In January the TIOBE Index (estimating programming language popularity) declared Python their language of the year. (Though it was already #1 in their rankings, it had showed a 9.3% increase in their ranking system, notes InfoWorld.) TIOBE CEO Paul Jansen says this reflects how easy Python is to learn, adding that "The demand for new programmers is still very high" (and that "developing applications completely in AI is not possible yet.")

In fact on February's version of the index, the top ten looks mostly static. The only languages dropping appear to be very old languages. Over the last 12 months C and PHP have both fallen on the index — C from the #2 to the #4 spot, and PHP from #10 all the way to #14. (Also dropping is Visual Basic, which fell from #9 to #10.)

But TechRepublican cites another factor that seems to be affecting the rankings: language speed. Fast programming languages are gaining popularity, TIOBE CEO Paul Jansen said in the TIOBE Programming Community Index in February. Fast programming languages he called out include C++ [#2], Go [#8], and Rust [#13 — up from #18 a year ago].

Also, according to the updated TIOBE rankings...

- C++ held onto its place at second from the top of the leaderboard.
- Mojo and Zig are following trajectories likely to bring them into the top 50, and reached #51 and #56 respectively in February.

"Now that the world needs to crunch more and more numbers per second, and hardware is not evolving fast enough, speed of programs is getting important. Having said this, it is not surprising that the fast programming languages are gaining ground in the TIOBE index," Jansen wrote. The need for speed helped Mojo [#51] and Zig [#56] rise...

Rust reached its all-time high in the proprietary points system (1.47%.), and Jansen expects Go to be a common sight in the top 10 going forward.

In 2022 Google released a tool to easily scan for vulnerabilities in dependencies named OSV-Scanner. "Together with the open source community, we've continued to build this tool, adding remediation features," according to Google's security blog, "as well as expanding ecosystem support to 11 programming languages and 20 package manager formats... Users looking for an out-of-the-box vulnerability scanning CLI tool should check out OSV-Scanner, which already provides comprehensive language package scanning capabilities..."

Thursday they also announced an extensible library for "software composition analysis" scanning (as well as file-system scanning) named OSV-SCALIBR (Open Source Vulnerability — Software Composition Analysis LIBRary). The new library "combines Google's internal vulnerability management expertise into one scanning library with significant new capabilities such as:

• Software composition analysis for installed packages, standalone binaries, as well as source code

• OSes package scanning on Linux (COS, Debian, Ubuntu, RHEL, and much more), Windows, and Mac

• Artifact and lockfile scanning in major language ecosystems (Go, Java, Javascript, Python, Ruby, and much more)

• Vulnerability scanning tools such as weak credential detectors for Linux, Windows, and Mac

• Software Bill of Materials (SBOM) generation in SPDX and CycloneDX, the two most popular document formats

• Optimization for on-host scanning of resource constrained environments where performance and low resource consumption is critical

"OSV-SCALIBR is now the primary software composition analysis engine used within Google for live hosts, code repos, and containers. It's been used and tested extensively across many different products and internal tools to help generate SBOMs, find vulnerabilities, and help protect our users' data at Google scale. We offer OSV-SCALIBR primarily as an open source Go library today, and we're working on adding its new capabilities into OSV-Scanner as the primary CLI interface."

Nvidia will begin selling a personal AI supercomputer in May that can run sophisticated AI models with up to 200 billion parameters, the chipmaker has announced. The $3,000 Project Digits system is powered by the new GB10 Grace Blackwell Superchip and can operate from a standard power outlet.

The device delivers 1 petaflop of AI performance and includes 128GB of memory and up to 4TB of storage. Two units can be linked to handle models with 405 billion parameters. "AI will be mainstream in every application for every industry," Nvidia CEO Jensen Huang said. The system runs on Linux-based Nvidia DGX OS and supports PyTorch, Python, and Jupyter notebooks.

Long-time Slashdot reader theodp writes: In an Op-ed for The Huntington News, fourth year Northeastern University CS student Derek Kaplan argues that real pedagogical merit is what should count when deciding which language to use to teach CS fundamentals (aka 'Fundies'). He makes the case for Northeastern to reconsider its decision to move from Racket to Python and Java later this year in an overhaul of its first-year curriculum.

"Students will get extensive training in Python, which is currently the most requested language by co-op employers," Northeastern explains (some two decades after a Slashdot commenter made the same Hot Languages = Jobs observation in a spirited 2001 debate on Java as a CS introductory language)...

"I have often heard computer science students complain that Fundies 1 teaches Racket instead of a 'useful language' like Python," Kaplan writes. "But the point of Fundies is not to teach Racket — it is to teach program design skills that can be applied using any programming language. Racket is just the tool it uses to do so. A student who does well in Fundies will have no difficulty applying the same skills to Python or any other language. And with how fast the tech industry changes, is it really worth having a course that teaches just Python when tomorrow, some other language might dominate the industry? Our current curriculum focuses on timeless principles rather than fleeting trends."

Also expressing concerns about the selection of suitable languages for novice programming is King's College CS Prof Michael Kölling, who explains, "One of the drivers is the perceived usefulness of the language in a real-world context. Students (and their parents) often have opinions which language is 'better' to learn. In forming these opinions, the definition of 'better' can often be vague and driven by limited insight. One strong aspect commonly cited is the perceived usefulness of a language in the 'real world.' If a language is widely used in industry, it is more likely to be seen as a useful language to learn." Kölling's recommendation? "We need a new language for teaching novices at secondary school and introductory university level," Kölling concludes. "This language should be designed explicitly for teaching [...] Maintenance and adaptation of this language should be driven by pedagogical considerations, not by industry needs."

While noble in intent, one suspects Kaplan and Kölling may be on a quixotic quest in a money wins world, outgunned by the demands, resources, and influence of tech giants like Amazon — the top employer of Northeastern MSCS program grads — who pushed back against NSF advice to deemphasize Java in high school CS and dropped $15 million to have tech-backed nonprofit Code.org develop and push a new Java-based, powered-by-AWS CS curriculum into high schools with the support of a consortium of politicians, educators, and tech companies. Echoing Northeastern, an Amazon press release argued the new Java-based curriculum "best prepares students for the next step in their education and careers."

"Over the course of 2024, Python has proven again and again why it's one of the most popular, useful, and promising programming languages out there," writes InfoWorld: The latest version of the language pushes the envelope further for speed and power, sheds many of Python's most decrepit elements, and broadens its appeal with developers worldwide. Here's a look back at the year in Python.

In the biggest news of the year, the core Python development team took a major step toward overcoming one of Python's longstanding drawbacks: the Global Interpreter Lock or "GIL," a mechanism for managing interpreter state. The GIL prevents data corruption across threads in Python programs, but it comes at the cost of making threads nearly useless for CPU-bound work. Over the years, various attempts to remove the GIL ended in tears, as they made single-threaded Python programs drastically slower. But the most recent no-GIL project goes a long way toward fixing that issue — enough that it's been made available for regular users to try out.

The no-GIL or "free-threaded" builds are still considered experimental, so they shouldn't be deployed in production yet. The Python team wants to alleviate as much of the single-threaded performance impact as possible, along with any other concerns, before giving the no-GIL builds the full green light. It's also entirely possible these builds may never make it to full-blown production-ready status, but the early signs are encouraging.

Another forward-looking feature introduced in Python 3.13 is the experimental just-in-time compiler or JIT. It expands on previous efforts to speed up the interpreter by generating machine code for certain operations at runtime. Right now, the speedup doesn't amount to much (maybe 5% for most programs), but future versions of Python will expand the JIT's functionality where it yields real-world payoffs.
Python is now more widely used than JavaScript on GitHub (thanks partly to its role in AI and data science code).

Google has announced an experimental AI-powered code agent called "Jules" that can automatically fix coding errors for developers. From a report: Jules was introduced today alongside Gemini 2.0, and uses the updated Google AI model to create multi-step plans to address issues, modify multiple files, and prepare pull requests for Python and Javascript coding tasks in GitHub workflows.

Microsoft introduced a similar experience for GitHub Copilot last year that can recognize and explain code, alongside recommending changes and fixing bugs. Jules will compete against Microsoft's offering, and also against tools like Cursor and even Claude and ChatGPT's coding abilities. Google's launch of a coding-focused AI assistant is no surprise -- CEO Sundar Pichai said in October that more than a quarter of all new code at the company is now generated by AI.

"Jules handles bug fixes and other time-consuming tasks while you focus on what you actually want to build," Google says in its blog post. "This effort is part of our long-term goal of building AI agents that are helpful in all domains, including coding."

An anonymous reader shares a report: Software vulnerability submissions generated by AI models have ushered in a "new era of slop security reports for open source" -- and the devs maintaining these projects wish bug hunters would rely less on results produced by machine learning assistants. Seth Larson, security developer-in-residence at the Python Software Foundation, raised the issue in a blog post last week, urging those reporting bugs not to use AI systems for bug hunting.

"Recently I've noticed an uptick in extremely low-quality, spammy, and LLM-hallucinated security reports to open source projects," he wrote, pointing to similar findings from the Curl project in January. "These reports appear at first glance to be potentially legitimate and thus require time to refute." Larson argued that low-quality reports should be treated as if they're malicious.

As if to underscore the persistence of these concerns, a Curl project bug report posted on December 8 shows that nearly a year after maintainer Daniel Stenberg raised the issue, he's still confronted by "AI slop" -- and wasting his time arguing with a bug submitter who may be partially or entirely automated.

"Generative AI is transforming software development by enabling natural language prompts to generate code, reducing the need for traditional programming skills," argues Analytics India magazine. Traditionally, coding was the bastion of the select few who had mastered mighty languages like C++, Python, or Java. The idea of programming seemed exclusively reserved for those fluent in syntax and logic. However, the narrative is now being challenged by natural language coding being implemented in AI tools like GitHub Copilot. Andrej Karpathy, senior director of AI at Tesla predicted this trend last year.... English is emerging as the universal coding language.

NVIDIA CEO Jensen Huang believes that English is becoming a new programming language thanks to AI advancements. Speaking at the World Government Summit, Huang explained, "It is our job to create computing technology such that nobody has to program and that the programming language is human"... He calls this a "miracle of AI," emphasising how it closes the technology divide and empowers people from all fields to become effective technologists without traditional coding skills... "In the future, you will tell the computer what you want, and it will do it,"â Huang commented. Large language models (LLMs) like OpenAI's GPT-4 and its successors have made this possible...

Microsoft CEO Satya Nadella has been equally vocal about the potential of English for coding. Microsoft's GitHub Copilot, an AI code assistant, enables developers to describe their needs in natural language and receive functional code in response. Nadella describes this as part of a broader mission to "empower every person and every organisation on the planet to achieve more".... In a discussion earlier last year, Stability AI CEO Emad Mostaque claimed, "41% of codes on GitHub are AI-generated"...

In 2024, the ability to program is no longer reserved for a few. It's a skill anyone can wield, thanks to the power of natural language processing and AI
"No longer is the power to create software restricted to those who can decipher programming languages," the article concludes. "Anyone with a problem to solve and a clear enough articulation of that problem can now write software."

Although the article also includes this consoling quote from Nvidia's Huang in March. "There is an artistry to prompt engineering. It's how you fine-tune the instructions to get exactly what you want"

Microsoft-owned GitHub published a blog post asking "Does GitHub Copilot improve code quality? Here's what the data says."

Its first paragraph includes statistics from past studies — that GitHub Copilot has helped developers code up to 55% faster, leaving 88% of developers feeling more "in the flow" and 85% feeling more confident in their code.

But does it improve code quality? [W]e recruited 202 [Python] developers with at least five years of experience. Half were randomly assigned GitHub Copilot access and the other half were instructed not to use any AI tools... We then evaluated the code with unit tests and with an expert review conducted by developers.

Our findings overall show that code authored with GitHub Copilot has increased functionality and improved readability, is of better quality, and receives higher approval rates... Developers with GitHub Copilot access had a 56% greater likelihood of passing all 10 unit tests in the study, indicating that GitHub Copilot helps developers write more functional code by a wide margin. In blind reviews, code written with GitHub Copilot had significantly fewer code readability errors, allowing developers to write 13.6% more lines of code, on average, without encountering readability problems. Readability improved by 3.62%, reliability by 2.94%, maintainability by 2.47%, and conciseness by 4.16%. All numbers were statistically significant... Developers were 5% more likely to approve code written with GitHub Copilot, meaning that such code is ready to be merged sooner, speeding up the time to fix bugs or deploy new features.
"While GitHub's reports have been positive, a few others haven't," reports Visual Studio magazine: For example, a recent study from Uplevel Data Labs said, "Developers with Copilot access saw a significantly higher bug rate while their issue throughput remained consistent."

And earlier this year a "Coding on Copilot" whitepaper from GitClear said, "We find disconcerting trends for maintainability. Code churn — the percentage of lines that are reverted or updated less than two weeks after being authored — is projected to double in 2024 compared to its 2021, pre-AI baseline. We further find that the percentage of 'added code' and 'copy/pasted code' is increasing in proportion to 'updated,' 'deleted,' and 'moved 'code. In this regard, AI-generated code resembles an itinerant contributor, prone to violate the DRY-ness [don't repeat yourself] of the repos visited."

Five local privilege escalation (LPE) vulnerabilities in the Linux utility "needrestart" -- widely used on Ubuntu to manage service updates -- allow attackers with local access to escalate privileges to root. The flaws were discovered by Qualys in needrestart version 0.8, and fixed in version 3.8. BleepingComputer reports: Complete information about the flaws was made available in a separate text file, but a summary can be found below:

- CVE-2024-48990: Needrestart executes the Python interpreter with a PYTHONPATH environment variable extracted from running processes. If a local attacker controls this variable, they can execute arbitrary code as root during Python initialization by planting a malicious shared library.
- CVE-2024-48992: The Ruby interpreter used by needrestart is vulnerable when processing an attacker-controlled RUBYLIB environment variable. This allows local attackers to execute arbitrary Ruby code as root by injecting malicious libraries into the process.
- CVE-2024-48991: A race condition in needrestart allows a local attacker to replace the Python interpreter binary being validated with a malicious executable. By timing the replacement carefully, they can trick needrestart into running their code as root.
- CVE-2024-10224: Perl's ScanDeps module, used by needrestart, improperly handles filenames provided by the attacker. An attacker can craft filenames resembling shell commands (e.g., command|) to execute arbitrary commands as root when the file is opened.
- CVE-2024-11003: Needrestart's reliance on Perl's ScanDeps module exposes it to vulnerabilities in ScanDeps itself, where insecure use of eval() functions can lead to arbitrary code execution when processing attacker-controlled input. The report notes that attackers would need to have local access to the operation system through malware or a compromised account in order to exploit these flaws. "Apart from upgrading to version 3.8 or later, which includes patches for all the identified vulnerabilities, it is recommended to modify the needrestart.conf file to disable the interpreter scanning feature, which prevents the vulnerabilities from being exploited," adds BleepingComputer.

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

Francois Chollet, an AI pioneer and creator of the Keras framework, announced that he's leaving Google to co-found a new company. Neowin reports: In his parting message, Chollet assured that he would still be active with Keras and participate in its development on GitHub. His successor, Jeff Carpenter, will now lead Keras at Google, and Chollet expressed his full confidence in the team's future direction.

Keras has come a long way since Chollet released it in 2015, initially as a high-level neural network API meant for simplicity and accessibility. Keras quickly gained traction in the AI community for its user-friendly Python interface and compatibility with frameworks like TensorFlow, simplifying machine learning model building for developers across various levels. Google published a blog post praising Chollet and reaffirming their commitment to Keras.

Last year, Google lost the "Godfather of AI," Geoffrey Hinton, who left the company after nearly a decade. He said he quit his job at Google so he can freely speak out about the risks of AI.

spatwei writes: Google has used a large language model (LLM) agent called "Big Sleep" to discover a previously unknown, exploitable memory flaw in a widely used software for the first time, the company announced Friday.

The stack buffer underflow vulnerability in a development version of the popular open-source database engine SQLite was found through variant analysis by Big Sleep, which is a collaboration between Google Project Zero and Google DeepMind.

Big Sleep is an evolution of Project Zero's Naptime project, which is a framework announced in June that enables LLMs to autonomously perform basic vulnerability research. The framework provides LLMs with tools to test software for potential flaws in a human-like workflow, including a code browser, debugger, reporter tool and sandbox environment for running Python scripts and recording outputs.

The researchers provided the Gemini 1.5 Pro-driven AI agent with the starting point of a previous SQLIte vulnerability, providing context for Big Sleep to search for potential similar vulnerabilities in newer versions of the software. The agent was presented with recent commit messages and diff changes and asked to review the SQLite repository for unresolved issues.

Google's Big Sleep ultimately identified a flaw involving the function "seriesBestIndex" mishandling the use of the special sentinel value -1 in the iColumn field. Since this field would typically be non-negative, all code that interacts with this field must be designed to handle this unique case properly, which seriesBestIndex fails to do, leading to a stack buffer underflow.

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%...

An anonymous reader quotes a report from InfoWorld: Select developers now are getting free access to JetBrains' WebStorm and Rider IDEs. The company on October 24 announced it has launched non-commercial licenses for its WebStorm JavaScript and TypeScript IDE and the Rider cross-platform .NET and game development IDE. As of now, developers using these IDEs for non-commercial purposes, such as open source project development or content creation, can use them for free. JetBrains views the move as expanding the availability of these IDEs to a broader swath of developer roles. More than two-thirds of developers code outside of work as a hobby and nearly 40% code for educational and learning purposes outside of work, the company said."Previously this year, JetBrains released other products under the same terms for non-commercial use, including RustRover, an IDE for Rust development, and Aqua, an IDE designed for test automation," notes InfoWorld. "JetBrains also provides community editions of IntelliJ and PyCharm, IDEs for Java and Python, respectively, which can be used to build proprietary and commercial software."

JetBrains has an FAQ section with additional details about the change.

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.

Though native Linux game servers have been scarce over the last two decades, "I've seen people using the Box64 emulator to play x86_64 games on ARM devices," writes Slashdot reader VennStone. "It got me thinking: why not apply this to game servers...?

"I thought it would be fun to see if I could build a super low-power Trackmania 2 server using a Raspberry Pi Zero 2 W."

They dubbed the experiment "Trackberry", and shared all the technical details in a blog post at Interfacing Linux (includinga video). For example, they installed PyEnv so it could create a virtual environment for the PyPlanet server controller. ("That's right, your little Pi Zero 2 W is about to compile some software, slowly....")

But ultimately "it turns out that the A53 can run not only the server but also the server controller, with minimal effort. Five players push one core to around 50% load, while the others handle the database and controller." WHY STOP THERE? There are a gang of x86 Linux servers that could potentially run with Box64. Imagine playing Pirraria, 7 Days to Pi, Counter-Pi 2, Pitorio, and countless others! Granted, you may need a more powerful device than a Raspberry Pi Zero 2 W. I'll leave that research up to you.

My main takeaway from this experiment? Box64 is straight-up Scandinavian witchcraft and is not to be trifled with. Not even a little bit.

That said, it introduces a compelling option for those of us looking to run dedicated game servers that don't require much in the way of system resources. Under load, TrackBerry averages 2.8 watts and, according to the scientific number digits below, ends up running just under $3.00 a year or $0.25 a month. I find the concept of having a stack of microSD cards, each holding a different game server, neat....

You can see TrackBerry in action every Tuesday and Friday on Twitch...

Last month C dropped from 3rd to 4th in TIOBE's ranking of programming language popularity (which tries to calculate each language's share of search engine results). Java moved up into the #3 position in September, reports TechRepublic, which notes that by comparison October "saw relatively little change" — though percentages of search results increased slightly. "At number one, Python jumped from 20.17% in September to 21.9% in October. In second place, C++ rose from 10.75% in September to 11.6%. In third, Java ascended from 9.45% to 10.51%..."

Is there a larger trend? TIOBE CEO Paul Jansen writes that the need to harvest more data increases demand for fast data manipulation languages. But they also need to be easy to learn ("because the resource pool of skilled software engineers is drying up") and secure ("because of continuous cyber threats.") King of all, Python, is easy to learn and secure, but not fast. Hence, engineers are frantically looking for fast alternatives for Python. C++ is an obvious candidate, but it is considered "not secure" because of its explicit memory management. Rust is another candidate, although not easy to learn. Rust is, thanks to its emphasis on security and speed, making its way to the TIOBE index top 10 now. [It's #13 — up from #20 a year ago]

The cry for fast, data crunching languages is also visible elsewhere in the TIOBE index. The language Mojo [a faster superset of Python designed for accelerated hardware like GPUs]... enters the top 50 for the first time. The fact that this language is only 1 year old and already showing up, makes it a very promising language.
In the last 12 months three languages also fell from the top ten:

• PHP (dropping from #8 to #15)

• SQL (dropping from #9 to #11)

• Assembly language (dropping from #10 to #16)

This week (MIT-licensed) WebAssembly runtime Wasmer announced "a major milestone in making any software run with WebAssembly."

The announcement's headline? Running Clang in the browser using WebAssembly... Thanks to the newest release of Wasmer (4.4) and the Wasmer JS SDK (0.8.0) you can now run [compiler front-end] clang anywhere Wasmer runs! This allows compiling C programs from virtually anywhere. Including Javascript and your preferred browser! (we tested Chrome, Safari and Firefox and everything is working like a charm)...

- You can compile C code to WebAssembly easily just using the Wasmer CLI: no toolchains or complex installations needed, install Wasmer and you are ready to go...!

- You can compile C projects directly from JavaScript...!

- We expect online IDEs to start adopting the SDK to allow their users compile and run C programs in the browser....

Do you want to use clang in your Javascript project? Thanks to our newly released Wasmer JS SDK you can do it easily, in both the browser and Node.js/Bun etc... Wasmer's clang can even optimize the file for you automatically using wasm-opt under the hood (Clang automatically detects if wasm-opt is used, and it will be automatically called when optimizing the file). Imagine using Emscripten without needing its toolchain installed — or even better, imagine running Emscripten in the browser.
The announcement looks to a future of compiling native Python libraries, when "any project depending on LLVM can now be easily compiled to WebAssembly..."

"This is the beginning of an awesome journey, we can't wait to see what you create next with this."

TechCrunch's Paul Sawers reports: Fledgling fintech startup OpenBB is revealing the next step in its plans to take on the heavyweights of the investment research world. The company is launching a new, free version of a product that will open its arsenal of data and financial tooling to more users. OpenBB is the handiwork of software engineer Didier Lopes, who launched the Python-based platform back in 2021 as a way for amateur investors and enthusiasts to do investment research using different datasets for free, via a command line interface (CLI). The company went on to raise $8.5 million in seed funding from OSS Capital and angel investors such as Ram Shriram, an early backer of Google. While the community-based, open source project has amassed some 50,000 users, OpenBB has also been building an enterprise incarnation called Terminal Pro. This paid version gives teams access to an interface, pre-built database integrations, an Excel add-in, and various security and support bolt-ons that would appeal to larger businesses. [...]

The all-new OpenBB Terminal -- not to be confused with the previous CLI-based OpenBB Terminal that the startup sunsetted in March -- is a full-fledged web app, though it strips out many of the premium features of Terminal Pro. It's fully customizable, can run on any operating system or platform, and provides access to an AI-enabled OpenBB copilot. Like the previous OpenBB Terminal, the all-new web app is also free to use. OpenBB Terminal is perhaps something of a middle ground between the CLI centricity of the open source project and the bells-and-whistles feature set of the enterprise product.

The OpenBB Terminal serves as a single end point for accessing financial information from some 100 data sources, spanning equity, options, forex, the macro economy, and more. Users can also throw all their new data into the mix -- the community has previously contributed financial datasets such as historical currency exchange rates and crypto pricing data. There are also a slew of extensions and toolkits to bring more functionality to OpenBB -- such as an AI stock analysis agent. Users are free to incorporate their own AI systems and large language models (LLMs), which might be particularly important for security and compliance use cases. But with the OpenBB Copilot, categorized as a "compound AI system," users can run natural-language queries about their data out of the box. While OpenBB has been likened to an "open-source Bloomberg," TechCrunch notes that it's not a direct competitor due to Bloomberg's massive data resources and built-in chat functionality. OpenBB, however, offers flexibility with its open-source platform and customization options.

OpenBB filed for a trademark, but Bloomberg has requested an extension to potentially oppose it, despite the company asserting there's no link between OpenBB and Bloomberg's abbreviation "BBG". Lopes says the name originates from BlackBerry stock, where the founders had lost money during the meme stock craze.