Uplevel provides insights from coding and collaboration data, according to a recent report from CIO magazine — and recently they measured "the time to merge code into a repository [and] the number of pull requests merged" for about 800 developers over a three-month period (comparing the statistics to the previous three months).

Their study "found no significant improvements for developers" using Microsoft's AI-powered coding assistant tool Copilot, according to the article (shared by Slashdot reader snydeq): Use of GitHub Copilot also introduced 41% more bugs, according to the study...

In addition to measuring productivity, the Uplevel study looked at factors in developer burnout, and it found that GitHub Copilot hasn't helped there, either. The amount of working time spent outside of standard hours decreased for both the control group and the test group using the coding tool, but it decreased more when the developers weren't using Copilot.
An Uplevel product manager/data analyst acknowledged to the magazine that there may be other ways to measure developer productivity — but they still consider their metrics solid. "We heard that people are ending up being more reviewers for this code than in the past... You just have to keep a close eye on what is being generated; does it do the thing that you're expecting it to do?"

The article also quotes the CEO of software development firm Gehtsoft, who says they didn't see major productivity gains from LLM-based coding assistants — but did see them introducing errors into code. With different prompts generating different code sections, "It becomes increasingly more challenging to understand and debug the AI-generated code, and troubleshooting becomes so resource-intensive that it is easier to rewrite the code from scratch than fix it."

On the other hand, cloud services provider Innovative Solutions saw significant productivity gains from coding assistants like Claude Dev and GitHub Copilot. And Slashdot reader destined2fail1990 says that while large/complex code bases may not see big gains, "I have seen a notable increase in productivity from using Cursor, the AI powered IDE." Yes, you have to review all the code that it generates, why wouldn't you? But often times it just works. It removes the tedious tasks like querying databases, writing model code, writing forms and processing forms, and a lot more. Some forms can have hundreds of fields and processing those fields along with doing checks for valid input is time consuming, but can be automated effectively using AI.
This prompted an interesting discussion on the original story submission. Slashdot reader bleedingobvious responded: Cursor/Claude are great BUT the code produced is almost never great quality. Even given these tools, the junior/intern teams still cannot outpace the senior devs. Great for learning, maybe, but the productivity angle not quite there.... yet.

It's damned close, though. GIve it 3-6 months.
And Slashdot reader abEeyore posted: I suspect that the results are quite a bit more nuanced than that. I expect that it is, even outside of the mentioned code review, a shift in where and how the time is spent, and not necessarily in how much time is spent.
Agree? Disagree? Share your own experiences in the comments.

And are developers really saving time with AI coding assistants?

Bryan Choi, an associate professor of law and computer science focusing on software safety, proposes shifting AI liability onto the builders of the systems: To date, most popular approaches to AI safety and accountability have focused on the technological characteristics and risks of AI systems, while averting attention from the workers behind the curtain responsible for designing, implementing, testing, and maintaining such systems...

I have previously argued that a negligence-based approach is needed because it directs legal scrutiny on the actual persons responsible for creating and managing AI systems. A step in that direction is found in California's AI safety bill, which specifies that AI developers shall articulate and implement protocols that embody the "developer's duty to take reasonable care to avoid producing a covered model or covered model derivative that poses an unreasonable risk of causing or materially enabling a critical harm" (emphasis added). Although tech leaders have opposed California's bill, courts don't need to wait for legislation to allow negligence claims against AI developers. But how would negligence work in the AI context, and what downstream effects should AI developers anticipate?
The article suggest two possibilities. Classifying AI developers as ordinary employees leaves employers then sharing liability for negligent acts (giving them "strong incentives to obtain liability insurance policies and to defend their employees against legal claims.") But AI developers could also be treated as practicing professionals (like physicians and attorneys). "{In this regime, each AI professional would likely need to obtain their own individual or group malpractice insurance policies." AI is a field that perhaps uniquely seeks to obscure its human elements in order to magnify its technical wizardry. The virtue of the negligence-based approach is that it centers legal scrutiny back on the conduct of the people who build and hype the technology. To be sure, negligence is limited in key ways and should not be viewed as a complete answer to AI governance. But fault should be the default and the starting point from which all conversations about AI accountability and AI safety begin.
Thanks to long-time Slashdot reader david.emery for sharing the article.

An anonymous reader quotes a report from Ars Technica, written by John Timmer: [P]art of the software stack that companies are developing to control their quantum hardware includes software that converts abstract representations of quantum algorithms into the series of commands needed to execute them. IBM's version of this software is called Qiskit (although it was made open source and has since been adopted by other companies). Recently, IBM made a couple of announcements regarding Qiskit, both benchmarking it in comparison to other software stacks and opening it up to third-party modules. [...] Right now, the company is supporting six third-party Qiskit functions that break down into two categories.

The first can be used as stand-alone applications and are focused on providing solutions to problems for users who have no expertise programming quantum computers. One calculates the ground-state energy of molecules, and the second performs optimizations. But the remainder are focused on letting users get more out of existing quantum hardware, which tends to be error prone. But some errors occur more often than others. These errors can be due to specific quirks of individual hardware qubits or simply because some specific operations are more error prone than others. These can be handled in two different ways. One is to design the circuit being executed to avoid the situations that are most likely to produce an error. The second is to examine the final state of the algorithm to assess whether errors likely occurred and adjust to compensate for any. And third parties are providing software that can handle both of these.

One of those third parties is Q-CTRL, and we talked to its CEO, Michael Biercuk. "We build software that is really focused on everything from the lowest level of hardware manipulation, something that we call quantum firmware, up through compilation and strategies that help users map their problem onto what has to be executed on hardware," he told Ars. (Q-CTRL is also providing the optimization tool that's part of this Qiskit update.) "We're focused on suppressing errors everywhere that they can occur inside the processor," he continued. "That means the individual gate or logic operations, but it also means the execution of the circuit. There are some errors that only occur in the whole execution of a circuit as opposed to manipulating an individual quantum device." Biercuk said Q-CTRL's techniques are hardware agnostic and have been demonstrated on machines that use very different types of qubits, like trapped ions. While the sources of error on the different hardware may be distinct, the manifestations of those problems are often quite similar, making it easier for Q-CTRL's approach to work around the problems.

Those work-arounds include things like altering the properties of the microwave pulses that perform operations on IBM's hardware, and replacing the portion of Qiskit that converts an algorithm to a series of gate operations. The software will also perform operations that suppress errors that can occur when qubits are left idle during the circuit execution. As a result of all these differences, he claimed that using Q-CTRL's software allows the execution of more complex algorithms than are possible via Qiskit's default compilation and execution. "We've shown, for instance, optimization with all 156 qubits on [an IBM] system, and importantly -- I want to emphasize this word -- successful optimization," Biercuk told Ars. "What it means is you run it and you get the right answer, as opposed to I ran it and I kind of got close."

First released on July 11th, the Firefox-based Zen browser is "taking a different approach to the user interface," according to the blog It's FOSS.

The Register says the project "reminds us strongly of Arc, a radical Chromium-based web browser... to modernize the standard web browser UI by revising some fundamental assumptions." [Arc] removes the URL bar from front and center, gets rid of the simple flat list of tabs, and so on. Zen is trying to do some similar things, but in a slightly more moderate way — and it's doing it on the basis of Mozilla's Firefox codebase... Instead of the tired old horizontal tab bar you'll see in both Firefox and Chrome, Zen implements its own tab bar... By default, this tab bar is narrow and just shows page icons — but there are some extra controls at the bottom of the sidebar, one of which expands the sidebar to show page titles too. For us, it worked better than Vivaldi's fancier sidebar.
The article concludes it's "a new effort to modernize web browsing by bringing tiling, workspaces, and so on — and it's blissfully free of Google code." One Reddit comment swooned over Zen's "extraordinary" implementation of a distraction-free "Compact Mode" (hiding things like the sidebar and top bar). And It's Foss described it as a "tranquil," browser, "written using CSS, C++, JavaScript, and a few other programming languages, with a community of over 30 people contributing to it." The layout of the interface felt quite clean to me; there were handy buttons on the top to control the webpage, manage extensions, and a menu with additional options... The split-view functionality allows you to open up two different tabs on the same screen, allowing for easy multitasking when working across different webpages... I split two tabs, but in my testing, I could split over 10+ tabs... If you have a larger monitor, then you are in for a treat...

The Zen Sidebar feature... can run web apps alongside any open tabs. This can be helpful in situations where you need to quickly access a service like a note-taking app, Wikipedia, Telegram, and others.

On the customization side of things, you will find that Zen Browser supports everything that Firefox does, be it the settings, adding new extensions/themes/plugins, etc.
The Register points out it's easy to give it a try. "Being based on Firefox means that as well as running existing extensions, it can connect to Mozilla's Sync service and pick up not just your bookmarks, but also your tabs from other instances."

And beyond all that, "There's just something satisfying about switching browsers every now and again..." argues the tech site Pocket-Lint: Zen Browser's vertical tabs layout is superb and feels much better than anything available in standard Firefox. [Firefox recently offered vertical tabs and a new sidebar experience in Nightly/Firefox Labs 131.] The tab bar can be set to automatically hide and show up whenever you hover near it, and it also contains quick access buttons to bookmarks, settings, and browsing history. The tab bar also contains a profile switcher...

One of the greatest parts of the Zen Browser is the community that has popped up around it. At its heart, Zen Browser is a community-driven project... Zen Browser themes are aesthetic and functional tweaks to the UI. While there aren't a ton available right now, the ones that are show a lot of promise for the browser's future... I've personally gotten great use out of the Super URL Bar theme, which makes your URL bar expand and become the focus of your screen while typing in it... There's a lot you can do to make Zen Browser feel nearly exactly like what you want it to feel like.
The "Business Standard calls it "an open-source alternative to Chromium-based browsers," adding "Where Zen truly shines is it offers a range of customisation, tab management, and workspace management..." Their theme store offers a range of options, including modifications to the bookmark toolbar, a floating URL bar, private mode theming, and removal of browser padding. In addition to these, users can also choose from custom colour schemes and built-in theming options... The Sidebar is another neat feature which allows you to open tabs in a smaller, smartphone-sized window. You can view websites in mobile layout by using this panel.
It's "focused on being always at the latest version of Firefox," according to its official site, noting that Firefox is known for its security features. But then, "We also have additional security features like https only built into Zen Browser to help keep you safe online." And it also promises automated Releases "to ensure security."

It's FOSS adds that you can get Zen Browser for Linux, Windows, and macOS from its official website (adding "They also offer it on the Flathub store for further accessibility on Linux.")

And its source code is available on GitHub.

The JavaScript runtime Bun is a Node.js/Deno alternative (that's also a bundler/test runner/package manager).

And Bun 1.1.28 now includes experimental support for ">compiling and running native C from JavaScript, according to this report from The New Stack: "From compression to cryptography to networking to the web browser you're reading this on, the world runs on C," wrote Jarred Sumner, creator of Bun. "If it's not written in C, it speaks the C ABI (C++, Rust, Zig, etc.) and is available as a C library. C and the C ABI are the past, present, and future of systems programming." This is a low-boilerplate way to use C libraries and system libraries from JavaScript, he said, adding that this feature allows the same project that runs JavaScript to also run C without a separate build step... "It's good for glue code that binds C or C-like libraries to JavaScript. Sometimes, you want to use a C library or system API from JavaScript, and that library was never meant to be used from JavaScript," Sumner added.

It's currently possible to achieve this by compiling to WebAssembly or writing a N-API (napi) addon or V8 C++ API library addon, the team explained. But both are suboptimal... WebAssembly can do this but its isolated memory model comes with serious tradeoffs, the team wrote, including an inability to make system calls and a requirement to clone everything. "Modern processors support about 280 TB of addressable memory (48 bits). WebAssembly is 32-bit and can only access its own memory," Sumner wrote. "That means by default, passing strings and binary data JavaScript WebAssembly must clone every time. For many projects, this negates any performance gain from leveraging WebAssembly."

The latest version of Bun, released Friday, builds on this by adding N-API (nap) support to cc [Bun's C compiler, which uses TinyCC to compile the C code]. "This makes it easier to return JavaScript strings, objects, arrays and other non-primitive values from C code," wrote Sumner. "You can continue to use types like int, float, double to send & receive primitive values from C code, but now you can also use N-API types! Also, this works when using dlopen to load shared libraries with bun:ffi (such as Rust or C++ libraries with C ABI exports)....

"TinyCC compiles to decently performant C, but it won't do advanced optimizations that Clang or GCC does like autovectorization or very specialized CPU instructions," Sumner wrote. "You probably won't get much of a performance gain from micro-optimizing small parts of your codebase through C, but happy to be proven wrong!"

Software developers who ship buggy, insecure code are the true baddies in the cyber crime story, Jen Easterly, boss of the US government's Cybersecurity and Infrastructure Security Agency, has argued. From a report: "The truth is: Technology vendors are the characters who are building problems" into their products, which then "open the doors for villains to attack their victims," declared Easterly during a Wednesday keynote address at Mandiant's mWise conference. Easterly also implored the audience to stop "glamorizing" crime gangs with fancy poetic names. How about "Scrawny Nuisance" or "Evil Ferret," Easterly suggested.

Even calling security holes "software vulnerabilities" is too lenient, she added. This phrase "really diffuses responsibility. We should call them 'product defects,'" Easterly said. And instead of automatically blaming victims for failing to patch their products quickly enough, "why don't we ask: Why does software require so many urgent patches? The truth is: We need to demand more of technology vendors."

Longtime Slashdot reader theodp writes: Python in Excel is now generally available for Windows users of Microsoft 365 Business and Enterprise," Microsoft announced in a Monday blog post. "Last August, in partnership with Anaconda, we introduced an exciting new addition to Excel by integrating Python, making it possible to seamlessly combine Python and Excel analytics within the same workbook, no setup required. Since then, we've brought the power of popular Python analytics libraries such as pandas, Matplotlib, and NLTK to countless Excel users." Microsoft also announced the public preview of Copilot in Excel with Python, which will take users' natural language requests for analysis and automatically generate, explain, and insert Python code into Excel spreadsheets.

While drawing criticism for limiting Python execution to locked-down Azure cloud containers, Python in Excel has also earned accolades from the likes of Python creator Guido van Rossum, now a Microsoft Distinguished Engineer, as well as Pandas creator Wes McKinney.

Left unmentioned in Monday's announcement is that Microsoft managed to convince the USPTO to issue it a patent in July 2024 on the Enhanced Integration of Spreadsheets With External Environments (alt. source), which Microsoft explains covers the "implementation of enhanced integrations of native spreadsheet environments with external resources such as-but not limited to-Python." All of which may come as a surprise to software vendors and individuals that were integrating Excel and external programming environments years before Microsoft filed its patent application in September 2022.

The Rust foundation is making "considerable progress" on a complete security audit of the Rust ecosystem, according to the coding news site I Programmer, citing a newly-released report from the nonprofit Rust foundation: The foundation is investigating the development of a Public Key Infrastructure (PKI) model for the Rust language, including the design and implementation for a PKI CA and a resilient Quorum model for the project to implement, and the report says that language updates suggested by members of the Project were nearly ready for implementation.

Following the XZ backdoor vulnerability, the Security Initiative has focused on supply chain security, including work on provenance-tracking, verifying that a given crate is actually associated with the repository it claims to be. The top 5,000 crates by download count have been checked and verified.

Threat modeling has now been completed on the Crates ecosystem. Rust Infrastructure, crates.io and the Rust Project.

Two open source security tools, Painter and Typomania, have been developed and released. Painter can be used to build a graph database of dependencies and invocations between all crates within the crates.io ecosystem, including the ability to obtain 'unsafe' statistics, better call graph pruning, and FFI boundary mapping. Typomania ports typogard to Rust, and can be used to detect potential typosquatting as a reusable library that can be adapted to any registry.
They've also tightened admin privileges for Rust's package registry, according to the article. And "In addition to the work on the Security Initiative, the Foundation has also been working on improving interoperability between Rust and C++, supported by a $1 million contribution from Google."

According to the Rust foundation's technology director, they've made "impressive technical strides and developed new strategies to reinforce the safety, security, and longevity of the Rust programming language." And the director says the new report "paints a clear picture of the impact of our technical projects like the Security Initiative, Safety-Critical Rust Consortium, infrastructure and crates.io support, Interop Initiative, and much more."

Wired published some thoughts from Hans Peter Brondmo, the former head of "Google's seven-year mission to give AI a robot body".

An anonymous reader shared this report from Axios: Building AI-powered robots that can flexibly operate in the real world is going to take much longer than Silicon Valley believes and promises, according to the former head of Google's robotics moonshot project, writing in Wired...

Everyday Robotics spent seven years and a small Google fortune developing a one-armed robot on a wheeled platform. By the time Google pulled the plug on the project in February 2023, the robots were helping clean up researchers' desks and sorting trash during the daytime; in the evening, they were improvising dances. [Google hired a professional dancer as an artist-in-residence who teamed with "a few other engineers" to build an AI algorithm trained on the dancer's choreography preferences...]

Google founder Larry Page — favored moving directly to "end to end" (e2e) learning, where you'd hand robots a general task and they'd be able to figure out how to execute it. That, Page felt, was a goal worthy of a moonshot. But it also turned out to be out of reach. "I have come to believe," Brondmo writes, "it will take many, many thousands, maybe even millions of robots doing stuff in the real world to collect enough data to train e2e models that make the robots do anything other than fairly narrow, well-defined tasks...." ["Building robots that perform useful services — like cleaning up and wiping all the tables in a restaurant, or making the beds in a hotel — will require both AI and traditional programming for a long time to come. In other words, don't expect robots to go running off outside our control, doing something they weren't programmed to do, anytime soon."]

The bottom line: So far, robot hype is outpacing robot reality. Boston Dynamics' back-flipping humanoid and quadruped bots have wowed YouTube viewers — but you wouldn't want to let them anywhere near your office or home.
It's an interesting look back. "My job: help figure out what to do with the employees and technology left over from nine robot companies that Google had acquired," Brondmo writes: Andy "the father of Android" Rubin, who had previously been in charge, had suddenly left. Larry Page and Sergey Brin kept trying to offer guidance and direction during occasional flybys in their "spare time...." I knew from firsthand experience how hard it was to build a company that, in Steve Jobs' famous words, could put a dent in the universe, and I believed that Google was the right place to make certain big bets. AI-powered robots, the ones that will live and work alongside us one day, was one such audacious bet.

Eight and a half years later — and 18 months after Google decided to discontinue its largest bet in robotics and AI — it seems as if a new robotics startup pops up every week. I am more convinced than ever that the robots need to come. Yet I have concerns that Silicon Valley, with its focus on "minimum viable products" and VCs' general aversion to investing in hardware, will be patient enough to win the global race to give AI a robot body. And much of the money that is being invested is focusing on the wrong things...

When I arrived, the lab had already hatched Waymo, Google Glass, and other science-fiction-sounding projects like flying energy windmills and stratospheric balloons that would provide internet access to the underserved... [But] in January 2023, two months after OpenAI introduced ChatGPT, Google shut down Everyday Robots, citing overall cost concerns. The robots and a small number of people eventually landed at Google DeepMind to conduct research. In spite of the high cost and the long timeline, everyone involved was shocked.
They'd tackled the problem with earnestness. ("[S]even robots working for months to learn how to pick up a rubber duckling? That wasn't going to cut it... So we built a cloud-based simulator and, in 2021, created more than 240 million robot instances in the sim.ma")

Brondmo adds this his mother had advanced Parkinson's disease, and hoped that one day robots could support her. "Our frequent conversations toward the end of her life convinced me more than ever that a future version of what we started at Everyday Robots will be coming. In fact, it can't come soon enough.

"So the question we are left to ponder becomes: How does this kind of change and future happen? I remain curious, and concerned."

Redmonk's latest programming language ranking (attempting to gauge "potential future adoption trends") has found evidence of "a landscape resistant to change." Outside of CSS moving down a spot and C++ moving up one, the Top 10 was unchanged. And even in the back half of the rankings, where languages tend to be less entrenched and movement is more common, only three languages moved at all... There are a few signs of languages following in TypeScript's footsteps and working their way up the path, both in the Top 20 and at the back end of the Top 100 as we'll discuss shortly, but they're the exception that proves the rule.

It's possible that we'll see more fluid usage of languages, and increased usage of code assistants would theoretically make that much more likely, but at this point it's a fairly static status quo. With that, some results of note:

- TypeScript (#6): technically TypeScript didn't move, as it was ranked sixth in our last run, but this is the first quarter in which is has been the sole occupant of that spot. CSS, in this case, dropped one place to seven leaving TypeScript just outside the Top 5. It will be interesting to see whether or not it has more momentum to expend or whether it's topped out for the time being.

- Kotlin (#14) / Scala (#14): both of these JVM-based languages jumped up a couple of spots — two spots in Scala's case and three for Kotlin. Scala's rise is notable because it had been on something of a downward trajectory from a one time high of 12th, and Kotlin's placement is a mild surprise because it had spent three consecutive runs not budging from 17, only to make the jump now. The tie here, meanwhile, is interesting because Scala's long history gives it an accretive advantage over Kotlin's more recent development, but in any case the combination is evidence of the continued staying power of the JVM.

- Objective C (#17): speaking of downward trajectories and the 17th placement on this list, Objective C's slide that began in mid-2018 continued and left the language with its lowest placement in these rankings to date at #17. That's still an enormously impressive achievement, of course, and there are dozens of languages that would trade their usage for Objective C's, but the direction of travel seems clear.

- Dart (#19) / Rust (#19): while once grouped with Kotlin as up and coming languages driven by differing incentives and trends, Dart and Rust have not been able to match the ascent of their counterpart with five straight quarters of no movement. That's not necessarily a negative; as with Objective C, these are still highly popular languages and communities, but it's worth questioning whether new momentum will arrive and from where, particularly because the communities are experiencing some friction in growing their usage.
It's important to remember Redmonk's methodology. "We extract language rankings from GitHub and Stack Overflow, and combine them for a ranking that attempts to reflect both code (GitHub) and discussion (Stack Overflow) traction. The idea is not to offer a statistically valid representation of current usage, but rather to correlate language discussion and usage in an effort to extract insights into potential future adoption trends."

Having said that, here's the current top ten in Redmonk's ranking:

1. JavaScript
2. Python
3. Java
4. PHP
5. C#
6. TypeScript
7. CSS
8. C++
9. Ruby
10. C

Their announcement also notes that at the other end of the list, the programming language Bicep "jumped eight spots to #78 and Zig 10 to #87. That progress pales next to Ballerina, however, which jumped from #80 to #61 this quarter. The general purpose language from WS02, thus, is added to the list of potential up and comers we're keeping an eye on."

Microsoft today announced significant strides in its Azure Quantum Cloud service, including the demonstration of logical operations using the largest number of error-corrected qubits ever achieved. This progress brings the industry closer to building reliable quantum computers capable of solving complex problems beyond the reach of classical systems, the company said.

In a significant partnership, Microsoft is collaborating with Atom Computing to integrate their neutral-atom hardware into Azure Quantum. Atom Computing has already shown promise with hardware exceeding 1,000 qubits. Key to Microsoft's advancements is the implementation of the "tesseract code" error correction scheme on Quantinuum's trapped-ion quantum hardware. This led to a 22-fold reduction in error rates, a critical step towards reliable quantum computations. Microsoft is also committed to simplifying quantum programming. Azure's Q# language will now automatically handle complex error correction, making quantum development more accessible.

The Register reports that Google "recently rewrote the firmware for protected virtual machines in its Android Virtualization Framework using the Rust programming language." And they add that Google "wants you to do the same, assuming you deal with firmware."

A post on Google's security blog by Android engineers Ivan Lozano and Dominik Maier promises to show "how to gradually introduce Rust into your existing firmware," adding "You'll see how easy it is to boost security with drop-in Rust replacements, and we'll even demonstrate how the Rust toolchain can handle specialized bare-metal targets."

This prompts the Register to quip that easy "is not a term commonly heard with regard to a programming language known for its steep learning curve." Citing the lack of high-level security mechanisms in firmware, which is often written in memory-unsafe languages such as C or C++, Lozano and Maier argue that Rust provides a way to avoid the memory safety bugs like buffer overflows and use-after-free that account for the majority of significant vulnerabilities in large codebases. "Rust provides a memory-safe alternative to C and C++ with comparable performance and code size," they note. "Additionally it supports interoperability with C with no overhead."
At one point the blog post explains that "You can replace existing C functionality by writing a thin Rust shim that translates between an existing Rust API and the C API the codebase expects." But their ultimate motivation is greater security. "Android's use of safe-by-design principles drives our adoption of memory-safe languages like Rust, making exploitation of the OS increasingly difficult with every release."

And the Register also got this quote from Lars Bergstrom, Google's director of engineering for Android Programming Languages (and chair of the Rust Foundation's board of directors). "At Google, we're increasing Rust's use across Android, Chromium, and more to reduce memory safety vulnerabilities. We're dedicated to collaborating with the Rust ecosystem to drive its adoption and provide developers with the resources and training they need to succeed.

"This work on bringing Rust to embedded and firmware addresses another critical part of the stack."

Long-time Slashdot reader theodp writes: "The Impact of AI on Computer Science Education" recounts an experiment Eric Klopfer conducted in his undergrad CS class at MIT. He divided the class into three groups and gave them a programming task to solve in the Fortran language, which none of them knew. Reminiscent of how The Three Little Pigs used straw, sticks, and bricks to build their houses with very different results, Klopfer allowed one group to use ChatGPT to solve the problem, while the second group was told to use Meta's Code Llama LLM, and the third group could only use Google. The group that used ChatGPT, predictably, solved the problem quickest, while it took the second group longer to solve it. It took the group using Google even longer, because they had to break the task down into components.

Then, the students were tested on how they solved the problem from memory, and the tables turned. The ChatGPT group "remembered nothing, and they all failed," recalled Klopfer. Meanwhile, half of the Code Llama group passed the test. The group that used Google? Every student passed.

"This is an important educational lesson," said Klopfer. "Working hard and struggling is actually an important way of learning. When you're given an answer, you're not struggling and you're not learning. And when you get more of a complex problem, it's tedious to go back to the beginning of a large language model and troubleshoot it and integrate it." In contrast, breaking the problem into components allows you to use an LLM to work on small aspects, as opposed to trying to use the model for an entire project, he says. "These skills, of how to break down the problem, are critical to learn."

GitHub Actions let developers "automate software builds and tests," writes CSO Online, "by setting up workflows that trigger when specific events are detected, such as when new code is committed to the repository."

They also "can be reused and shared with others on the GitHub Marketplace, which currently lists thousands of public Actions that developers can use instead of coding their own. Actions can also be included as dependencies inside other Actions, creating an ecosystem similar to other open-source component registries." Researchers from Orca Security recently investigated the impact typosquatting can have in the GitHub Actions ecosystem by registering 14 GitHub organizations with names that are misspellings of popular Actions owners — for example, circelci instead of circleci, actons instead of actions, google-github-actons instead of google-github-actions... One might think that developers making typos is not very common, but given the scale of GitHub — over 100 million developers with over 420 million repositories — even a statistically rare occurrence can mean thousands of potential victims. For example, the researchers found 194 workflow files calling the "action" organization instead of "actions"; moreover, 12 public repositories started referencing the researchers' fake "actons" organization within two months of setting it up.

"Although the number may not seem that high, these are only the public repositories we can search for and there could be multiple more private ones, with numbers increasing over time," the researchers wrote... Ultimately this is a low-cost high-impact attack. Having the ability to execute malicious actions against someone else's code is very powerful and can result in software supply chain attacks, with organizations and users that then consume the backdoored code being impacted as well...

Out of the 14 typosquatted organizations that Orca set up for their proof-of-concept, GitHub only suspended one over a three-month period — circelci — and that's likely because someone reported it. CircleCI is one of the most popular CI/CD platforms.
Thanks to Slashdot reader snydeq for sharing the article.

Efforts to add Rust code to the Linux kernel has suffered a setback as one of the maintainers of the Rust for Linux project has stepped down -- citing frustration with "nontechnical nonsense," according to The Register: Wedson Almeida Filho, a software engineer at Microsoft who has overseen the Rust for Linux project, announced his resignation in a message to the Linux kernel development mailing list. "I am retiring from the project," Filho declared. "After almost four years, I find myself lacking the energy and enthusiasm I once had to respond to some of the nontechnical nonsense, so it's best to leave it up to those who still have it in them."

[...] Memory safety bugs are regularly cited as the major source of serious software vulnerabilities by organizations overseeing large projects written in C and C++. So in recent years there's been a concerted push from large developers like Microsoft and Google, as well as from government entities like the US Cybersecurity and Infrastructure Security Agency, to use memory-safe programming languages -- among them Rust. Discussions about adding Rust to Linux date back to 2020 and were realized in late 2022 with the release of Linux 6.1. "I truly believe the future of kernels is with memory-safe languages," Filho's note continued. "I am no visionary but if Linux doesn't internalize this, I'm afraid some other kernel will do to it what it did to Unix."

Pundits aggregate results from multiple pollsters to minimize biases. So ZDNet tried the same approach, but aggregating rankings for the popularity of 19 top programming languages. Senior contributing editor David Gewirtz combined results from nine popularity rankings, including PYPL, the Tiobe index, GitHub's Usage 2023 summary report, and several rankings from Stack Overflow and from IEEE Spectrum.

The results? The top cluster contains Python, JavaScript, and Java. These are all very representative in the world of AI coding...

The next cluster contains the classic C-based languages [C++, C#, C], plus TypeScript (which is a more robust JavaScript variant) and SQL.

Below that are languages that were dominant a while ago, the web languages used to build and operate websites [HTML/CSS, PHP, Shell], followed by a range of other languages that are either growing in popularity (R, Dart) or dropping in popularity (Ruby). [Just above Ruby are Go, Rust, Kotlin, and Lua.]

Finally, at the bottom is Swift, Apple's language of choice. Objective-C, the previous language of Apple programming, has all but dropped off the list since Apple launched Swift. But while Apple boasts many developers, Swift is clearly not a standout in programmer interest... [T]here aren't a huge number of companies hiring Apple app developers, at least primarily. That's why Swift is relatively far down the chart. Objective-C is being replaced by Swift, and we can see it dropping right before our eyes.
"With the exception of Java, the C-family of languages still dominates," the article concludes, before adding that if you're only going to learn one language, "I'd recommend Python, Java, and JavaScript instead." But it also advises aspiring programmers to learn "multiple languages and multiple frameworks. Build things in the languages. Programming is not just an intellectual exercise. You have to actually make stuff....

"[L]earning how to learn languages is as important as learning a language — and the best way to do that is to learn more than one."

"Sometimes an artificial intelligence tool comes out of nowhere and dominates the conversation on social media," writes Tom's Guide.

"This week that app is Cursor, an AI coding tool that uses models like Claude 3.5 Sonnet and GPT-4o to make it easier than ever to build your own apps," with the ability to "write, predict and manipulate code using nothing but a text prompt." Cursor is part development environment, part AI chatbot and unlike tools like GitHub Copilot it can more or less do all of the work for you, transforming a simple idea into functional code in minutes... Built on the same system as the popular Microsoft Visual Studio Code, Cursor has already found a fanbase among novice coders and experienced engineers...

Cursor's simplicity, working from a chat window, means even someone completely new to code could get a functional app running in minutes and keep building on it to add new features... The startup has raised over $400 million since it was founded in 2022 and works with various models including those from Anthropic and OpenAI... In my view, its true power is in the democratization of coding. It would also allow someone without much coding experience to build the tools they need by typing a few lines of text.
More from ReadWrite: Cursor, an AI firm that is attempting to build a "magical tool that will one day write all the world's code," has announced it has raised $60 million in its Series A funding round... As of August 22, the company had a valuation of $400 million, according to sources cited by TechCrunch...

Anysphere is the two-year-old startup that developed the app. Its co-founders are Michael Truell, Sualeh Asif, Arvid Lunnemark and Aman Sanger, who started the company while they were students at MIT... Using advanced AI capabilities, it is said to be able to finish, correct, and change AI code through natural language commands. It currently works with JavaScript, Python, and TypeScript, and is free for most uses. The pro plan will set you back $20 per month.
But how well does it work? Tom's Guide notes that after requesting a test app, "It generated the necessary code in the sidebar chat window and all I had to do was click Apply and then Accept. This added the code to a new Python file including all the necessary imports. It also gave me instructions on how to add modules to my machine to make the code work.

"As the chat is powered by Claude 3.5 Sonnet, you can just have it explain in more detail any element of the code or any task required to make it run..."

Andreessen Horowitz explains why they invested in the company: It's very clear that LLMs are a powerful tool for programmers, and that their coding abilities will improve over time. But it's also clear that for most coding tasks, the problem to solve is not how to make LLMs perform well in isolation, but how to make them perform well alongside a human developer. We believe, therefore, the interface between programmers and AI models will soon become one of the most important pieces of the dev stack. And we're thrilled to announce our series A investment...

Cursor is a fork of VS Code that's heavily customized for AI-assisted programming. It works with all the latest LLMs and supports the full VS Code plugin ecosystem. What makes Cursor special are the features designed to integrate AI into developer workflows — including next action prediction, natural language edits, chatting with your codebase, and a bunch of new ones to come... Our belief is that Cursor, distinctly among AI coding tools, has simply gotten it right. That's why, in a little over a year, thousands of users have signed up for Cursor, including at companies like OpenAI, Midjourney, Perplexity, Replicate, Shopify, Instacart, and many others. Users give glowing reviews of the product, many of them have started to pay for it, and they rarely switch back to other IDEs. Most of the a16z Infra team have also become avid Cursor users!
One site even argues that Cursor's coding and AI capabilities "should be a wake up call for Microsoft to make VS Code integration with GitHub Copilot a lot easier."

Thanks to Slashdot reader joshuark for sharing the article.

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

Some interesting findings:

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

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

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

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

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

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

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

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

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

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

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

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

An anonymous reader quotes a report from Gizmodo: Thousands of Tennesseans were illegally denied Medicaid and other benefits due to programming and data errors in an algorithmic system the state uses to determine eligibility for low-income residents and people with disabilities, a U.S. District Court judge ruled this week. The TennCare Connect system -- built by Deloitte and other contractors for more than $400 million -- is supposed to analyze income and health information to automatically determine eligibility for benefits program applicants. But in practice, the system often doesn't load the appropriate data, assigns beneficiaries to the wrong households, and makes incorrect eligibility determinations, according to the decision (PDF) from Middle District of Tennessee Judge Waverly Crenshaw Jr.

"When an enrollee is entitled to state-administered Medicaid, it should not require luck, perseverance, and zealous lawyering for him or her to receive that healthcare coverage," Crenshaw wrote in his opinion. The decision was a result of a class action lawsuit filed in 2020 on behalf of 35 adults and children who were denied benefits. [...] ]Crenshaw found that TennCare Connect did not consider whether applicants were eligible for all available programs before it terminated their coverage. Deloitte was a major beneficiary of the nationwide modernization effort, winning contracts to build automated eligibility systems in more than 20 states, including Tennessee and Texas. Advocacy groups have asked (PDF) the Federal Trade Commission to investigate Deloitte's practices in Texas, where they say thousands of residents are similarly being inappropriately denied life-saving benefits by the company's faulty systems.

An anonymous reader quotes a report from Ars Technica: Too often, new tech product or service launches seem like solutions in search of a problem, but not this one: ESPN is launching software that lets you figure out just where you can watch the specific game you want to see amid an overcomplicated web of streaming services, cable channels, and arcane licensing agreements. Every sports fan is all too familiar with today's convoluted streaming schedules. Launching today on ESPN.com and the various ESPN mobile and streaming device apps, the new guide offers various views, including one that lists all the sporting events in a single day and a search function, among other things. You can also flag favorite sports or teams to customize those views.

"At the core of Where to Watch is an event database created and managed by the ESPN Stats and Information Group (SIG), which aggregates ESPN and partner data feeds along with originally sourced information and programming details from more than 250 media sources, including television networks and streaming platforms," ESPN's press release says. ESPN previously offered browsable lists of games like this, but it didn't identify where you could actually watch all the games. There's no guarantee that you'll have access to the services needed to watch the games in the list, though. Those of us who cut the cable cord long ago know that some games -- especially those local to your city -- are unavailable without cable.