0.0")
- Using the release year and month as major and minor version (YY.MM.micro, "26.10.0")

[...] Overall the proposal to use the current year as the minor version was well-received, Hugo mentioned that he'd be drafting up a PEP for this change.

"Rust is the fastest-growing programming language, with its developer community doubling in size over the past two years," writes The New Stack, "yet JavaScript remains the most popular language with 25.2 million active developers, according to the results of a recent survey." The 26th edition of SlashData's Developer Nation survey showed that the Rust community doubled its number of users over the past two years — from two million in the first quarter of 2022 to four million in the first quarter of 2024 — and by 33% in the last 12 months alone. The SlashData report covers the first quarter of 2024. "Rust has developed a passionate community that advocates for it as a memory-safe language which can provide great performance, but cybersecurity concerns may lead to an even greater increase," the report said. "The USA and its international partners have made the case in the last six months for adopting memory-safe languages...."

"JavaScript's dominant position is unlikely to change anytime soon, with its developer population increasing by 4M developers over the last 12 months, with a growth rate in line with the global developer population growth," the report said. The strength of the JavaScript community is fueled by the widespread use of the language across all types of development projects, with at least 25% of developers in every project type using it, the report said. "Even in development areas not commonly associated with the language, such as on-device coding for IoT projects, JavaScript still sees considerable adoption," SlashData said.

Also, coming in strong, Python has overtaken Java as the second most popular language, driven by the interest in machine learning and AI. The battle between Python and Java shows Python with 18.2 million developers in Q1 2024 compared to Java's 17.7 million. This comes about after Python added more than 2.1 million net new developers to its community over the last 12 months, compared to Java which only increased by 1.2 million developers... Following behind Java there is a six-million-developer gap to the next largest community, which is C++ with 11.4 million developers, closely trailed by C# with 10.2 million and PHP with 9.8 million. Languages with the smallest communities include Objective-C with 2.7 million developers, Ruby with 2.5 million, and Lua with 1.8 million. Meanwhile, the Go language saw its developer population grow by 10% over the last year. It had previously outpaced the global developer population growth, growing by 5Y% over the past two years, from three million in Q1 2022 to 4.7 million in Q1 2024.
"TNS analyst Lawrence Hecht has a few different takeaways. He notes that with the exceptions of Rust, Go and JavaScript, the other major programming languages all grew slower than the total developer population, which SlashData says increased 39% over the last two years alone."

An anonytmous reader shared a recent report from BleepingComputer: Cybercriminals are abusing Stack Overflow in an interesting approach to spreading malware — answering users' questions by promoting a malicious PyPi package that installs Windows information-stealing malware... "We further noticed that a StackOverflow account 'EstAYA G' [was] exploiting the platform's community members seeking debugging help [1, 2, 3] by directing them to install this malicious package as a 'solution' to their issue even though the 'solution' is unrelated to the questions posted by developers," explained Sonatype researcher Ax Sharma in the Sonatype report.
Sonatype's researcher "noticed that line 17 was laden with ...a bit too many whitespaces," according to the report, "in turn hiding code much further to the right which would be easy to miss, unless you notice the scroll bar. The command executes a base64-encoded payload..."

And then, reports BleepingComputer... When deobfuscated, this command will download an executable named 'runtime.exe' from a remote site and execute it. This executable is actually a Python program converted into an .exe that acts as an information-stealing malware to harvest cookies, passwords, browser history, credit cards, and other data from web browsers. It also appears to search through documents for specific phrases and, if found, steal the data as well.

All of this information is then sent back to the attacker, who can sell it on dark web markets or use it to breach further accounts owned by the victim.

Mistral, the French AI startup backed by Microsoft and valued at $6 billion, has released its first generative AI model for coding, dubbed Codestral. From a report: Codestral, like other code-generating models, is designed to help developers write and interact with code. It was trained on over 80 programming languages, including Python, Java, C++ and JavaScript, explains Mistral in a blog post. Codestral can complete coding functions, write tests and "fill in" partial code, as well as answer questions about a codebase in English. Mistral describes the model as "open," but that's up for debate. The startup's license prohibits the use of Codestral and its outputs for any commercial activities. There's a carve-out for "development," but even that has caveats: the license goes on to explicitly ban "any internal usage by employees in the context of the company's business activities." The reason could be that Codestral was trained partly on copyrighted content. Codestral might not be worth the trouble, in any case. At 22 billion parameters, the model requires a beefy PC in order to run.