All those original shows on streaming services brought us "peak TV." But the New York Times reports on the flipside: back in the cable universe, they're experiencing "zombie TV": In 2015, the USA cable network was a force in original programming. Dramas like "Suits," "Mr. Robot" and "Royal Pains" either won awards or attracted big audiences. What a difference a few years make. Viewership is way down, and USA's original programming department is gone. The channel has had just one original scripted show this year, and it is not exclusive to the network — it also airs on another channel. During one 46-hour stretch last week, USA showed repeats of NBC's "Law & Order: Special Victims Unit" for all but two hours, when it showed reruns of CBS' "NCIS" and "NCIS: Los Angeles."

Instead of standing out among its peers, USA is emblematic of cable television's transformation. Many of the most popular channels — TBS, Comedy Central, MTV — have quickly morphed into zombie versions of their former selves. Networks that were once rich with original scripted programming are now vessels for endless marathons of reruns, along with occasional reality shows and live sports... Advertisers have begun to pull money from cable at high rates, analysts say, and leaders at cable providers have started to question what their consumers are paying for. In a dispute with Disney this year, executives who oversee the Spectrum cable service said media companies were letting their cable "programming house burn to the ground...."

The media companies that own the channels are in a bind. The so-called cable bundle was enormously profitable for media companies, and more than 100 million households subscribed at the peak. But subscribers are rapidly declining as people migrate toward streaming. Now roughly 70 million households subscribe to cable. As a result, most media companies are pulling resources from their individual cable networks and directing investment toward their streaming services. Peacock, which is owned by NBCUniversal, also the parent of USA, has begun making more and more original scripted shows over the last three years.

However, most streaming services are hemorrhaging cash. (An NBCUniversal executive said this week that Peacock would lose $2.8 billion this year.) Cable, although it is getting smaller, remains profitable.
Media analyst Michael Nathanson believes last year was saw a "tipping point" when cable advertising decreased — by double-digit percentages — in five consecutive fiscal quarters. "Advertisers are starting to realize that there's really nothing on here and they shouldn't pay for it."

One consultant who works with entertainment companies and used to run marketing at the Oxygen cable network tells the newspaper that cable channels "are being stripped for parts." The article calculates that in 2022 there were 39% fewer scripted programs on basic and premium cable than there were in 2015.

"Reruns are filling the hole."

TechSpot remembers that Turbo Pascal "stands out as one of the first instances of an integrated development environment (IDE), providing a text-based interface through which developers could write their code, compile it, and finally link it with runtime libraries." The early IDE, written in Assembly, eschewed the use of floppies, instead building the code directly in RAM for an unprecedented performance boost.

The language demonstrated superior speed, greater convenience, and a more affordable price compared to its competition. Philippe Kahn, Borland's CEO who initially conceptualized turning the new language into an all-in-one product, decided to sell the software via mail orders for just $49.95, establishing a market presence for the then-newly founded company.
It was called "Turbo" because its use of RAM made it considerable faster, adds the Register: Anders Hejlsberg, who would later go on to join Microsoft as part of the C# project, is widely credited as creator of the language, with Borland boss Philippe Kahn identifying the need for the all-in-one tool...

Version 1 had limitations. Source code files, for example, were limited to 64 KB. It would only produce .COM executable files for DOS and CP/M — although other architectures and operating systems were supported. It would also run from a single floppy disk, saving users from endless swapping in a world where single drives were the norm and a hard disk seemed impossibly exotic — and expensive... However, it was with version 4, in 1987, that Turbo Pascal changed dramatically. For one, support for CP/M and CP/M-86 was dropped, and the compiler would generate .EXE executables under DOS, lifting the .COM restrictions...

For this writer, 1989's version 5.5 was peak Turbo Pascal. Object-oriented programming features turned up, including classes and inheritance, and a step-by-step debugger. Version 6 and 7 brought in inline assembly and support for the creation of Windows executables and DLLs respectively, but version 7 also marked the end of the line as far as Borland was concerned. Turbo Pascal for Windows would turn up, but was eventually superseded by Delphi.

However, the steamroller of tools such as Visual Basic 3 ensured that Borland never had the same success in Windows that it enjoyed under DOS. As for Turbo Pascal, several versions were eventually released by Borland as freeware including version 1 for DOS, 5.5, and 7.
I once took a computer programming course taught entirely in Pascal. (Functions, subroutines, and procedures...)

Any Slashdot readers have their own memories to share about Pascal?

"Every day, 3 trillion dollars worth of transactions are handled by a 64-year-old programming language that hardly anybody knows anymore," writes PC Magazine. But most school's don't teach the mainframe programming language COBOL any more, and "COBOL cowboys" are aging out of the workforce, with replacements in short supply.

"This is precisely the kind of problem that IBM thinks it can fix with AI." IBM's approach is fairly straightforward: Rather than relying exclusively on a limited pool of human programmers to solve the problem, it built a generative AI-powered code assistant (watsonx) that helps convert all that dusty old COBOL code to a more modern language, thereby saving coders countless hours of reprogramming. In extremely simplified terms, the process is similar to feeding an essay written in English into ChatGPT and asking it to translate certain paragraphs into Esperanto. It allows programmers to take a chunk of COBOL and enlist watsonx to transform it into Java.

But of course, it's not quite that simple in practice... After IBM and the customer have a thorough understanding of the application landscape, the data flow, and the existing dependencies, "we help them refactor their applications," says IBM's Vice President of Product Management, IT Automation, Keri Olson. "That is, breaking it down into smaller pieces, which the customer can selectively choose, at that point, to do the modernization from COBOL to Java." Skyla Loomis, IBM's Vice President of IBM Z Software adds, "But you have to remember that this is a developer assistant tool. It's AI assisted, but it still requires the developer. So yes, the developer is involved with the tooling and helping the customers select the services."

Once the partnership between man and machine is established, the AI steps in and says, 'Okay, I want to transform this portion of code. The developer may still need to perform some minor editing of the code that the AI provides, Loomis explains. "It might be 80 or 90 percent of what they need, but it still requires a couple of changes. It's a productivity enhancement — not a developer replacement type of activity."
The article quotes a skeptical Gartner Distinguished Vice President and Analyst, who notes that IBM "has no case studies, at this time, to validate its claims."

The Go team conducted a survey of Go Developers in August — and has just released the results. Among the findings: "90% of survey respondents saying they felt satisfied while working with Go during the prior year," while 6% said they were dissastified. Further, the number of people working with Go continues to increase; we see evidence of this from external research like Stack Overflow's Developer Survey (which found 14% of professional developers worked with Go during the past year, a roughly 15% year-over-year increase), as well as analytics for go.dev (which show an 8% rise in visitors year-over-year). Combining this growth with a high satisfaction score is evidence that Go continues to appeal to developers, and suggests that many developers who choose to learn the language feel good about their decision long afterwards...

As in prior years, the majority of survey respondents told us they work with Go on Linux (63%) and macOS (58%) systems... We do continue to see that newer members of the Go community are more likely to be working with Windows than more experienced Go developers. We interpret this as a signal that Windows-based development is important for onboarding new developers to the Go ecosystem, and is a topic our team hopes to focus on more in 2024...

While x86-compatible systems still account for the majority of development (89%), ARM64 is also now used by a majority of respondents (56%). This adoption appears to be partly driven by Apple Silicon; macOS developers are now more likely to say they develop for ARM64 than for x86-based architectures (76% vs. 71%). However, Apple hardware isn't the only factor driving ARM64 adoption: among respondents who don't develop on macOS at all, 29% still say they develop for ARM64.
The most-preferred code editors among the surveyed Go programmers were VS Code (44%), GoLand (31%), Vim/Neovim (16%), and Emacs (3%). 52% of the survey's respondents actually selected "very satisfied" for their feelings about Go — the highest possible rating.

Other interesting findings:

• " The top requests for improving toolchain warnings and errors were to make the messages more comprehensible and actionable; this sentiment was shared by developers of all experience levels, but was particularly strong among newer Go developers."

• "Three out of every four respondents work on Go software that also uses cloud services; this is evidence that developers see Go as a language for modern, cloud-based development."

• The experimental gonew tool (which offers predefined templates for instantiating new Go projects) "appears to solve critical problems for Go developers (especially developers new to Go) and does so in a way that matches their existing workflows for starting a new project. Based on these findings, we believe gonew can substantially reduce onboarding barriers for new Go developers and ease adoption of Go in organizations."

• And when it comes to AI, "Go developers said they are more interested in AI/ML tooling that improves the quality, reliability, and performance of code they write, rather than writing code for them."

AI coding assistants are here to stay -- but just how big a difference they make is still unclear. From a report: Thomas Dohmke, GitHub's CEO: "You've got a lot of tabs open, you're planning a vacation, maybe you're reading the news. At last you copy the text you need and go back to your code, but it's 20 minutes later and you lost the flow." The key idea behind Copilot and other programs like it, sometimes called code assistants, is to put the information that programmers need right next to the code they are writing.

The tool tracks the code and comments (descriptions or notes written in natural language) in the file that a programmer is working on, as well as other files that it links to or that have been edited in the same project, and sends all this text to the large language model behind Copilot as a prompt. (GitHub co-developed Copilot's model, called Codex, with OpenAI. It is a large language model fine-tuned on code.) Copilot then predicts what the programmer is trying to do and suggests code to do it. This round trip between code and Codex happens multiple times a second, the prompt updating as the programmer types. At any moment, the programmer can accept what Copilot suggests by hitting the tab key, or ignore it and carry on typing. The tab button seems to get hit a lot. A study of almost a million Copilot users published by GitHub and the consulting firm Keystone Strategy in June -- a year after the tool's general release -- found that programmers accepted on average around 30% of its suggestions, according to GitHub's user data.

[...] Copilot has changed the basic skills of coding. As with ChatGPT or image makers like Stable Diffusion, the tool's output is often not exactly what's wanted -- but it can be close. "Maybe it's correct, maybe it's not -- but it's a good start," says Arghavan Moradi Dakhel, a researcher at Polytechnique Montreal in Canada who studies the use of machine-learning tools in software development. Programming becomes prompting: rather than coming up with code from scratch, the work involves tweaking half-formed code and nudging a large language model to produce something more on point.

"Advent of Code" has begun. New programming puzzles will appear every day until Christmas at AdventOfCode.com — and the annual event (first started in 2015) has grown into a worldwide phenomenon. This year's first puzzle has been completed by over 150,000 programmers (with another 115,652 completing Day Two's puzzle). And 108,000 fans have also joined the Advent of Code subReddit.

Contest-related comments are popping up all around the web. Some participants are live streaming their puzzle-solving efforts on Twitch. Self-described computer nerd Gary Grady is tweeting cartoons about each day's puzzle. JetBrains is even giving away some prizes in their "Advent of Code with Kotlin" event. And JetBrains developer advocate Sebastian Aigner is also hosting daily livestreams about each puzzle.

It's hard to overstate how big this event has become. This year's event attracted 60 sponsors, including Kotlin (for the third consecutive year), as well as Spotify, Shopify, and Sony Interactive Entertainment (as well as JPMorgan Chase, Bank of America, and American Express). Individual donors can get a special badge next to their name, and there's also a shop selling coffee mugs and t-shirts. But at its core is real-world developer Eric Wastl (plus a team of loyal beta-testers) sharing his genuine fondness for computer programming. Wastl is also the creator of a satirical web page for the fast, lightweight, cross-platform framework Vanilla JS ("so popular that browsers have been automatically loading it for over a decade") and also curates a collection of "things in PHP which make me sad".

And you can find him on X sharing encouraging comments for this year's participants.

"Structured concurrency is a new way to use multithreading in Java," reports InfoWorld.

"It allows developers to think about work in logical groups while taking advantage of both traditional and virtual threads." Available in preview in Java 21, structured concurrency is a key aspect of Java's future, so now is a good time to start working with it... Java's thread model makes it a strong contender among concurrent languages, but multithreading has always been inherently tricky. Structured concurrency allows you to use multiple threads with structured programming syntax. In essence, it provides a way to write concurrent software using familiar program flows and constructs. This lets developers focus on the business at hand, instead of the orchestration of threading.

As the JEP for structured concurrency says, "If a task splits into concurrent subtasks then they all return to the same place, namely the task's code block." Virtual threads, now an official feature of Java, create the possibility of cheaply spawning threads to gain concurrent performance. Structured concurrency provides the simple syntax to do so. As a result, Java now has a unique and highly-optimized threading system that is also easy to understand...

Between virtual threads and structured concurrency, Java developers have a compelling new mechanism for breaking up almost any code into concurrent tasks without much overhead... Any time you encounter a bottleneck where many tasks are occurring, you can easily hand them all off to the virtual thread engine, which will find the best way to orchestrate them. The new thread model with structured concurrency also makes it easy to customize and fine-tune this behavior. It will be very interesting to see how developers use these new concurrency capabilities in our applications, frameworks, and servers going forward.
It involves a new class StructuredTaskScope located in the java.util.concurrent library. (InfoWorld points out that "you'll need to use --enable-preview and --source 21 or --source 22 to enable structured concurrency.")

Their reporter shared an example on GitHub, and there's more examples in the Java 21 documentation. "The structured concurrency documentation includes an example of collecting subtask results as they succeed or fail and then returning the results."

The Linux Foundation's own "Open Software Security foundation" has an associated project called Alpha-Omega funded by Microsoft, Google, and Amazon with a mission to catalyze sustainable security improvements to critical open source projects and ecosystems.

It was established nearly two years ago in February of 2022 — and this month announced plans to continue supporting the Rust Foundation Security Initiative: 2022 was also the first full year of operation for the Rust Foundation — an independent nonprofit dedicated to stewarding the Rust programming language and supporting its global community. Given the considerable growth and rising popularity of the Rust programming language in recent years, it has never been more critical to have a healthy and well-funded foundation in place to help ensure the safety and security of this important language.

When the Rust Foundation emerged, OpenSSF recognized a shared vision of global open source security baked into their organizational priorities from day one. These shared security values were the driving force behind Alpha-Omega's decision to grant $460k USD to the Rust Foundation in 2022. This funding helped underwrite their Security Initiative — a program dedicated to improving the state of security within the Rust programming language ecosystem and sowing security best practices within the Rust community. The Security Initiative began in earnest this past January and has now been in operation for a full year with many achievements to note and exciting plans in development.

While security is a clear priority of the Rust language itself and can be seen in its memory safety-critical features, the Rust Project cannot reasonably be expected to foster long term, sustainable security without proper support and funding. Indeed, there is still a pervasive attitude across technology that cybersecurity is being managed and prioritized by "someone else." The unfortunate impact of this attitude is that critical security work often falls on overburdened and under-resourced open source maintainers. By prioritizing the Security Initiative during their first full year in operation, the Rust Foundation has taken on the responsibility of overseeing — and supporting — security improvements within the Rust ecosystem while ensuring meaningful progress...

Alpha-Omega is excited to announce our second year of supporting the Rust Foundation Security Initiative. We believe that this funding will build on the good work and momentum established by the Rust Foundation in 2023. Through this partnership, we are helping relieve maintainer burdens while paving an important path towards a healthier and more secure future within the Rust ecosystem.
Meanwhile, this month the Rust Foundation announced that downloads from Rust's package repository crates.io have now reached 45 billion — and that the foundation is "committed to facilitating the healthy growth of Rust through funding and resources for the community and the Project.

"After conducting initial planning and research and getting approval from our board of directors, we are pleased to announce our intention to help fulfill this commitment by developing a Rust Foundation training and certification program." We continue to be supportive of anyone creating Rust training and education materials. In fact, we are proud to have provided funding to a few individuals involved in this work via our Community Grants Program. Our team is also aware that commercial Rust training courses already exist and that global training entities are already developing their own Rust-focused programs. Given the value of Rust in professional open source, this makes sense. However, we are eager to introduce a program that will allow us to direct profits back into the Rust ecosystem.

As a nonprofit organization, we sit in a unique position thanks to the tools, connections, insights, administrative support, and resources at our disposal — all of which will add value to course material aimed at professional development and adoption. We see our forthcoming program as one tool of many that can be used to verify skills for prospective employers, and for those employers to build out their professional teams of Rust expertise. We will remain supportive of existing training programs offered by Rust Foundation member companies and we'll look for ways to ensure this remains the case as program development progresses... There is no set launch date for the Rust Foundation training and certification program yet, but we plan to continue laying high-quality groundwork in Q4 of 2023 and the first half of 2024.

An anonymous reader quotes a report from Hackaday: The BBC has a long history of teaching the world about computers. The broadcaster's name was proudly displayed on the BBC Micro, and BBC Basic was the programming language developed especially for that computer. Now, BBC Basic is back and running on a whole mess of modern platforms. BBC Basic for SDL 2.0 will run on Windows, MacOS, x86 Linux, and even Raspberry Pi OS, Android, and iOS. Desktop versions of the programming environment feature a BASIC editor that has syntax coloring for ease of use, along with luxury features like search and replace that weren't always available at the dawn of the microcomputer era. Meanwhile, the smartphone versions feature a simplified interface designed to work better in a touchscreen environment.

It's weird to see, but BBC Basic can actually do some interesting stuff given the power of modern hardware. It can address up to 256 MB of memory, and work with far more advanced graphical assets than would ever have been possible on the original BBC Micro. If you honed your programming skills on that old metal, you might be impressed with what they can achieve with BBC Basic in a new, more powerful context.

Long-time Slashdot reader destinyland writes: The Linux Foundation recently funded a new "security developer in residence" position for Python. (It's funded through the Linux Foundation's own "Open Software Security foundation", which has a stated mission of partnering with open source project maintainers "to systematically find new, as-yet-undiscovered vulnerabilities in open source code, and get them fixed to improve global software supply chain security.") The position went to the lead maintainer for the HTTP client library urllib3, the most downloaded package on the Python Package Index with over 10 billion downloads. But he hopes to create a ripple effect by demonstrating the impact of security investments in critical communities — ultimately instigating a wave of improvements to all software supply chains. (And he's also documenting everything for easy replication by other communities...)

So far he's improved the security of Python's release processes with signature audits and security-hardening automation. But he also learned that CVE numbers were being assigned to newly-discovered vulnerabilities by the National Cyber Security Division of the America's Department of Homeland Security — often without talking to anyone at the Python project. So by August he'd gotten the Python Software Foundation authorized as a CVE Numbering Authority, which should lead to more detailed advisories (including remediation information), now reviewed and approved by Python's security response teams.

"The Python Software wants to help other Open Source organizations, and will be sharing lessons learned," he writes in a blog post. And he now says he's already been communicating with the Curl program about his experiences to help them take the same step, and even authored a guide to the process for other open source projects.

Researchers have used the technology behind the AI chatbot ChatGPT to create a fake clinical-trial data set to support an unverified scientific claim. From a report: In a paper published in JAMA Ophthalmology on 9 November, the authors used GPT-4 -- the latest version of the large language model on which ChatGPT runs -- paired with Advanced Data Analysis (ADA), a model that incorporates the programming language Python and can perform statistical analysis and create data visualizations. The AI-generated data compared the outcomes of two surgical procedures and indicated -- wrongly -- that one treatment is better than the other.

"Our aim was to highlight that, in a few minutes, you can create a data set that is not supported by real original data, and it is also opposite or in the other direction compared to the evidence that are available," says study co-author Giuseppe Giannaccare, an eye surgeon at the University of Cagliari in Italy. The ability of AI to fabricate convincing data adds to concern among researchers and journal editors about research integrity. "It was one thing that generative AI could be used to generate texts that would not be detectable using plagiarism software, but the capacity to create fake but realistic data sets is a next level of worry," says Elisabeth Bik, a microbiologist and independent research-integrity consultant in San Francisco, California. "It will make it very easy for any researcher or group of researchers to create fake measurements on non-existent patients, fake answers to questionnaires or to generate a large data set on animal experiments."

This week the Linux Foundation "announced the intention to form the High Performance Software Foundation.

"Through a series of technical projects, the High Performance Software Foundation aims to build, promote, and advance a portable software stack for high performance computing by increasing adoption, lowering barriers to contribution, and supporting development efforts." As use of high performance computing becomes ubiquitous in scientific computing and digital engineering, and AI use cases multiply, more and more data centers deploy GPUs and other compute accelerators. The High Performance Software Foundation intends to leverage investments made by the United States Department of Energy's Exascale Computing Project, the EuroHPC Joint Undertaking, and other international projects in accelerated high performance computing to exploit the performance of this diversifying set of architectures. As an umbrella project under the Linux Foundation, HPSF intends to provide a neutral space for pivotal projects in the high performance software ecosystem, enabling industry, academia, and government entities to collaborate together on the scientific software stack.

The High Performance Software Foundation already benefits from strong support across the high performance computing landscape, including leading companies and organizations like Amazon Web Services, Argonne National Laboratory, CEA, CIQ, Hewlett Packard Enterprise, Intel, Kitware, Lawrence Berkeley National Laboratory, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, NVIDIA, Oak Ridge National Laboratory, Sandia National Laboratory, and the University of Oregon.
Its first open source technical projects include:

• Spack: the high performance computing package manager
• Kokkos: a performance-portable programming model for writing modern C++ applications in a hardware-agnostic way.
• AMReX: a performance-portable software framework designed to accelerate solving partial differential equations on block-structured, adaptively refined meshes.
• WarpX: a performance-portable Particle-in-Cell code with advanced algorithms that won the 2022 Gordon Bell Prize
• Trilinos: a collection of reusable scientific software libraries, known in particular for linear, non-linear, and transient solvers, as well as optimization and uncertainty quantification.
• Apptainer: a container system and image format specifically designed for secure high-performance computing.
• VTK-m: a toolkit of scientific visualization algorithms for accelerator architectures.
• HPCToolkit: performance measurement and analysis tools for computers ranging from laptops to the world's largest GPU-accelerated supercomputers.
• E4S: the Extreme-scale Scientific Software Stack
• Charliecloud: high performance computing-tailored, lightweight, fully unprivileged container implementation.

Python "come with downsides," argues a new article in Communications of the ACM. "Its programs tend to run slowly, and because it is inefficient at running processes in parallel, it is not well suited to some of the latest AI programming."

"Hoping to overcome those difficulties, computer scientist Chris Lattner set out to create a new language, Mojo, which offers the ease of use of Python, but the performance of more complex languages such as C++ or Rust." Lattner tells the site "we don't want to break Python, we want to make Python better," while software architect Doug Meil says Mojo is essentially "Python for AI... and it's going to be way faster in scale across multiple hardware platforms." Lattner teamed up with Tim Davis, whom he had met when they both worked for Google, to form Modular in January 2022. The company, where Lattner is chief executive officer and Davis chief product officer, provides support for companies working on AI and is developing Mojo.

A modern AI programming stack generally has Python on top, Lattner says, but because that is an inefficient language, it has C++ underneath to handle the implementation. The C++ then must communicate with performance accelerators or GPUs, so developers add a platform such as Compute Unified Device Architecture (CUDA) to make efficient use of those GPUs. "Mojo came from the need to unify these three different parts of the stack so that we could build a unified solution that can scale up and down," Lattner says. The result is a language with the same syntax as Python, so people used to programming in Python can adopt it with little difficulty, but which, by some measures, can run up to 35,000 times faster. For AI, Mojo is especially fast at performing the matrix multiplications used in many neural networks because it compiles the multiplication code to run directly on the GPU, bypassing CUDA...

"Increasingly, code is not being written by computer programmers. It's being written by doctors and journalists and chemists and gamers," says Jeremy Howard, an honorary professor of computer science at the University of Queensland, Australia, and a co-founder of fast.ai, a. "All data scientists write code, but very few data scientists would consider themselves professional computer programmers." Mojo attempts to fill that need by being a superset of Python. A program written in Python can be copied into Mojo and will immediately run faster, the company says. The speedup comes from a variety of factors. For instance, Mojo, like other modern languages, enables threads, small tasks that can be run simultaneously, rather than in sequence. Instead of using an interpreter to execute code as Python does, Mojo uses a compiler to turn the code into assembly language.

Mojo also gives developers the option of using static typing, which defines data elements and reduces the number of errors... "Static behavior is good because it leads to performance," Lattner says. "Static behavior is also good because it leads to more correctness and safety guarantees."
Python creator Guido van Rossum "says he is interested to watch how Mojo develops and whether it can hit the lofty goals Lattner is setting for it..." according to the article, " but he emphasizes that the language is in its early stages and, as of July 2023, Mojo had not yet been made available for download."


In June, Lattner did an hour-long interview with the TWIML AI podcast. And in 2017 Chris Lattner answered questions from Slashdot's readers.

An anonymous reader shared this report from InfoWorld: JetBrains' Kotlin language, a Java rival endorsed by Google for Android mobile development, continues to scale up Tiobe's index of language popularity, reaching the 15th spot in the November 2023 rankings...

Software quality services company Tiobe cites Kotlin advantages including interoperability with Java and unrivaled Android accommodations as reasons for the language's rise. Kotlin, Tiobe CEO Paul Jansen said, also fits in with a modern programming culture of expressive languages that have a strong type system and avoid null pointer exceptions by design. "Based on my experience, I am pretty sure Kotlin can reach a top 10 position," Jansen said. It remains to be seen if it can ever scale as high as a top four slot, he added...

In the rival Pypl Popularity of Programming languages index this month, Kotlin was ranked 13th with a 1.76% share, having slipped slightly year-over-year.
Kotlin's rank on the TIOBE index rose three positions in the last month — after rising two positions the month before. TIOBE's CEO says the language has now achieved its highest ranking ever on the index, surpassing 2017's "first wave of Kotlin popularity...when Google announced first class support for Kotlin on Android."

Rust now ranks #20 on the index, behind Delphi/Object Pascal, Swift, Ruby, and R.

Here's TIOBE November rankings for top-20 most popular programming languages:

1. Python
2. C
3. C++
4. Java
5. C#
6. JavaScript
7. PHP
8. Visual Basic
9. SQL
10. Assembly Language
11. Scratch
12. Fortran
13. Go
14. MATLAB
15. Kotlin
16. Delphi/Object Pascal
17. Swift
18. Ruby
19. R
20. Rust

ZDNet shares on update on "Rust in Linux: Where we are and where we're going next," citing a talk at the Linux Plumbers Conference in Richmond, Virginia by Linux/Rust developer Miguel Ojeda: In brief, Rust Linux is continuing to mature and is getting strong support from developers and vendors, such as Cisco, Samsung, and Canonical... Rust is taking the steps it needs to become — along with C — a fully-fledged member of the Linux language toolchain... That's not to say that we're ready to retire C for Rust just yet. In fact, that day is unlikely ever to come. But Rust is definitely on its way to becoming an important language for Linux development...

As for the day-to-day work that's required to make Rust fully integrated with Linux, the "official" website of the initiative is now the self-explanatory Rust for Linux. This site is your one-stop source for all things Rust on Linux... However, the move forward has not been straightforward. Rust on Linux developers have discovered some problems along the way. For example, while deadlocks, when two or more threads are waiting on the other to finish, are safe in Rust, because they don't result in undefined behavior, they're not safe in the Linux kernel. The programmers are working on fixing this issue...

A related issue is that there's growing interest in backporting Rust support into long-term support (LTS) versions of Linux, specifically 5.15 and 6.1. Some people are especially showing interest in the super LTS Linux 6.1 kernel. However, Linux doesn't generally allow backports into LTS Linuxes. So, if you really, really want fully featured Rust support in an older LTS kernel, you're going to need to pay for it in one way or the other. Another general rule that Rust developers have decided they're going to try to "break" is the rule against duplicate drivers. Usually, no one wants anyone wasting time reinventing the wheel, but some maintainers are open to the idea of experimenting with Rust, by starting simple with a driver they're already familiar with...

These movements are small steps forward, but they're all critical for making Rust equal to C as a Linux programming language.

The Python community is staying busy.

• Three weeks ago a new podcast launched with Python core developer/steering council member Pablo Galindo and Python developer-in-residence Åukasz Langa.

• This month the Python Sofware Foundation announced its seventh Python Developers Survey.

• And for the fifth year in a row the foundation partnered with JetBrains to offer a 30% discount on JetBrains' dedicated Python Integrated Development Environment "PyCharm" — with all proceeds benefitting the Python Software Foundation ("The discount will be automatically applied when you check out. The promotion will only be available through November 27th...")
  
  A PSF blog post about the fundraiser notes you can also just make a direct donation to the Python Software Foundation, or sign up to become a foundation member to "help us sustain what we do."

Despite more than a decade of reminding, prodding, and downright nagging, a surprising number of developers still can't bring themselves to keep their code free of credentials that provide the keys to their kingdoms to anyone who takes the time to look for them. From a report: The lapse stems from immature coding practices in which developers embed cryptographic keys, security tokens, passwords, and other forms of credentials directly into the source code they write. The credentials make it easy for the underlying program to access databases or cloud services necessary for it to work as intended. [...]

The number of studies published since following the revelations underscored just how common the practice had been and remained in the years immediately following Uber's cautionary tale. Sadly, the negligence continues even now. Researchers from security firm GitGuardian this week reported finding almost 4,000 unique secrets stashed inside a total of 450,000 projects submitted to PyPI, the official code repository for the Python programming language. Nearly 3,000 projects contained at least one unique secret. Many secrets were leaked more than once, bringing the total number of exposed secrets to almost 57,000.

Programmer and writer James Somers, writing for New Yorker: Yes, our jobs as programmers involve many things besides literally writing code, such as coaching junior hires and designing systems at a high level. But coding has always been the root of it. Throughout my career, I have been interviewed and selected precisely for my ability to solve fiddly little programming puzzles. Suddenly, this ability was less important.

I had gathered as much from Ben (friend of the author), who kept telling me about the spectacular successes he'd been having with GPT-4. It turned out that it was not only good at the fiddly stuff but also had the qualities of a senior engineer: from a deep well of knowledge, it could suggest ways of approaching a problem. For one project, Ben had wired a small speaker and a red L.E.D. light bulb into the frame of a portrait of King Charles, the light standing in for the gem in his crown; the idea was that when you entered a message on an accompanying Web site the speaker would play a tune and the light would flash out the message in Morse code. (This was a gift for an eccentric British expat.) Programming the device to fetch new messages eluded Ben; it seemed to require specialized knowledge not just of the microcontroller he was using but of Firebase, the back-end server technology that stored the messages. Ben asked me for advice, and I mumbled a few possibilities; in truth, I wasn't sure that what he wanted would be possible. Then he asked GPT-4. It told Ben that Firebase had a capability that would make the project much simpler. Here it was -- and here was some code to use that would be compatible with the microcontroller.

Afraid to use GPT-4 myself -- and feeling somewhat unclean about the prospect of paying OpenAI twenty dollars a month for it -- I nonetheless started probing its capabilities, via Ben. We'd sit down to work on our crossword project, and I'd say, "Why don't you try prompting it this way?" He'd offer me the keyboard. "No, you drive," I'd say. Together, we developed a sense of what the A.I. could do. Ben, who had more experience with it than I did, seemed able to get more out of it in a stroke. As he later put it, his own neural network had begun to align with GPT-4's. I would have said that he had achieved mechanical sympathy. Once, in a feat I found particularly astonishing, he had the A.I. build him a Snake game, like the one on old Nokia phones. But then, after a brief exchange with GPT-4, he got it to modify the game so that when you lost it would show you how far you strayed from the most efficient route. It took the bot about ten seconds to achieve this. It was a task that, frankly, I was not sure I could do myself.

In chess, which for decades now has been dominated by A.I., a player's only hope is pairing up with a bot. Such half-human, half-A.I. teams, known as centaurs, might still be able to beat the best humans and the best A.I. engines working alone. Programming has not yet gone the way of chess. But the centaurs have arrived. GPT-4 on its own is, for the moment, a worse programmer than I am. Ben is much worse. But Ben plus GPT-4 is a dangerous thing.

Started in 2013, "Hour of Code" is an annual tradition started by the education non-profit Code.org (which provides free coding lessons to schools). Its FAQ describes the December event for K-12 students as "a worldwide effort to celebrate computer science, starting with 1-hour coding activities," and over 100 million schoolkids have participated over the years.

This year's theme will be "Creativity With AI," and the "computer vision" lesson includes a short video (less than 7 minutes) featuring a Tesla Autopilot product manager from its computer vision team. "I build self-driving cars," they say in the video. "Any place where there can be resources used more efficiently I think is a place where technology can play a role. But of course one of the best, impactful ways of AI, I hope, is through self-driving cars." (The video then goes on to explain how lots of training data ultimately generates a statistical model, "which is just a fancy way of saying, a guessing machine.")

The 7-minute video is part of a larger lesson plan (with a total estimated time of 45 minutes) in which students tackle a fun story problem. If a sports arena's scoreboard is showing digital numbers, what series of patterns would a machine-vision system have to recognize to identify each digit. (Students are asked to collaborate in groups.) And it's just one of seven 45-minute lessons, each one accompanied by a short video. (The longest video is 7 minutes and 28 seconds, and all seven videos, if watched back-to-back, would run for about 31 minutes.)

Not all the lessons involve actual coding, but the goal seems to be familiarizing students (starting at the 6th grade level) with artificial intelligence of today, and the issues it raises. The second-to-last lesson is titled "Algorithmic Bias" — with a video including interviews with an ethicist at Open AI and professor focused on AI from both MIT and Stanford. And the last lesson — "Our AI Code of Ethics" — challenges students to assemble documents and videos on AI-related "ethical pitfalls," and then pool their discoveries into an educational resource "for AI creators and legislators everywhere."

This year's installment is being billed as "the largest learning event in history." And it's scheduled for the week of December 4 so it coincides with "Computer Science Education Week" (a CS-education event launched in 2009 by the Association for Computing Machinery, with help from partners including Intel, Microsoft, Google, and the National Science Foundation).

The senior security editor at Ars Technica writes: Highly invasive malware targeting software developers is once again circulating in Trojanized code libraries, with the latest ones downloaded thousands of times in the last eight months, researchers said Wednesday.

Since January, eight separate developer tools have contained hidden payloads with various nefarious capabilities, security firm Checkmarx reported. The most recent one was released last month under the name "pyobfgood." Like the seven packages that preceded it, pyobfgood posed as a legitimate obfuscation tool that developers could use to deter reverse engineering and tampering with their code. Once executed, it installed a payload, giving the attacker almost complete control of the developerâ(TM)s machine. Capabilities include:


- Exfiltrate detailed host information
- Steal passwords from the Chrome web browser
- Set up a keylogger
- Download files from the victim's system
- Capture screenshots and record both screen and audio
- Render the computer inoperative by ramping up CPU usage, inserting a batch script in the startup directory to shut down the PC, or forcing a BSOD error with a Python script
- Encrypt files, potentially for ransom
- Deactivate Windows Defender and Task Manager
- Execute any command on the compromised host


In all, pyobfgood and the previous seven tools were installed 2,348 times. They targeted developers using the Python programming language... Downloads of the package came primarily from the US (62%), followed by China (12%) and Russia (6%)
Ars Technica concludes that "The never-ending stream of attacks should serve as a cautionary tale underscoring the importance of carefully scrutinizing a package before allowing it to run."