The Tiobe index tries to track the popularity of programming languages by counting the number of search results for the language's name followed by the word "programming" (on 25 different search engines). And this month there were some surprises...

By TIOBE's reckoning, compared to a year ago PHP has now fallen from #7 to #12, while Delphi/Object Pascal shot up five spots from #16 to #11. In that same year, Fortran jumped from #12 to #8 — while both Visual Basic and SQL dropped down a single rank. Toward the top of the list, C actually fell from the #2 spot over the last 12 months to the #4 spot.

And Go just reached the #7 rank on the TIOBE's ranking of programming language popularity — "an all time high for Go," according to TIOBE CEO Paul Jansen. In this month's note, he explains what he thinks is unusual about this — starting by saying that Go programs are both fast, and easy in many ways — easy to deploy, easy to learn, and easy to understand. Python for instance is easy to learn but not fast, and deployment for larger Python programs is fragile due to dependencies on all kind of versioned libraries in the environment.

If compared to Rust for instance (another contender for a top position), Go is a tiny bit slower, but the Go programs are much easier to understand. The next hurdle for Go in the TIOBE index is JavaScript at position #6. That will be a tough one to pass. JavaScript is ubiquitous in software development, although for larger JavaScript systems we see a shift to TypeScript nowadays.
"If annual trends continue this way, Go will bypass JavaScript within 3 years," TIOBE's CEO predicts. (Adding "Let's see what the future has in store for Go...") Although the Go team actually has specific plans for the future, according to a blog post this week celebrating Go's 15th anniversary: We're working on making Go better for AI — and AI better for Go — by enhancing Go's capabilities in AI infrastructure, applications, and developer assistance. Go is a great language for building production systems, and we want it to be a great language for building production AI systems, too... For AI applications, we will continue building out first-class support for Go in popular AI SDKs, including LangChainGo and Genkit. And from its very beginning, Go aimed to improve the end-to-end software engineering process, so naturally we're looking at bringing the latest tools and techniques from AI to bear on reducing developer toil, leaving more time for the fun stuff — like actually programming!
TIOBE's top 10 programming language rankings for the month of November:

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

Google's transistion to Safe Coding and memory-safe languages "will take multiple years," according to a post on Google's security blog. So "we're also retrofitting secure-by-design principles to our existing C++ codebase wherever possible," a process which includes "working towards bringing spatial memory safety into as many of our C++ codebases as possible, including Chrome and the monolithic codebase powering our services." We've begun by enabling hardened libc++, which adds bounds checking to standard C++ data structures, eliminating a significant class of spatial safety bugs. While C++ will not become fully memory-safe, these improvements reduce risk as discussed in more detail in our perspective on memory safety, leading to more reliable and secure software... It's also worth noting that similar hardening is available in other C++ standard libraries, such as libstdc++. Building on the successful deployment of hardened libc++ in Chrome in 2022, we've now made it default across our server-side production systems. This improves spatial memory safety across our services, including key performance-critical components of products like Search, Gmail, Drive, YouTube, and Maps... The performance impact of these changes was surprisingly low, despite Google's modern C++ codebase making heavy use of libc++. Hardening libc++ resulted in an average 0.30% performance impact across our services (yes, only a third of a percent) ...

In just a few months since enabling hardened libc++ by default, we've already seen benefits. Hardened libc++ has already disrupted an internal red team exercise and would have prevented another one that happened before we enabled hardening, demonstrating its effectiveness in thwarting exploits. The safety checks have uncovered over 1,000 bugs, and would prevent 1,000 to 2,000 new bugs yearly at our current rate of C++ development...

The process of identifying and fixing bugs uncovered by hardened libc++ led to a 30% reduction in our baseline segmentation fault rate across production, indicating improved code reliability and quality. Beyond crashes, the checks also caught errors that would have otherwise manifested as unpredictable behavior or data corruption... Hardened libc++ enabled us to identify and fix multiple bugs that had been lurking in our code for more than a decade. The checks transform many difficult-to-diagnose memory corruptions into immediate and easily debuggable errors, saving developers valuable time and effort.
The post notes that they're also working on "making it easier to interoperate with memory-safe languages. Migrating our C++ to Safe Buffers shrinks the gap between the languages, which simplifies interoperability and potentially even an eventual automated translation."

Longtime Slashdot reader DaPhil writes: I taught myself to code at 12 years old in the 90s and I've always liked the back-and-forth with the runtime to achieve the right result. I recently got back from other roles to code again, and when starting a new project last year, I decided to give the new "AI assistants" a go.

My initial surprise at the quality and the speed you can achieve when using ChatGPT and/or Copilot when coding turned sour over the months, as I realized that all the joy I felt about trying to get the result I want -- slowly improving my code by (slowly) thinking, checking the results against the runtime, and finally achieving success -- is, well, gone. What I do now is type English sentences in increasingly desperate attempts to get ChatGPT to output what I want (or provide snippets to Copilot to get the right autocompletion), which -- as they are pretty much black boxes -- is frustrating and non-linear: it either "just works," or it doesn't. There is no measure of progress. In a way, having Copilot in the IDE was even worse, since it often disrupts my thinking when suggesting completions.

I've since disabled Copilot. Interestingly, I myself now feel somehow "disabled" without it in the IDE; however, the abstention has given me back the ability to sit back and think, and through that, the joy of programming. Still, it feels like I'm now somehow an ex-drug addict always on the verge of a relapse. I was wondering if any of you felt the same, or if I'm just... old.

Phoronix's Michael Larabel reports: The OpenMP Architecture Review Board announced from SC24 that OpenMP 6.0 is now available as a major upgrade to the OpenMP specification for multi-process programming within C / C++ / Fortran. A big emphasis on OpenMP 6.0 is making it easier for developers to embrace. OpenMP 6.0 aims to make it easier to support parallel programming in new applications, easier to adapt to new use-cases, and more fine-grained developer control.

OpenMP 6.0 simplifies task programming with support for task execution by free-agent threads, allowing for recording of task graphs for efficient replay, and other improvements. OpenMP 6.0 also brings support for array syntax applications, better control over memory allocations and accessibility, easier writing of asynchronous data transfers, and other improvements for enhanced device support / offloading. There is also easier programming of loop transformations, support for induction, support for C23 / Fortran 2023 / C++23, grater user control of storage resources and memory spaces, and other improvements.

An anonymous reader quotes a report from Bloomberg: OpenAI is preparing to launch a new artificial intelligence agent codenamed "Operator" that can use a computer to take actions on a person's behalf (Warning: source may be paywalled; alternative source), such as writing code or booking travel [...]. In a staff meeting on Wednesday, OpenAI's leadership announced plans to release the tool in January as a research preview and through the company's application programming interface for developers [...]. The one nearest completion will be a general-purpose tool that executes tasks in a web browser, one of the people said.

OpenAI Chief Executive Officer Sam Altman hinted at the shift to agents in response to a question last month during an Ask Me Anything session on Reddit. "We will have better and better models," Altman wrote. "But I think the thing that will feel like the next giant breakthrough will be agents." The move to release an agentic AI tool also comes as OpenAI and its competitors have seen diminishing returns from their costly efforts to develop more advanced AI models.

Mark Rainey: Engineers are currently debugging why the Voyager 1 spacecraft, which is 15 billions miles away, turned off its main radio and switched to a backup radio that hasn't been used in over forty years!

I've had some tricky debugging issues in the past, including finding compiler bugs and debugging code with no debugger that had been burnt into prom packs for terminals, however I have huge admiration for the engineers maintaining the operation of Voyager 1.

Recently they sent a command to the craft that caused it to shut off its main radio transmitter, seemingly in an effort to preserve power and protect from faults. This prompted it to switch over to the backup radio transmitter, that is lower power. Now they have regained communication they are trying to determine the cause on hardware that is nearly 50 years old. Any communication takes days. When you think you have a difficult issue to debug, spare a thought for this team.

Paul Kinlan, who leads the Chrome and the Open Web Developer Relations team at Google, asks and answers the question (with a no.): Frameworks are abstractions over a platform designed for people and teams to accelerate their teams new work and maintenance while improving the consistency and quality of the projects. They also frequently force a certain type of structure and architecture to your code base. This isn't a bad thing, team productivity is an important aspect of any software.

I'm of the belief that software development is entering a radical shift that is currently driven by agents like Replit's and there is a world where a person never actually has to manipulate code directly anymore. As I was making broad and sweeping changes to the functionality of the applications by throwing the Agent a couple of prompts here and there, the software didn't seem to care that there was repetition in the code across multiple views, it didn't care about shared logic, extensibility or inheritability of components... it just implemented what it needed to do and it did it as vanilla as it could.

I was just left wondering if there will be a need for frameworks in the future? Do the architecture patterns we've learnt over the years matter? Will new patterns for software architecture appear that favour LLM management?

Google's head of research Yossi Matias maintains that learning to code remains "as important as ever" despite AI's growing role in software development. While AI tools have reduced coding time for some developers -- and Alphabet CEO Sundar Pichai noting that AI now generates a quarter of all code, Matias stressed that human engineers still review and approve AI-generated code.

The Google executive, who also serves as a company VP, acknowledged that junior professionals have faced challenges gaining experience as AI handles entry-level tasks. Google has launched initiatives to support early-career employees through this transition. Matias compared coding literacy to basic mathematics, arguing it provides crucial understanding of technology regardless of career path.

Nathan Sobo "joined GitHub in late 2011 to build the Atom text editor," according to an online biography, "and he led the Atom team until 2018." Max Brunsfeld joined the Atom team in 2013, and "While driving Atom towards its 1.0 launch during the day, Max spent nights and weekends building Tree-sitter, a blazing-fast and expressive incremental parsing framework that currently powers all code analysis at GitHub."

Last year they teamed up with Antonio Scandurra (another Atom alumnus) to launch a new startup called Zed (which in 2023 raised $10 million, according to TechCrunch). And today the open source blog It's FOSS checks in on their open-source code editor — "Zed Editor". Mainly written in Rust, it supports running in CLI, diagnosing project-wide errors, split panes, and markdown previews: By default, any added content is treated as plain text. I used the language switcher to change it to Rust so that I would get proper syntax highlighting, indentation, error detection, and other useful language-specific functions. The switch highlighted all the Rust elements correctly, and I then focused on Zed Editor's user interface. The overall feel of the editor was minimal, with all the important options being laid out nicely.

[Its status bar] had some interesting panels. The first one I checked was the Terminal Panel, which, as the name suggests, lets you run commands, scripts, and facilitates interaction with system files or processes directly from within the editor. I then moved to the Assistant Panel, which is home to various large language models that can be integrated into Zed Editor. There are options like Anthropic, GitHub Copilot Chat, Ollama, OpenAI, and Google AI... The Zed Editor team has also recently introduced Zed AI in collaboration with Anthropic for assisting with coding, allowing for code generation, advanced context-powered interactions, and more...

The real-time collaboration features on Zed Editor are quite appealing too. To check them out, I had to log in with my GitHub account. After logging in, the Collab Panel opened up, and I could see many channels from the official Zed community. I could chat with others, add collaborators to existing projects, join a call with the option to share my screen and track other collaborators' cursors, add new contacts, and carry out many other collaborative tasks.

One can also use extensions and themes to extend what Zed Editor can do. There are some nice pre-installed themes as well.

"The Rust trademark policy has been updated and a new draft is available to view," announced the Rust Foundation this week.

The last proposed trademark policy (in April of 2023) was criticized by open source advocate Bruce Perens in The Register as "far awry of fair use which is legally permitted." The Rust Foundation says this new version has "incorporated a number of suggestions from the Rust community," in a blog post that summarizes the feedback and enumerates specific ways it's been addressed: 1. We primarily plan to lean on community reports for enforcement and have no intention of spending our limited resources policing the work of small creators.

2. We have removed the non-legal language summary and instead have clarified wording throughout as best we can while keeping the policy valid.

3. The Rust trademark does not cover use of the word "Rust" in general and instead pertains to its use in relevant technical settings.

4. We have updated the logo usage policy. Color modifications are allowed.

5. The non-endorsement rule is about managing perception of official affiliation with the Foundation and Rust Project, and is thus subjective.

6. We removed restrictions on the use of "Rust" and "Cargo" in package names. The crates prefixes "rust-" and "cargo-" are no longer reserved to the Rust Project.

7. We will usually allow the community to use the marks on limited merchandise (more details in the updated draft)....

[T]he central purpose of these updates is to empower all Rustaceans to engage with the Rust language ecosystem more confidently. As a final step in this process, we invite you to review the updated policy and share any blocking concerns you might have... Thank you to everyone who weighed in with helpful suggestions on the initial trademark policy draft we shared. The level of engagement and passion within the Rust community is inspiring to all of us at the Rust Foundation.
The tech news site Heise Online writes "It is noticeable that the language is much clearer and dispenses with a lot of legal jargon," in a piece which argues the new draft "should calm the waves and create clarity." The new draft is not only formulated more simply, but is also significantly shorter. Some restrictions have been softened in the new rules or have disappeared completely...

Meanwhile, the Foundation has also adapted its logo so that it is clear which logo stands for the programming language and which for the Foundation. The use of the name Rust is explicitly permitted to identify projects that are either written in the programming language or are compatible with it...

Before the new trademark rules come into force, the Rust Foundation is collecting feedback on the current draft. The web form is open until November 20, 2024.

I had the opportunity to interview esteemed programmer Steve Yegge for the SourceForge Podcast to ask him all about AI-powered coding assistants and the future of programming. "We're moving from where you have to write the code to where the LLM will write the code and you're just having a conversation with it about the code," said Yegge. "That is much more accessible to people who are just getting into the industry."

Steve has nearly 30 years of programming experience working at Geoworks, Amazon, Google, Grab and now SourceGraph, working to build out the Cody AI assistant platform. Here's his Wikipedia page. He's not shy about sharing his opinions or predictions for the industry, no matter how difficult it may be for some to hear. "I'm going to make the claim that ... line-oriented programming, which we've done for the last 40, 50 years, ... is going away. It is dying just like assembly language did, and it will be completely dead within five years."

You can watch the episode on YouTube and stream on all major podcast platforms. A transcription of the podcast is available here.

Software engineer David Gomes writes in a blog post: I sometimes need to search for a website that will "convert a PNG to SVG", or "remove page from PDF" or "resize svg". And these apps are... okay. I don't really trust most of them with my data, and also a lot of times they just don't work or have too many ads. So, I've been noticing a trend of people just using AI agents to create full blown apps for these simple use cases.

I decided to try it myself for a "resize SVG" app since I recently had to go through a bunch of websites to do this. So, I pulled up Replit Agent and even though I've used it before, it doesn't cease to amaze me just how insanely good it is. The level of polish on this product is unlike any other AI agent out there right now. It starts off by drawing up a plan and asking you for feedback on that plan. Then, it'll just go to town and try to build the app. But what's super clever about it is that the agent asks you for feedback along the way. Effectively, the Replit Agent guides you, not the other way around (as one might have expected).

Anton Shilov reports via Tom's Hardware: FFmpeg is an open-source video decoding project developed by volunteers who contribute to its codebase, fix bugs, and add new features. The project is led by a small group of core developers and maintainers who oversee its direction and ensure that contributions meet certain standards. They coordinate the project's development and release cycles, merging contributions from other developers. This group of developers tried to implement a handwritten AVX512 assembly code path, something that has rarely been done before, at least not in the video industry.

The developers have created an optimized code path using the AVX-512 instruction set to accelerate specific functions within the FFmpeg multimedia processing library. By leveraging AVX-512, they were able to achieve significant performance improvements -- from three to 94 times faster -- compared to standard implementations. AVX-512 enables processing large chunks of data in parallel using 512-bit registers, which can handle up to 16 single-precision FLOPS or 8 double-precision FLOPS in one operation. This optimization is ideal for compute-heavy tasks in general, but in the case of video and image processing in particular.

The benchmarking results show that the new handwritten AVX-512 code path performs considerably faster than other implementations, including baseline C code and lower SIMD instruction sets like AVX2 and SSSE3. In some cases, the revamped AVX-512 codepath achieves a speedup of nearly 94 times over the baseline, highlighting the efficiency of hand-optimized assembly code for AVX-512.

GitHub released its annual "State of the Octoverse" report this week. And while "Systems programming languages, like Rust, are also on the rise... Python, JavaScript, TypeScript, and Java remain the most widely used languages on GitHub."

In fact, "In 2024, Python overtook JavaScript as the most popular language on GitHub." They also report usage of Jupyter Notebooks "skyrocketed" with a 92% jump in usage, which along with Python's rise seems to underscore "the surge in data science and machine learning on GitHub..." We're also seeing increased interest in AI agents and smaller models that require less computational power, reflecting a shift across the industry as more people focus on new use cases for AI... While the United States leads in contributions to generative AI projects on GitHub, we see more absolute activity outside the United States. In 2024, there was a 59% surge in the number of contributions to generative AI projects on GitHub and a 98% increase in the number of projects overall — and many of those contributions came from places like India, Germany, Japan, and Singapore...

Notable growth is occurring in India, which is expected to have the world's largest developer population on GitHub by 2028, as well as across Africa and Latin America... [W]e have seen greater growth outside the United States every year since 2013 — and that trend has sped up over the past few years.
Last year they'd projected India would have the most developers on GitHub #1 by 2027, but now believe it will happen a year later. This year's top 10?

1. United States
2. India
3. China
4. Brazil
5. United Kingdom
6. Russia
7. Germany
8. Indonesia
9. Japan
10. Canada

Interestingly, the UK's population ranks #21 among countries of the world, while Germany ranks #19, and Canada ranks #36.)

GitHub's announcement argues the rise of non-English, high-population regions "is notable given that it is happening at the same time as the proliferation of generative AI tools, which are increasingly enabling developers to engage with code in their natural language." And they offer one more data point: GitHub's For Good First Issue is a curated list of Digital Public Goods that need contributors, connecting those projects with people who want to address a societal challenge and promote sustainable development...

Significantly, 34% of contributors to the top 10 For Good Issue projects... made their first contribution after signing up for GitHub Copilot.
There's now 518 million projects on GitHub — with a year-over-year growth of 25%...

"AI-driven 0-day detection is here," argues a new blog post from ZeroPath, makers of a GitHub app that "detects, verifies, and issues pull requests for security vulnerabilities in your code."

They write that AI-assisted security research "has been quietly advancing" since early 2023, when researchers at the DARPA and ARPA-H's Artificial Intelligence Cyber Challenge demonstrated the first practical applications of LLM-powered vulnerability detection — with new advances continuing. "Since July 2024, ZeroPath's tool has uncovered critical zero-day vulnerabilities — including remote code execution, authentication bypasses, and insecure direct object references — in popular AI platforms and open-source projects." And they ultimately identified security flaws in projects owned by Netflix, Salesforce, and Hulu by "taking a novel approach combining deep program analysis with adversarial AI agents for validation. Our methodology has uncovered numerous critical vulnerabilities in production systems, including several that traditional Static Application Security Testing tools were ill-equipped to find..." TL;DR — most of these bugs are simple and could have been found with a code review from a security researcher or, in some cases, scanners. The historical issue, however, with automating the discovery of these bugs is that traditional SAST tools rely on pattern matching and predefined rules, and miss complex vulnerabilities that do not fit known patterns (i.e. business logic problems, broken authentication flaws, or non-traditional sinks such as from dependencies). They also generate a high rate of false positives.

The beauty of LLMs is that they can reduce ambiguity in most of the situations that caused scanners to be either unusable or produce few findings when mass-scanning open source repositories... To do this well, you need to combine deep program analysis with an adversarial agents that test the plausibility of vulnerabilties at each step. The solution ends up mirroring the traditional phases of a pentest — recon, analysis, exploitation (and remediation which is not mentioned in this post)...

AI-driven vulnerability detection is moving fast... What's intriguing is that many of these vulnerabilities are pretty straightforward — they could've been spotted with a solid code review or standard scanning tools. But conventional methods often miss them because they don't fit neatly into known patterns. That's where AI comes in, helping us catch issues that might slip through the cracks.
"Many vulnerabilities remain undisclosed due to ongoing remediation efforts or pending responsible disclosure processes," according to the blog post, which includes a pie chart showing the biggest categories of vulnerabilities found:

• 53%: Authorization flaws, including roken access control in API endpoints and unauthorized Redis access and configuration exposure. ("Impact: Unauthorized access, data leakage, and resource manipulation across tenant boundaries.")

• 26%: File operation issues, including directory traversal in configuration loading and unsafe file handling in upload features. ("Impact: Unauthorized file access, sensitive data exposure, and potential system compromise.")

• 16%: Code execution vulnerabilities, including command injection in file processing and unsanitized input in system commands. ("Impact: Remote code execution, system command execution, and potential full system compromise.")

The company's CIO/cofounder was "former Red Team at Tesla," according to the startup's profile at YCombinator, and earned over $100,000 as a bug-bounty hunter. (And another co-founded is a former Google security engineer.)

Thanks to Slashdot reader Mirnotoriety for sharing the article.

Google is advancing the release timeline for Android 16, shifting it to the second quarter of 2025 to better align with new device launches and accelerate access to its latest AI and machine learning resources. It should also "enable app creators and phone companies to prepare their products for the new software more quickly," reports CNET. From the report: [I]n a big-picture sense, the change could help facilitate a new wave of apps with more AI integration, considering developers will get access to Google's latest machine learning and AI resources even sooner. "We're in a once-in-a-generation moment to completely reimagine what our smartphones can do and how we interact with them," Google's Seang Chau, who took on the role of vice president and general manager of the Android Platform earlier this year, said in an interview with CNET. "It's a really exciting time for smartphones, and we've been putting a lot of thought into what we want to do next with them."

In addition to moving up the major release, Google will roll out a minor update in the fourth quarter of 2025 with feature updates, optimizations and bug fixes. It's a notable switch from Google's usual release timeline, but it's just one of several changes the company has made to the way it distributes Android updates in an effort to add features more frequently. [...] "Things are moving quite fast in the AI world right now," Chau said. "So we want to make sure that we get those developer [application programming interfaces], especially around machine learning and AI, available to our developers so they can build these capabilities faster and get them out to our users faster."

GitHub Copilot will switch from using exclusively OpenAI's GPT models to a multi-model approach, adding Anthropic's Claude 3.5 Sonnet and Google's Gemini 1.5 Pro. Ars Technica reports: First, Anthropic's Claude 3.5 Sonnet will roll out to Copilot Chat's web and VS Code interfaces over the next few weeks. Google's Gemini 1.5 Pro will come a bit later. Additionally, GitHub will soon add support for a wider range of OpenAI models, including GPT o1-preview and o1-mini, which are intended to be stronger at advanced reasoning than GPT-4, which Copilot has used until now. Developers will be able to switch between the models (even mid-conversation) to tailor the model to fit their needs -- and organizations will be able to choose which models will be usable by team members.

The new approach makes sense for users, as certain models are better at certain languages or types of tasks. "There is no one model to rule every scenario," wrote [GitHub CEO Thomas Dohmke]. "It is clear the next phase of AI code generation will not only be defined by multi-model functionality, but by multi-model choice." It starts with the web-based and VS Code Copilot Chat interfaces, but it won't stop there. "From Copilot Workspace to multi-file editing to code review, security autofix, and the CLI, we will bring multi-model choice across many of GitHub Copilot's surface areas and functions soon," Dohmke wrote. There are a handful of additional changes coming to GitHub Copilot, too, including extensions, the ability to manipulate multiple files at once from a chat with VS Code, and a preview of Xcode support. GitHub also introduced "Spark," a natural language-based app development tool that enables both non-coders and coders to create and refine applications using conversational prompts. It's currently in an early preview phase, with a waitlist available for those who are interested.

Google has integrated AI deeply across its operations, with over 25% of its new code generated by AI. CEO Sundar Pichai announced the milestone during the company's third quarter 2024 earnings call. The Verge reports: AI is helping Google make money as well. Alphabet reported $88.3 billion in revenue for the quarter, with Google Services (which includes Search) revenue of $76.5 billion, up 13 percent year-over-year, and Google Cloud (which includes its AI infrastructure products for other companies) revenue of $11.4 billion, up 35 percent year-over-year. Operating incomes were also strong. Google Services hit $30.9 billion, up from $23.9 billion last year, and Google Cloud hit $1.95 billion, significantly up from last year's $270 million. "In Search, our new AI features are expanding what people can search for and how they search for it," CEO Sundar Pichai says in a statement. "In Cloud, our AI solutions are helping drive deeper product adoption with existing customers, attract new customers and win larger deals. And YouTube's total ads and subscription revenues surpassed $50 billion over the past four quarters for the first time."

theodp writes: "Computer science education is a necessity for all students," argues tech-backed nonprofit Code.org in its newly-published 2024 State of Computer Science Education (Understanding Our National Imperative) report. "Students of all identities and chosen career paths need quality computer science education to become informed citizens and confident creators of content and digital tools."

In the 200-page report, Code.org pays special attention to participation in "foundational computer science courses" in high school. "Across the country, 60% of public high schools offer at least one foundational computer science course," laments Code.org (curiously promoting a metric that ignores school size which nonetheless was embraced by Education Week and others).

"A course that teaches foundational computer science includes a minimum amount of time applying learned concepts through programming (at least 20 hours of programming/coding for grades 9-12 high schools)," Code.org explains in a separate 13-page Defining Foundational Computer Science document. Interestingly, Code.org argues that Data and Informatics courses -- in which "students may use Oracle WebDB, SQL, PL/SQL, SPSS, and SAS" to learn "the K-12 CS Framework concepts about data and analytics" -- do not count, because "the course content focuses on querying using a scripting language rather than creating programs [the IEEE's Top Programming Languages 2024 begs to differ]." Code.org similarly dissed the use of the Wolfram Language for broad educational use back in 2016.

With its insistence on the importance of kids taking Code.org-defined 'programming' courses in K-12 to promote computational thinking, it's probably no surprise to see that the data behind the 2024 State of Computer Science Education report was prepared using Python (the IEEE's top programming language) and presented to the public in a Jupyter notebook. Just kidding. Ironically, the data behind the 2024 State of Computer Science Education analysis is prepared and presented by Code.org in a no-code Tableau workbook.

Speaking of streaming services, an anonymous reader shares a story that looks into Apple's entertainment offering: Ever since its launch in 2019, Apple TV+ has been carving out an identity as the new home for prestige shows from some of Hollywood's biggest names -- the kind of shows that sound natural coming out of Jimmy Kimmel's mouth in monologue jokes at the Emmys. While the company never provides spending details, Apple is estimated to have spent at least $20 billion recruiting the likes of Reese Witherspoon, M. Night Shayamalan, and Harrison Ford to help cultivate its award-worthy sheen. For all the effort Apple has expended, and for all the cultural excitement around Ted Lasso during its three-season run, the streaming service has won nearly 500 Emmys ... while attracting just 0.2% of total TV viewing in the U.S.

No wonder the company reportedly began reining in its spending spree recently. (Apple did not reply to a request for comment.) "It seems like Apple TV wants to be seen as a platform that's numbers-agnostic," says Ashley Ray, comedian, TV writer, and host of the erstwhile podcast TV I Say. "They wanna be known for being about the creativity and the love of making TV shows, even if nobody's watching them."

The experience of enjoying a new Apple TV+ series can often be a lonely one. Adventurous subscribers might see an in-network ad about something like last summer's Sunny, the timely, genre-bending Rashida Jones series about murderous AI, and give it a shot -- only to find that nobody else is talking about it in their social media feeds or around the company Keurig machine. Sure, the same could be said for hundreds of other streaming series in the post-monoculture era, but most streaming companies aren't consistently landing as much marquee talent for such a limited library. (Apple currently has 259 TV shows and films compared to Netflix's nearly 16,000.)

How is it possible for a streaming service to have as much high-pedigree programming as Apple TV+ does and so relatively few viewers, despite an estimated 25 million paid subscribers? How can shows starring Natalie Portman, Idris Elba, and Colin Farrell launch and even get renewed without ever quite grazing the zeitgeist? How does a show set in the same Monsterverse as Godzilla vs. Kong, and starring Kurt Russell and his roguishly charming son, not become a monster-size hit?

For many perplexed observers, the blame falls squarely on Apple's marketing efforts, or seeming lack thereof.