In an opinion piece for The Register, Steven J. Vaughan-Nichols argues that while "vibe coding" can be fun and occasionally useful for small, throwaway projects, it produces brittle, low-quality code that doesn't scale and ultimately burdens real developers with cleanup and maintenance. An anonymous reader shares an excerpt: Vibe coding got a big boost when everyone's favorite open source programmer, Linux's Linus Torvalds, said he'd been using Google's Antigravity LLM on his toy program AudioNoise, which he uses to create "random digital audio effects" using his "random guitar pedal board design." This is not exactly Linux or even Git, his other famous project, in terms of the level of work. Still, many people reacted to Torvalds' vibe coding as "wow!" It's certainly noteworthy, but has the case for vibe coding really changed? [...] It's fun, and for small projects, it's productive. However, today's programs are complex and call upon numerous frameworks and resources. Even if your vibe code works, how do you maintain it? Do you know what's going on inside the code? Chances are you don't. Besides, the LLM you used two weeks ago has been replaced with a new version. The exact same prompts that worked then yield different results today. Come to think of it, it's an LLM. The same prompts and the same LLM will give you different results every time you run it. This is asking for disaster. Just ask Jason Lemkin. He was the guy who used the vibe coding platform Replit, which went "rogue during a code freeze, shut down, and deleted our entire database." Whoops! Yes, Replit and other dedicated vibe programming AIs, such as Cursor and Windsurf, are improving. I'm not at all sure, though, that they've been able to help with those fundamental problems of being fragile and still cannot scale successfully to the demands of production software. It's much worse than that. Just because a program runs doesn't mean it's good. As Ruth Suehle, President of the Apache Software Foundation, commented recently on LinkedIn, naive vibe coders "only know whether the output works or doesn't and don't have the skills to evaluate it past that. The potential results are horrifying." Why? In another LinkedIn post, Craig McLuckie, co-founder and CEO of Stacklok, wrote: "Today, when we file something as 'good first issue' and in less than 24 hours get absolutely inundated with low-quality vibe-coded slop that takes time away from doing real work. This pattern of 'turning slop into quality code' through the review process hurts productivity and hurts morale." McLuckie continued: "Code volume is going up, but tensions rise as engineers do the fun work with AI, then push responsibilities onto their team to turn slop into production code through structured review."

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AI still can't produce code as well as most junior programmers he's worked with, David Heinemeier Hansson, the creator of Ruby on Rails and co-founder of 37 Signals, said on a recent podcast [video link], which is why he continues to write most of his code by hand. Hansson compared AI's current coding capabilities to "a flickering light bulb" -- total darkness punctuated by moments of clarity before going pitch black again. At his company, humans wrote 95% of the code for Fizzy, 37 Signals' Kanban-inspired organization product, he said. The team experimented with AI-powered features, but those ended up on the cutting room floor. "I'm not feeling that we're falling behind at 37 Signals in terms of our ability to produce, in terms of our ability to launch things or improve the products," Hansson said. Hansson said he remains skeptical of claims that businesses can fire half their programmers and still move faster. Despite his measured skepticism, Hansson said he marvels at the scale of bets the U.S. economy is placing on AI reaching AGI. "The entire American economy right now is one big bet that that's going to happen," he said.

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For a quarter century, the TIOBE Index has attempted to rank the popularity of programming languages by the number of search engine results they bring up — and this week they had an announcement. Over the last year the language showing the largest increase in its share of TIOBE's results was C#. TIOBE founder/CEO Paul Jansen looks back at how C++ evolved: From a language-design perspective, C# has often been an early adopter of new trends among mainstream languages. At the same time, it successfully made two major paradigm shifts: from Windows-only to cross-platform, and from Microsoft-owned to open source. C# has consistently evolved at the right moment. For many years now, there has been a direct battle between Java and C# for dominance in the business software market. I always assumed Java would eventually prevail, but after all this time the contest remains undecided. It is an open question whether Java — with its verbose, boilerplate-heavy style and Oracle ownership — can continue to keep C# at bay. While C# remains stuck in the same #5 position it was in a year ago, its share of TIOBE's results rose 2.94% — the largest increase of the 100 languages in their rankngs. But TIOBE's CEO notes that his rankings for the top 10 highest-scoring languages delivered "some interesting movements" in 2025: C and C++ swapped positions. [C rose to the #2 position — behind Python — while C++ dropped from #2 to the #4 rank that C held in January of 2025]. Although C++ is evolving faster than ever, some of its more radical changes — such as the modules concept — have yet to see widespread industry adoption. Meanwhile, C remains simple, fast, and extremely well suited to the ever-growing market of small embedded systems. Even Rust has struggled to penetrate this space, despite reaching an all-time high of position #13 this month. So who were the other winners of 2025, besides C#? Perl made a surprising comeback, jumping from position #32 to #11 and re-entering the top 20. Another language returning to the top 10 is R, driven largely by continued growth in data science and statistical computing. Of course, where there are winners, there are also losers. Go appears to have permanently lost its place in the top 10 during 2025. The same seems true for Ruby, which fell out of the top 20 and is unlikely to return anytime soon. What can we expect from 2026? I have a long history of making incorrect predictions, but I suspect that TypeScript will finally break into the top 20. Additionally, Zig, which climbed from position #61 to #42 in 2025, looks like a strong candidate to enter the TIOBE top 30. Here's how TIOBE estimated the 10 most popularity programming languages at the end of 2025 PythonCJavaC++C#JavaScriptVisual BasicSQLDelphi/Object PascalR

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Boris Cherny, the creator of Claude Code at Anthropic, revealed a deceptively simple workflow that uses parallel AI agents, verification loops, and shared memory to let one developer operate with the output of an entire engineering team. "I run 5 Claudes in parallel in my terminal," Cherny wrote. "I number my tabs 1-5, and use system notifications to know when a Claude needs input." He also runs "5-10 Claudes on claude.ai" in his browser, using a "teleport" command to hand off work between the web and his local machine. This validates the "do more with less" strategy Anthropic's President Daniela Amodei recently pitched during an interview with CNBC. VentureBeat reports: For the past week, the engineering community has been dissecting a thread on X from Boris Cherny, the creator and head of Claude Code at Anthropic. What began as a casual sharing of his personal terminal setup has spiraled into a viral manifesto on the future of software development, with industry insiders calling it a watershed moment for the startup. "If you're not reading the Claude Code best practices straight from its creator, you're behind as a programmer," wrote Jeff Tang, a prominent voice in the developer community. Kyle McNease, another industry observer, went further, declaring that with Cherny's "game-changing updates," Anthropic is "on fire," potentially facing "their ChatGPT moment." The excitement stems from a paradox: Cherny's workflow is surprisingly simple, yet it allows a single human to operate with the output capacity of a small engineering department. As one user noted on X after implementing Cherny's setup, the experience "feels more like Starcraft" than traditional coding -- a shift from typing syntax to commanding autonomous units.

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Stack Overflow's monthly question volume has collapsed to about 300 -- levels not seen since the site launched in 2009, according to data from the Stack Overflow Data Explorer that tracks the platform's activity over its sixteen-year history. Questions peaked around 2014 at roughly 200,000 per month, then began a gradual decline that accelerated dramatically after ChatGPT's November 2022 launch. By May 2025, monthly questions had fallen to early-2009 levels, and the latest data through early 2026 shows the collapse has only continued -- the line now sits near the bottom of the chart, barely registering. The decline predates LLMs. Questions began dropping around 2014 when Stack Overflow improved moderator efficiency and closed questions more aggressively. In mid-2021, Prosus acquired Stack Overflow for $1.8 billion. The founders, Jeff Atwood and Joel Spolsky, exited before the terminal decline became apparent. ChatGPT accelerated what was already underway. The chatbot answers programming questions faster, draws on Stack Overflow's own corpus for training data, and doesn't close questions for being duplicates.

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The relentless climb in memory prices driven by the AI boom's insatiable demand for datacenter hardware has renewed an old debate about whether modern software has grown inexcusably fat, a column by the Register argues. The piece points to Windows Task Manager as a case study: the current executable occupies 6MB on disk and demands nearly 70MB of RAM just to display system information, compared to the original's 85KB footprint. "Its successor is not orders of magnitude more functional," the column notes. The author draws a parallel to the 1970s fuel crisis, when energy shortages spurred efficiency gains, and argues that today's memory crunch could force similar discipline. "Developers should consider precisely how much of a framework they really need and devote effort to efficiency," the column adds. "Managers must ensure they also have the space to do so." The article acknowledges that "reversing decades of application growth will not happen overnight" but calls for toolchains to be rethought and rewards given "for compactness, both at rest and in operation."

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Michael Truell, the 25-year-old CEO and cofounder of Cursor, is drawing a sharp distinction between careful AI-assisted development and the more hands-off approach commonly known as "vibe coding." Speaking at a conference, Truell described vibe coding as a method where users "close your eyes and you don't look at the code at all and you just ask the AI to go build the thing for you." He compared it to constructing a house by putting up four walls and a roof without understanding the underlying wiring or floorboards. The approach might work for quickly mocking up a game or website, but more advanced projects face real risks. "If you close your eyes and you don't look at the code and you have AIs build things with shaky foundations as you add another floor, and another floor, and another floor, and another floor, things start to kind of crumble," Truell said. Truell and three fellow MIT graduates created Cursor in 2022. The tool embeds AI directly into the integrated development environment and uses the context of existing code to predict the next line, generate functions, and debug errors. The difference, as Truell frames it, is that programmers stay engaged with what's happening under the hood rather than flying blind.

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Apple's Developer Academy in Detroit has spent roughly $30 million over four years training hundreds of people to build iPhone apps, but not everyone lands coding jobs right away, according to a WIRED story published this week. The program launched in 2021 as part of Apple's $200 million response to the Black Lives Matter protests and costs an estimated $20,000 per student -- nearly twice what state and local governments budget for community colleges. About 600 students have completed the 10-month course at Michigan State University. Academy officials say 71% of graduates from the past two years found full-time jobs across various industries. The program provides iPhones, MacBooks and stipends ranging from $800 to $1,500 per month, though one former student said many participants relied on food stamps. Apple contributed $11.6 million to the academy. Michigan taxpayers and the university's regular students covered about $8.6 million -- nearly 30% of total funding. Two graduates said their lack of proficiency in Android hurt their job prospects. Apple's own US tech workforce went from 6% Black before the academy opened to about 3% this year.

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theodp writes: We all mix pictures, emojis, and text freely in our communications. So why not in our code? That's the premise of "Fun With Python and Emoji: What Might Adding Pictures to Text Programming Languages Look Like?" (two-image Bluesky explainer; full slides), which takes a look at what mixing emoji with Python and SQL might look like. A GitHub repo includes a Google Colab-ready Python notebook proof of concept that does rudimentary emoji-to-text translation via an IPython input transformer. So, in the Golden Age of AI -- some 60+ years after Kenneth Iverson introduced the chock-full-of-symbols APL -- are valid technical reasons still keeping symbols and pictures out of code, or is their absence more of a programming dogma thing?

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Microsoft plans to eliminate all C and C++ code across its major codebases by 2030, replacing it with Rust using AI-assisted, large-scale refactoring. "My goal is to eliminate every line of C and C++ from Microsoft by 2030," Microsoft Distinguished Engineer Galen Hunt writes in a post on LinkedIn. "Our strategy is to combine AI and Algorithms to rewrite Microsoft's largest codebases. Our North Star is '1 engineer, 1 month, 1 million lines of code.' To accomplish this previously unimaginable task, we've built a powerful code processing infrastructure. Our algorithmic infrastructure creates a scalable graph over source code at scale. Our AI processing infrastructure then enables us to apply AI agents, guided by algorithms, to make code modifications at scale. The core of this infrastructure is already operating at scale on problems such as code understanding." Hunt says he's looking to hire a Principal Software Engineer to help with this effort. "The purpose of this Principal Software Engineer role is to help us evolve and augment our infrastructure to enable translating Microsoft's largest C and C++ systems to Rust," writes Hunt. "A critical requirement for this role is experience building production quality systems-level code in Rust -- preferably at least 3 years of experience writing systems-level code in Rust. Compiler, database, or OS implementation experience is highly desired. While compiler implementation experience is not required to apply, the willingness to acquire that experience in our team is required."

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The team authoring the Rust 2025 Vision Doc interviewed Rust developers to find out what they liked about the language — and have now issued three recommendations "to help Rust continue to scale across domains and usage levels." — Enumerate and describe Rust's design goals and integrate them into our processes, helping to ensure they are observed by future language designers and the broader ecosystem. — Double down on extensibility, introducing the ability for crates to influence the develop experience and the compilation pipeline. — Help users to navigate the crates.io ecosystem and enable smoother interop The real "empowering magic" of Rust arises from achieving a number of different attributes all at once — reliability, efficiency, low-level control, supportiveness, and so forth. It would be valuable to have a canonical list of those values that we could collectively refer to as a community and that we could use when evaluating RFCs or other proposed designs... We recommend creating an RFC that defines the goals we are shooting for as we work on Rust... One insight from our research is that we don't need to define which values are "most important". We've seen that for Rust to truly work, it must achieveallthe factors at once... We recommenddoubling down on extensibilityas a core strategy. Rust's extensibility — traits, macros, operator overloading — has been key to its versatility. But that extensibility is currently concentrated in certain areas: the type system and early-stage proc macros. We should expand it to coversupportive interfaces(better diagnostics and guidance from crates) andcompilation workflow(letting crates integrate at more stages of the build process)... Doubling down on extensibility will not only make current Rust easier to use, it will enable and support Rust's use in new domains. Safety Critical applications in particular require a host of custom lints and tooling to support the associated standards. Compiler extensibility allows Rust to support those niche needs in a more general way. We recommend finding ways to help users navigate the crates.io ecosystem... [F]inding which crates to use presents a real obstacle when people are getting started. The Rust org maintains a carefully neutral stance, which is good, but also means that people don't have anywhere to go for advice on a good "starter set" crates... Part of the solution is enabling better interop between libraries.

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Elite computer science degrees are no longer a guaranteed on-ramp to tech jobs, as AI-driven coding tools slash demand for entry-level engineers and concentrate hiring around a small pool of already "elite" or AI-savvy developers. The Los Angeles Times reports: "Stanford computer science graduates are struggling to find entry-level jobs" with the most prominent tech brands, said Jan Liphardt, associate professor of bioengineering at Stanford University. "I think that's crazy." While the rapidly advancing coding capabilities of generative AI have made experienced engineers more productive, they have also hobbled the job prospects of early-career software engineers. Stanford students describe a suddenly skewed job market, where just a small slice of graduates -- those considered "cracked engineers" who already have thick resumes building products and doing research -- are getting the few good jobs, leaving everyone else to fight for scraps. "There's definitely a very dreary mood on campus," said a recent computer science graduate who asked not to be named so they could speak freely. "People [who are] job hunting are very stressed out, and it's very hard for them to actually secure jobs." The shake-up is being felt across California colleges, including UC Berkeley, USC and others. The job search has been even tougher for those with less prestigious degrees. [...] Data suggests that even though AI startups like OpenAI and Anthropic are hiring many people, it is not offsetting the decline in hiring elsewhere. Employment for specific groups, such as early-career software developers between the ages of 22 and 25 has declined by nearly 20% from its peak in late 2022, according to a Stanford study. [...] A common sentiment from hiring managers is that where they previously needed ten engineers, they now only need "two skilled engineers and one of these LLM-based agents," which can be just as productive, said Nenad Medvidovic, a computer science professor at the University of Southern California. "We don't need the junior developers anymore," said Amr Awadallah, CEO of Vectara, a Palo Alto-based AI startup. "The AI now can code better than the average junior developer that comes out of the best schools out there." [...] Stanford students say they are arriving at the job market and finding a split in the road; capable AI engineers can find jobs, but basic, old-school computer science jobs are disappearing. As they hit this surprise speed bump, some students are lowering their standards and joining companies they wouldn't have considered before. Some are creating their own startups. A large group of frustrated grads are deciding to continue their studies to beef up their resumes and add more skills needed to compete with AI.

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The R programming language "is sometimes frowned upon by 'traditional' software engineers," says the CEO of software quality services vendor Tiobe, "due to its unconventional syntax and limited scalability for large production systems." But he says it "continues to thrive at universities and in research-driven industries, and "for domain experts, it remains a powerful and elegant tool." Yet it's now gaining more popularity as statistics and large-scale data visualization become important (a trend he also sees reflected in the rise of Wolfram/Mathematica). That's according to December's edition of his TIOBE Index, which attempts to rank the popularity of programming languages based on search-engine results for courses, third-party vendors, and skilled engineers. InfoWorld explains: In the December 2025 index, published December 7, R ranks 10th with a 1.96% rating. R has cracked the Tiobe index's top 10 before, such as in April 2020 and July 2020, but not in recent years. The rival Pypl Popularity of Programming Language Index, meanwhile, has R ranked fifth this month with a 5.84% share. "Programming language R is known for fitting statisticians and data scientists like a glove," said Paul Jansen, CEO of software quality services vendor Tiobe, in a bulletin accompanying the December index... Although data science rival Python has eclipsed R in terms of general adoption, Jansen said R has carved out a solid and enduring niche, excelling at rapid experimentation, statistical modeling, and exploratory data analysis. "We have seen many Tiobe index top 10 entrants rising and falling," Jansen wrote. "It will be interesting to see whether R can maintain its current position." "Python remains ahead at 23.64%," notes TechRepublic, "while the familiar chase group behind it holds steady for the moment. The real movement comes deeper in the list, where SQL edges upward, R rises to the top 10, and Delphi/Object Pascal slips away... SQLclimbs from tenth to eighth at 2.10%, adding a small +0.11% that's enough to move it upward in a tightly packed section of the table. Perl holds ninth at 1.97%, strengthened by a +1.33% gain that extends its late-year resurgence." It's interesting to see how TIOBE's ranking compare with PYPL's (which ranks languages based solely on how often language tutorials are searched on Google): TIOBE PYPL Python Python C C/C++ C++ Objective-C Java Java C# R JavaScript JavaScript Visual Basic Swift SQL C# Perl PHP R Rust Despite their different methodologies, both lists put Python at #1, Java at #5, and JavaScript at #7.

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Steven J. Vaughan-Nichols files this report from Tokyo: At the invitation-only Linux Kernel Maintainers Summit here, the top Linux maintainers decided, as Jonathan Corbet, Linux kernel developer, put it, "The consensus among the assembled developers is that Rust in the kernel is no longer experimental — it is now a core part of the kernel and is here to stay. So the 'experimental' tag will be coming off." As Linux kernel maintainer Steven Rosted told me, "There was zero pushback." This has been a long time coming. This shift caps five years of sometimes-fierce debate over whether the memory-safe language belonged alongside C at the heart of the world's most widely deployed open source operating system... It all began when Alex Gaynor and Geoffrey Thomas at the 2019 Linux Security Summit said that about two-thirds of Linux kernel vulnerabilities come from memory safety issues. Rust, in theory, could avoid these by using Rust's inherently safer application programming interfaces (API)... In those early days, the plan was not to rewrite Linux in Rust; it still isn't, but to adopt it selectively where it can provide the most security benefit without destabilizing mature C code. In short, new drivers, subsystems, and helper libraries would be the first targets... Despite the fuss, more and more programs were ported to Rust. By April 2025, the Linux kernel contained about 34 million lines of C code, with only 25 thousand lines written in Rust. At the same time, more and more drivers and higher-level utilities were being written in Rust. For instance, the Debian Linux distro developers announced that going forward, Rust would be a required dependency in its foundational Advanced Package Tool (APT). This change doesn't mean everyone will need to use Rust. C is not going anywhere. Still, as several maintainers told me, they expect to see many more drivers being written in Rust. In particular, Rust looks especially attractive for "leaf" drivers (network, storage, NVMe, etc.), where the Rust-for-Linux bindings expose safe wrappers over kernel C APIs. Nevertheless, for would-be kernel and systems programmers, Rust's new status in Linux hints at a career path that blends deep understanding of C with fluency in Rust's safety guarantees. This combination may define the next generation of low-level development work.

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