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.

"While general-purpose languages like Python, C++, and Java remain popular in AI development," writes VentureBeat, "the resurgence of AI-first languages signifies a recognition that AI's unique demands require specialized languages tailored to the domain's specific needs... designed from the ground up to address the specific needs of AI development." Bend, created by Higher Order Company, aims to provide a flexible and intuitive programming model for AI, with features like automatic differentiation and seamless integration with popular AI frameworks. Mojo, developed by Modular AI, focuses on high performance, scalability, and ease of use for building and deploying AI applications. Swift for TensorFlow, an extension of the Swift programming language, combines the high-level syntax and ease of use of Swift with the power of TensorFlow's machine learning capabilities...

At the heart of Mojo's design is its focus on seamless integration with AI hardware, such as GPUs running CUDA and other accelerators. Mojo enables developers to harness the full potential of specialized AI hardware without getting bogged down in low-level details. One of Mojo's key advantages is its interoperability with the existing Python ecosystem. Unlike languages like Rust, Zig or Nim, which can have steep learning curves, Mojo allows developers to write code that seamlessly integrates with Python libraries and frameworks. Developers can continue to use their favorite Python tools and packages while benefiting from Mojo's performance enhancements... It supports static typing, which can help catch errors early in development and enable more efficient compilation... Mojo also incorporates an ownership system and borrow checker similar to Rust, ensuring memory safety and preventing common programming errors. Additionally, Mojo offers memory management with pointers, giving developers fine-grained control over memory allocation and deallocation...

Mojo is conceptually lower-level than some other emerging AI languages like Bend, which compiles modern high-level language features to native multithreading on Apple Silicon or NVIDIA GPUs. Mojo offers fine-grained control over parallelism, making it particularly well-suited for hand-coding modern neural network accelerations. By providing developers with direct control over the mapping of computations onto the hardware, Mojo enables the creation of highly optimized AI implementations.

According to Mojo's creator, Modular, the language has already garnered an impressive user base of over 175,000 developers and 50,000 organizations since it was made generally available last August. Despite its impressive performance and potential, Mojo's adoption might have stalled initially due to its proprietary status. However, Modular recently decided to open-source Mojo's core components under a customized version of the Apache 2 license. This move will likely accelerate Mojo's adoption and foster a more vibrant ecosystem of collaboration and innovation, similar to how open source has been a key factor in the success of languages like Python.

Developers can now explore Mojo's inner workings, contribute to its development, and learn from its implementation. This collaborative approach will likely lead to faster bug fixes, performance improvements and the addition of new features, ultimately making Mojo more versatile and powerful.
The article also notes other languages "trying to become the go-to choice for AI development" by providing high-performance execution on parallel hardware. Unlike low-level beasts like CUDA and Metal, Bend feels more like Python and Haskell, offering fast object allocations, higher-order functions with full closure support, unrestricted recursion and even continuations. It runs on massively parallel hardware like GPUs, delivering near-linear speedup based on core count with zero explicit parallel annotations — no thread spawning, no locks, mutexes or atomics. Powered by the HVM2 runtime, Bend exploits parallelism wherever it can, making it the Swiss Army knife for AI — a tool for every occasion...

The resurgence of AI-focused programming languages like Mojo, Bend, Swift for TensorFlow, JAX and others marks the beginning of a new era in AI development. As the demand for more efficient, expressive, and hardware-optimized tools grows, we expect to see a proliferation of languages and frameworks that cater specifically to the unique needs of AI. These languages will leverage modern programming paradigms, strong type systems, and deep integration with specialized hardware to enable developers to build more sophisticated AI applications with unprecedented performance. The rise of AI-focused languages will likely spur a new wave of innovation in the interplay between AI, language design and hardware development. As language designers work closely with AI researchers and hardware vendors to optimize performance and expressiveness, we will likely see the emergence of novel architectures and accelerators designed with these languages and AI workloads in mind. This close relationship between AI, language, and hardware will be crucial in unlocking the full potential of artificial intelligence, enabling breakthroughs in fields like autonomous systems, natural language processing, computer vision, and more.

The future of AI development and computing itself are being reshaped by the languages and tools we create today.
In 2017 Modular AI's founder Chris Lattner (creator of the Swift and LLVM) answered questions from Slashdot readers.

"The TIOBE Index sets out to reflect the relative popularity of computer languages," writes i-Programmer, "so it comes as something of a surprise to see two languages dating from the 1950's in this month's Top 20. Having broken into the the Top 20 in April 2021 Fortran has continued to rise and has now risen to it's highest ever position at #10... The headline for this month's report by Paul Jansen on the TIOBE index is:

Fortran in the top 10, what is going on?

Jansen's explanation points to the fact that there are more than 1,000 hits on Amazon for "Fortran Programming" while languages such as Kotlin and Rust, barely hit 300 books for the same search query. He also explains that Fortran is still evolving with the new ISO Fortran 2023 definition published less than half a year ago....

The other legacy language that is on the rise in the TIOBE index is COBOL. We noticed it re-enter the Top 20 in January 2024 and, having dropped out in the interim, it is there again this month.
More details from TechRepublic: Along with Fortran holding on to its spot in the rankings, there were a few small changes in the top 10. Go gained 0.61 percentage points year over year, rising from tenth place in May 2023 to eighth this year. C++ rose slightly in popularity year over year, from fourth place to third, while Java (-3.53%) and Visual Basic (-1.8) fell.
Here's how TIOBE ranked the 10 most popular programming languages in May:

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

On the rival PYPL ranking of programming language popularity, Fortran does not appear anywhere in the top 29.

A note on its page explains that "Worldwide, Python is the most popular language, Rust grew the most in the last 5 years (2.1%) and Java lost the most (-4.0%)." Here's how it ranks the 10 most popular programming languages for May:

1. Python (28.98% share)
2. Java (15.97% share)
3. JavaScript (8.79%)
4. C# (6.78% share)
5. R (4.76% share)
6. PHP (4.55% share)
7. TypeScript (3.03% share)
8. Swift (2.76% share)
9. Rust (2.6% share)

According to CNBC, Google is laying off at least 200 employees from its "Core" teams and moving some roles to India and Mexico. From the report: The Core unit is responsible for building the technical foundation behind the company's flagship products and for protecting users' online safety, according to Google's website. Core teams include key technical units from information technology, its Python developer team, technical infrastructure, security foundation, app platforms, core developers, and various engineering roles. At least 50 of the positions eliminated were in engineering at the company's offices in Sunnyvale, California, filings show. Many Core teams will hire corresponding roles in Mexico and India, according to internal documents viewed by CNBC.

Asim Husain, vice president of Google Developer Ecosystem, announced news of the layoffs to his team in an email last week. He also spoke at a town hall and told employees that this was the biggest planned reduction for his team this year, an internal document shows. "We intend to maintain our current global footprint while also expanding in high-growth global workforce locations so that we can operate closer to our partners and developer communities," Husain wrote in the email. [...] "Announcements of this sort may leave many of you feeling uncertain or frustrated," Husain wrote in the email to developers. He added that his message to developers is that the changes "are in service of our broader goals" as a company. The teams involved in the reorganization have been key to the company's developer tools, an area Google is streamlining as it incorporates more artificial intelligence into the products.

Ahead of its annual I/O developer conference in May, Google has decided to lay off staff across key teams like Flutter, Dart, Python and others. "As we've said, we're responsibly investing in our company's biggest priorities and the significant opportunities ahead," said a Google spokesperson. "To best position us for these opportunities, throughout the second half of 2023 and into 2024, a number of our teams made changes to become more efficient and work better, remove layers, and align their resources to their biggest product priorities. Through this, we're simplifying our structures to give employees more opportunity to work on our most innovative and important advances and our biggest company priorities, while reducing bureaucracy and layers." TechCrunch reports: The company clarified that the layoffs were not company-wide but were reorgs that are part of the normal course of business. Affected employees will be able to apply for other open roles at Google, we're told. [...] Though Google didn't detail headcount, some of the layoffs at Google may have been confirmed in a WARN notice filed on April 24. WARN, or the California Worker Adjustment and Retraining Notification Act, requires employers with more than 100 employees to provide 60-day notice in advance of layoffs. In the filing, Google said it was laying off a total of 50 employees across three locations in Sunnyvale.

On social media, commenters raised concerns with the Python layoffs in particular, given the role that Python tooling plays in AI. But others pointed out that Google didn't eliminate its Python team; it replaced that team with another group based in Munich -- at least according to Python Steering Council member Thomas Wouters in a post on Mastodon last Thursday.

An anonymous reader quotes a report from BleepingComputer: A new campaign tracked as "Dev Popper" is targeting software developers with fake job interviews in an attempt to trick them into installing a Python remote access trojan (RAT). The developers are asked to perform tasks supposedly related to the interview, like downloading and running code from GitHub, in an effort to make the entire process appear legitimate. However, the threat actor's goal is make their targets download malicious software that gathers system information and enables remote access to the host. According to Securonix analysts, the campaign is likely orchestrated by North Korean threat actors based on the observed tactics. The connections are not strong enough for attribution, though. [...]

Although the perpetrators of the Dev Popper attack aren't known, the tactic of using job lures as bait to infect people with malware is still prevalent, so people should remain vigilant of the risks. The researchers note that the method "exploits the developer's professional engagement and trust in the job application process, where refusal to perform the interviewer's actions could compromise the job opportunity," which makes it very effective.

Long-time Slashdot reader tippen shared this report from the Register: AI agents, which combine large language models with automation software, can successfully exploit real world security vulnerabilities by reading security advisories, academics have claimed.

In a newly released paper, four University of Illinois Urbana-Champaign (UIUC) computer scientists — Richard Fang, Rohan Bindu, Akul Gupta, and Daniel Kang — report that OpenAI's GPT-4 large language model (LLM) can autonomously exploit vulnerabilities in real-world systems if given a CVE advisory describing the flaw. "To show this, we collected a dataset of 15 one-day vulnerabilities that include ones categorized as critical severity in the CVE description," the US-based authors explain in their paper. "When given the CVE description, GPT-4 is capable of exploiting 87 percent of these vulnerabilities compared to 0 percent for every other model we test (GPT-3.5, open-source LLMs) and open-source vulnerability scanners (ZAP and Metasploit)...."

The researchers' work builds upon prior findings that LLMs can be used to automate attacks on websites in a sandboxed environment. GPT-4, said Daniel Kang, assistant professor at UIUC, in an email to The Register, "can actually autonomously carry out the steps to perform certain exploits that open-source vulnerability scanners cannot find (at the time of writing)."
The researchers wrote that "Our vulnerabilities span website vulnerabilities, container vulnerabilities, and vulnerable Python packages. Over half are categorized as 'high' or 'critical' severity by the CVE description...."

"Kang and his colleagues computed the cost to conduct a successful LLM agent attack and came up with a figure of $8.80 per exploit"

The PHP programming language has sunk to its lowest position ever on the long-running TIOBE index of programming language popularity. It now ranks #17 — lower than Assembly Language, Ruby, Swift, Scratch, and MATLAB. InfoWorld reports: When the Tiobe index started in 2001, PHP was about to become the standard language for building websites, said Paul Jansen, CEO of software quality services vendor Tiobe. PHP even reached the top 3 spot in the index, ranking third several times between 2006 and 2010. But as competing web development frameworks such as Ruby on Rails, Django, and React arrived in other languages, PHP's popularity waned.

"The major driving languages behind these new frameworks were Ruby, Python, and most notably JavaScript," Jansen noted in his statement accompanying the index. "On top of this competition, some security issues were found in PHP. As a result, PHP had to reinvent itself." Nowadays, PHP still has a strong presence in small and medium websites and is the language leveraged in the WordPress web content management system. "PHP is certainly not gone, but its glory days seem to be over," Jansen said.
A note on the rival Pypl Popularity of Programming Language Index argues that the TIOBE Index "is a lagging indicator. It counts the number of web pages with the language name." So while "Objective-C" ranks #30 on TIOBE's index (one rank above Classic Visual Basic), "who is reading those Objective-C web pages? Hardly anyone, according to Google Trends data." On TIOBE's index, Fortran now ranks #10.

Meanwhile, PHP ranks #7 on Pypl (based on the frequency of searches for language tutorials).

TIOBE's top ten?

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

The next two languages, ranked #11 and #12, are Delphi/Object Pascal and Assembly Language.

theodp writes: From a Wednesday press release: "Code.org, in collaboration with The Piech Lab at Stanford University, launched today its AI Teaching Assistant, ushering in a new era of computer science instruction to support teachers in preparing students with the foundational skills necessary to work, live and thrive in an AI world. [...] Launching as a part of Code.org's leading Computer Science Discoveries (CSD) curriculum [for grades 6-10], the tool is designed to bolster teacher confidence in teaching computer science." EdWeek reports that in a limited pilot project involving twenty teachers nationwide, the AI computer science grading tool cut one middle school teacher's grading time in half. Code.org is now inviting an additional 300 teachers to give the tool a try. "Many teachers who lead computer science courses," EdWeek notes, "don't have a degree in the subject -- or even much training on how to teach it -- and might be the only educator in their school leading a computer science course."

Stanford's Piech Lab is headed by assistant professor of CS Chris Piech, who also runs the wildly-successful free Code in Place MOOC (30,000+ learners and counting), which teaches fundamentals from Stanford's flagship introduction to Python course. Prior to coming up with the new AI teaching assistant, which automatically assesses Code.org students' JavaScript game code, Piech worked on a Stanford Research team that partnered with Code.org nearly a decade ago to create algorithms to generate hints for K-12 students trying to solve Code.org's Hour of Code block-based programming puzzles (2015 paper [PDF]). And several years ago, Piech's lab again teamed with Code.org on Play-to-Grade, which sought to "provide scalable automated grading on all types of coding assignments" by analyzing the game play of Code.org students' projects. Play-to-Grade, a 2022 paper (PDF) noted, was "supported in part by a Stanford Hoffman-Yee Human Centered AI grant" for AI tutors to help prepare students for the 21st century workforce. That project also aimed to develop a "Super Teaching Assistant" for Piech's Code in Place MOOC. LinkedIn co-founder Reid Hoffman, who was present for the presentation of the 'AI Tutors' work he and his wife funded, is a Code.org Diamond Supporter ($1+ million). In other AI grading news, Texas will use computers to grade written answers on this year's STAAR tests. The state will save more than $15 million by using technology similar to ChatGPT to give initial scores, reducing the number of human graders needed.