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."

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.

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.

An anonymous reader quotes a report from Wired: Two years ago, Lizmary Fernandez took a detour from studying to be an immigration attorney to join a free Apple course for making iPhone apps. The Apple Developer Academy in Detroit launched as part of the company's $200 million response to the Black Lives Matter protests and aims to expand opportunities for people of color in the country's poorest big city. But Fernandez found the program's cost-of-living stipend lacking -- "A lot of us got on food stamps," she says -- and the coursework insufficient for landing a coding job. "I didn't have the experience or portfolio," says the 25-year-old, who is now a flight attendant and preparing to apply to law school. "Coding is not something I got back to."

Since 2021, the academy has welcomed over 1,700 students, a racially diverse mix with varying levels of tech literacy and financial flexibility. About 600 students, including Fernandez, have completed its 10-month course of half-days at Michigan State University, which cosponsors the Apple-branded and Apple-focused program. WIRED reviewed contracts and budgets and spoke with officials and graduates for the first in-depth examination of the nearly $30 million invested in the academy over the past four years -- almost 30 percent of which came from Michigan taxpayers and the university's regular students. As tech giants begin pouring billions of dollars into AI-related job training courses across the country, the Apple academy offers lessons on the challenges of uplifting diverse communities.

[...] The program gives out iPhones and MacBooks and spends an estimated $20,000 per student, nearly twice as much as state and local governments budget for community colleges. [...] About 70 percent of students graduate, which [Sarah Gretter, the academy leader for Michigan State] describes as higher than typical for adult education. She says the goal is for them to take "a next step," whether a job or more courses. Roughly a third of participants are under 25, and virtually all of them pursue further schooling. [...] About 71 percent of graduates from the last two years went onto full-time jobs across a variety of industries, according to academy officials. Amy J. Ko, a University of Washington computer scientist who researches computing education, calls under 80 percent typical for the coding schools she has studied but notes that one of her department's own undergraduate programs has a 95 percent job placement rate.

Microsoft has denied any plans to rewrite Windows 11 using AI and Rust after a LinkedIn post from one of its top-level engineers sparked a wave of online backlash by claiming the company's goal was to "eliminate every line of C and C++ from Microsoft by 2030."

Galen Hunt, a principal software engineer responsible for several large-scale research projects at Microsoft, made the claim in what was originally a hiring post for his team. His original wording described a "North Star" of "1 engineer, 1 month, 1 million lines of code" and outlined a strategy to "combine AI and Algorithms to rewrite Microsoft's largest codebases." The repeated use of "our" in the post led many to interpret it as an official company direction rather than a personal research ambition.

Frank X. Shaw, Microsoft's head of communications, told Windows Latest that the company has no such plans. Hunt subsequently edited his LinkedIn post to clarify that "Windows is NOT being rewritten in Rust with AI" and that his team's work is a research project focused on building technology to enable language-to-language migration. He characterized the reaction as "speculative reading between the lines."

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?

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."

Long-time Slashdot reader theodp writes: Last September, tech-backed nonprofit Code.org pledged to engage 25 million K-12 schoolchildren in an "Hour of AI" this school year. Preliminary numbers released this week by the Code.org Advocacy Coalition showed that [halfway through the five-day event Computer Science Education Week] 13.1 million users had participated in the inaugural Hour of AI, attaining 52.4% of its goal of 25 million participants.

In a pivot from coding to AI literacy, the Hour of AI replaced Code.org's hugely-popular Hour of Code this December as the flagship event of Computer Science Education Week (December 8-14). According to Code.org's 2024-25 Impact Report, "in 2024–25 alone, students logged over 100 million Hours of Code, including more than 43 million in the four months leading up to and including CS Education Week."
Minecraft participated with their own Hour of AI lessons. ("Program an AI Agent to craft tools and build shelter before dusk falls in this iconic challenge!") And Google contributed AI Quests, "a gamified, in-class learning experience" allowing students to "step into the shoes of Google researchers using AI to solve real-world challenges." Other participating organizations included the Scratch Foundation, Lego Education, Adobe, and Roblox.

And Microsoft contributed two — including one with their block-based programming environment Microsoft MakeCode Arcade, with students urged to "code and train your own super-smart bug using AI algorithms and challenge other AI bugs in an epic Tower battle for ultimate Bug Arena glory!"

See all the educational festivities here...

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.

One developer tells MIT Technology Review that AI tools weaken the coding instincts he used to have. And beyond that, "It's just not fun sitting there with my work being done for me."

But is AI making coders faster? "After speaking to more than 30 developers, technology executives, analysts, and researchers, MIT Technology Review found that the picture is not as straightforward as it might seem..." For some developers on the front lines, initial enthusiasm is waning as they bump up against the technology's limitations. And as a growing body of research suggests that the claimed productivity gains may be illusory, some are questioning whether the emperor is wearing any clothes.... Data from the developer analytics firm GitClear shows that most engineers are producing roughly 10% more durable code — code that isn't deleted or rewritten within weeks — since 2022, likely thanks to AI. But that gain has come with sharp declines in several measures of code quality. Stack Overflow's survey also found trust and positive sentiment toward AI tools falling significantly for the first time. And most provocatively, a July study by the nonprofit research organization Model Evaluation & Threat Research (METR) showed that while experienced developers believed AI made them 20% faster, objective tests showed they were actually 19% slower...

Developers interviewed by MIT Technology Review generally agree on where AI tools excel: producing "boilerplate code" (reusable chunks of code repeated in multiple places with little modification), writing tests, fixing bugs, and explaining unfamiliar code to new developers. Several noted that AI helps overcome the "blank page problem" by offering an imperfect first stab to get a developer's creative juices flowing. It can also let nontechnical colleagues quickly prototype software features, easing the load on already overworked engineers. These tasks can be tedious, and developers are typically glad to hand them off. But they represent only a small part of an experienced engineer's workload. For the more complex problems where engineers really earn their bread, many developers told MIT Technology Review, the tools face significant hurdles...

The models also just get things wrong. Like all LLMs, coding models are prone to "hallucinating" — it's an issue built into how they work. But because the code they output looks so polished, errors can be difficult to detect, says James Liu, director of software engineering at the advertising technology company Mediaocean. Put all these flaws together, and using these tools can feel a lot like pulling a lever on a one-armed bandit. "Some projects you get a 20x improvement in terms of speed or efficiency," says Liu. "On other things, it just falls flat on its face, and you spend all this time trying to coax it into granting you the wish that you wanted and it's just not going to..." There are also more specific security concerns, she says. Researchers have discovered a worrying class of hallucinations where models reference nonexistent software packages in their code. Attackers can exploit this by creating packages with those names that harbor vulnerabilities, which the model or developer may then unwittingly incorporate into software.
Other key points from the article:

• LLMs can only hold limited amounts of information in context windows, so "they struggle to parse large code bases and are prone to forgetting what they're doing on longer tasks."

• "While an LLM-generated response to a problem may work in isolation, software is made up of hundreds of interconnected modules. If these aren't built with consideration for other parts of the software, it can quickly lead to a tangled, inconsistent code base that's hard for humans to parse and, more important, to maintain."

• "Accumulating technical debt is inevitable in most projects, but AI tools make it much easier for time-pressured engineers to cut corners, says GitClear's Harding. And GitClear's data suggests this is happening at scale..."

• "As models improve, the code they produce is becoming increasingly verbose and complex, says Tariq Shaukat, CEO of Sonar, which makes tools for checking code quality. This is driving down the number of obvious bugs and security vulnerabilities, he says, but at the cost of increasing the number of 'code smells' — harder-to-pinpoint flaws that lead to maintenance problems and technical debt."

Yet the article cites a recent Stanford University study that found employment among software developers aged 22 to 25 dropped nearly 20% between 2022 and 2025, "coinciding with the rise of AI-powered coding tools."

The story is part of MIT Technology Review's new Hype Correction series of articles about AI.

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.

The new chief of Britain's Secret Intelligence Service told officers this week that they must become as fluent in programming languages like Python as they are in foreign languages like Russian as the spy agency adapts to what she described as a space between peace and war. Blaise Metreweli, MI6's first female chief and previously the service's director general of technology and innovation, said in her first public speech that mastery of technology is now required across the organization.

She warned that advanced technologies including AI, biotechnology and quantum computing are revolutionizing both economies and the reality of conflict. Metreweli focused particularly on threats from Russia, saying the country is testing the UK in the grey zone through cyberattacks on critical infrastructure, drones near sensitive sites and propaganda operations.

AI "has gutted entry-level roles in the tech industry," reports Rest of World.

One student at a high-ranking engineering college in India tells them that among his 400 classmates, "fewer than 25% have secured job offers... there's a sense of panic on the campus." Students at engineering colleges in India, China, Dubai, and Kenya are facing a "jobpocalypse" as artificial intelligence replaces humans in entry-level roles. Tasks once assigned to fresh graduates, such as debugging, testing, and routine software maintenance, are now increasingly automated. Over the last three years, the number of fresh graduates hired by big tech companies globally has declined by more than 50%, according to a report published by SignalFire, a San Francisco-based venture capital firm. Even though hiring rebounded slightly in 2024, only 7% of new hires were recent graduates. As many as 37% of managers said they'd rather use AI than hire a Gen Z employee...

Indian IT services companies have reduced entry-level roles by 20%-25% thanks to automation and AI, consulting firm EY said in a report last month. Job platforms like LinkedIn, Indeed, and Eures noted a 35% decline in junior tech positions across major EU countries during 2024...

"Five years ago, there was a real war for [coders and developers]. There was bidding to hire," and 90% of the hires were for off-the-shelf technical roles, or positions that utilize ready-made technology products rather than requiring in-house development, said Vahid Haghzare, director at IT hiring firm Silicon Valley Associates Recruitment in Dubai. Since the rise of AI, "it has dropped dramatically," he said. "I don't even think it's touching 5%. It's almost completely vanished." The company headhunts workers from multiple countries including China, Singapore, and the U.K... The current system, where a student commits three to five years to learn computer science and then looks for a job, is "not sustainable," Haghzare said. Students are "falling down a hole, and they don't know how to get out of it."

An anonymous reader shared this report from Phoronix: Due to the growing number of GNOME Shell extensions looking to appear on extensions.gnome.org that were generated using AI, it's now prohibited. The new rule in their guidelines note that AI-generated code will be explicitly rejected:

"Extensions must not be AI-generated

While it is not prohibited to use AI as a learning aid or a development tool (i.e. code completions), extension developers should be able to justify and explain the code they submit, within reason.

Submissions with large amounts of unnecessary code, inconsistent code style, imaginary API usage, comments serving as LLM prompts, or other indications of AI-generated output will be rejected."
In a blog post, GNOME developer Javad Rahmatzadeh explains that "Some devs are using AI without understanding the code..."

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.