The education-news site EdSurge published "sponsored content" from Minecraft Education this month. "Students light up when they create something meaningful," the article begins. "Self-expression fuels learning, and creativity lies at the heart of the human experience."

But they also argue that "As AI rapidly reshapes software development, computer science education must move beyond syntax drills and algorithmic repetition." Students "must also learn to think systemically..." As AI automates many of the mechanical aspects of programming, the value of CS education is shifting, from writing perfect code to shaping systems, telling stories through logic and designing ethical, human-centered solutions... [I]t's critical to offer computer science experiences that foster invention, expression and design. This isn't just an education issue — it's a workforce one. Creativity now ranks among the top skills employers seek, alongside analytical thinking and AI literacy. As automation reshapes the job market, McKinsey estimates up to 375 million workers may need to change occupations by 2030. The takeaway? We need more adaptable, creative thinkers.

Creative coding, where programming becomes a medium for self-expression and innovation, offers a promising solution to this disconnect. By positioning code as a creative tool, educators can tap into students' intrinsic motivation while simultaneously building computational thinking skills. This approach helps students see themselves as creators, not just consumers, of technology. It aligns with digital literacy frameworks that emphasize critical evaluation, meaningful contribution and not just technical skills.

One example of creative coding comes from a curriculum that introduces computer science through game design and storytelling in Minecraft... Developed by Urban Arts in collaboration with Minecraft Education, the program offers middle school teachers professional development, ongoing coaching and a 72-session curriculum built around game-based instruction. Designed for grades 6-8, the project-based program is beginner-friendly; no prior programming experience is required for teachers or students. It blends storytelling, collaborative design and foundational programming skills with a focus on creativity and equity.... Students use Minecraft to build interactive narratives and simulations, developing computational thinking and creative design... Early results are promising: 93 percent of surveyed teachers found the Creative Coders program engaging and effective, noting gains in problem-solving, storytelling and coding, as well as growth in critical thinking, creativity and resilience.

As AI tools like GitHub Copilot become standard in development workflows, the definition of programming proficiency is evolving. Skills like prompt engineering, systems thinking and ethical oversight are rising in importance, precisely what creative coding develops... As AI continues to automate routine tasks, students must be able to guide systems, understand logic and collaborate with intelligent tools. Creative coding introduces these capabilities in ways that are accessible, culturally relevant and engaging for today's learners.
Some background from long-time Slashdot reader theodp: The Urban Arts and Microsoft Creative Coders program touted by EdSurge in its advertorial was funded by a $4 million Education Innovation and Research grant that was awarded to Urban Arts in 2023 by the U.S. Education Department "to create an engaging, game-based, middle school CS course using Minecraft tools" for 3,450 middle schoolers (6th-8th grades)" in New York and California (Urban Arts credited Minecraft for helping craft the winning proposal)... New York City is a Minecraft Education believer — the Mayor's Office of Media and Entertainment recently kicked off summer with the inaugural NYC Video Game Festival, which included the annual citywide Minecraft Education Battle of the Boroughs Esports Competition in partnership with NYC Public Schools.

It's not just Amazon's CEO predicting AI will lower their headcount. "Top executives at some of the largest American companies have a warning for their workers: Artificial intelligence is a threat to your job," reports the Washington Post — including IBM, Salesforce, and JPMorgan Chase.

But are they really just trying to impress their shareholders? Economists say there aren't yet strong signs that AI is driving widespread layoffs across industries.... CEOs are under pressure to show they are embracing new technology and getting results — incentivizing attention-grabbing predictions that can create additional uncertainty for workers. "It's a message to shareholders and board members as much as it is to employees," Molly Kinder, a Brookings Institution fellow who studies the impact of AI, said of the CEO announcements, noting that when one company makes a bold AI statement, others typically follow. "You're projecting that you're out in the future, that you're embracing and adopting this so much that the footprint [of your company] will look different."

Some CEOs fear they could be ousted from their job within two years if they don't deliver measurable AI-driven business gains, a Harris Poll survey conducted for software company Dataiku showed. Tech leaders have sounded some of the loudest warnings — in line with their interest in promoting AI's power...

IBM, which recently announced job cuts, said it replaced a couple hundred human resource workers with AI "agents" for repetitive tasks such as onboarding and scheduling interviews. In January, Meta CEO Mark Zuckerberg suggested on Joe Rogan's podcast that the company is building AI that might be able to do what some human workers do by the end of the year.... Marianne Lake, JPMorgan's CEO of consumer and community banking, told an investor meeting last month that AI could help the bank cut headcount in operations and account services by 10 percent. The CEO of BT Group Allison Kirkby suggested that advances in AI would mean deeper cuts at the British telecom company...

Despite corporate leaders' warnings, economists don't yet see broad signs that AI is driving humans out of work. "We have little evidence of layoffs so far," said Columbia Business School professor Laura Veldkamp, whose research explores how companies' use of AI affects the economy. "What I'd look for are new entrants with an AI-intensive business model, entering and putting the existing firms out of business." Some researchers suggest there is evidence AI is playing a role in the drop in openings for some specific jobs, like computer programming, where AI tools that generate code have become standard... It is still unclear what benefits companies are reaping from employees' use of AI, said Arvind Karunakaran, a faculty member of Stanford University's Center for Work, Technology, and Organization. "Usage does not necessarily translate into value," he said. "Is it just increasing productivity in terms of people doing the same task quicker or are people now doing more high value tasks as a result?"

Lynda Gratton, a professor at London Business School, said predictions of huge productivity gains from AI remain unproven. "Right now, the technology companies are predicting there will be a 30% productivity gain. We haven't yet experienced that, and it's not clear if that gain would come from cost reduction ... or because humans are more productive."
On an earnings call, Salesforce's chief operating and financial officer said AI agents helped them reduce hiring needs — and saved $50 million, according to the article. (And Ethan Mollick, co-director of Wharton School of Business' generative AI Labs, adds that if advanced tools like AI agents can prove their reliability and automate work — that could become a larger disruptor to jobs.) "A wave of disruption is going to happen," he's quoted as saying.

But while the debate continues about whether AI will eliminate or create jobs, Mollick still hedges that "the truth is probably somewhere in between."

In April Anthorpic introduced a new AI trick: multiple Claude agents combine for a "Research" feature that can "search across both your internal work context and the web" (as well as Google Workspace "and any integrations...")

But a recent Anthropic blog post notes this feature "involves an agent that plans a research process based on user queries, and then uses tools to create parallel agents that search for information simultaneously," which brings challenges "in agent coordination, evaluation, and reliability.... The model must operate autonomously for many turns, making decisions about which directions to pursue based on intermediate findings." Multi-agent systems work mainly because they help spend enough tokens to solve the problem.... This finding validates our architecture that distributes work across agents with separate context windows to add more capacity for parallel reasoning. The latest Claude models act as large efficiency multipliers on token use, as upgrading to Claude Sonnet 4 is a larger performance gain than doubling the token budget on Claude Sonnet 3.7. Multi-agent architectures effectively scale token usage for tasks that exceed the limits of single agents.

There is a downside: in practice, these architectures burn through tokens fast. In our data, agents typically use about 4Ã-- more tokens than chat interactions, and multi-agent systems use about 15Ã-- more tokens than chats. For economic viability, multi-agent systems require tasks where the value of the task is high enough to pay for the increased performance. Further, some domains that require all agents to share the same context or involve many dependencies between agents are not a good fit for multi-agent systems today.

For instance, most coding tasks involve fewer truly parallelizable tasks than research, and LLM agents are not yet great at coordinating and delegating to other agents in real time. We've found that multi-agent systems excel at valuable tasks that involve heavy parallelization, information that exceeds single context windows, and interfacing with numerous complex tools.
Thanks to Slashdot reader ZipNada for sharing the news.

alternative_right shares a report from ScienceAlert: A team of biologists from Northeastern University and the University of Kentucky has found one of the key molecules involved in axolotl regeneration. It's a crucial component in ensuring the body grows back the right parts in the right spot: for instance, growing a hand, from the wrist. "The cells can interpret this cue to say, 'I'm at the elbow, and then I'm going to grow back the hand' or 'I'm at the shoulder... so I'm going to then enable those cells to grow back the entire limb'," biologist James Monaghan explains.

That molecule, retinoic acid, is arranged through the axolotl body in a gradient, signaling to regenerative cells how far down the limb has been severed. Closer to the shoulder, axolotls have higher levels of retinoic acid, and lower levels of the enzyme that breaks it down. This ratio changes the further the limb extends from the body. The team found this balance between retinoic acid and the enzyme that breaks it down plays a crucial role in 'programming' the cluster of regenerative cells that form at an injury site. When they added surplus retinoic acid to the hand of an axolotl in the process of regenerating, it grew an entire arm instead.

In theory, the human body has the right molecules and cells to do this too, but our cells respond to the signals very differently, instead forming collagen-based scars at injury sites. Next, Monaghan is keen to find out what's going on inside cells -- the axolotl's, and our own -- when those retinoic acid signals are received. The research is published in Nature Communications.

A new benchmark assembled by a team of International Olympiad medalists suggests the hype about large language models beating elite human coders is premature. LiveCodeBench Pro, unveiled in a 584-problem study [PDF] drawn from Codeforces, ICPC and IOI contests, shows the best frontier model clears just 53% of medium-difficulty tasks on its first attempt and none of the hard ones, while grandmaster-level humans routinely solve at least some of those highest-tier problems.

The researchers measured models and humans on the same Elo scale used by Codeforces and found that OpenAI's o4-mini-high, when stripped of terminal tools and limited to one try per task, lands at an Elo rating of 2,116 -- hundreds of points below the grandmaster cutoff and roughly the 1.5 percentile among human contestants. A granular tag-by-tag autopsy identified implementation-friendly, knowledge-heavy problems -- segment trees, graph templates, classic dynamic programming -- as the models' comfort zone; observation-driven puzzles such as game-theory endgames and trick-greedy constructs remain stubborn roadblocks.

Because the dataset is harvested in real time as contests conclude, the authors argue it minimizes training-data leakage and offers a moving target for future systems. The broader takeaway is that impressive leaderboard jumps often reflect tool use, multiple retries or easier benchmarks rather than genuine algorithmic reasoning, leaving a conspicuous gap between today's models and top human problem-solvers.

Meta and AWS have used Rust, and Netflix uses Go,reports the programming news site InfoQ. But using another language, Apple recently "migrated its global Password Monitoring service from Java to Swift, achieving a 40% increase in throughput, and significantly reducing memory usage."

This freed up nearly 50% of their previously allocated Kubernetes capacity, according to the article, and even "improved startup time, and simplified concurrency." In a recent post, Apple engineers detailed how the rewrite helped the service scale to billions of requests per day while improving responsiveness and maintainability... "Swift allowed us to write smaller, less verbose, and more expressive codebases (close to 85% reduction in lines of code) that are highly readable while prioritizing safety and efficiency."

Apple's Password Monitoring service, part of the broader Password app's ecosystem, is responsible for securely checking whether a user's saved credentials have appeared in known data breaches, without revealing any private information to Apple. It handles billions of requests daily, performing cryptographic comparisons using privacy-preserving protocols. This workload demands high computational throughput, tight latency bounds, and elastic scaling across regions... Apple's previous Java implementation struggled to meet the service's growing performance and scalability needs. Garbage collection caused unpredictable pause times under load, degrading latency consistency. Startup overhead — from JVM initialization, class loading, and just-in-time compilation, slowed the system's ability to scale in real time. Additionally, the service's memory footprint, often reaching tens of gigabytes per instance, reduced infrastructure efficiency and raised operational costs.

Originally developed as a client-side language for Apple platforms, Swift has since expanded into server-side use cases.... Swift's deterministic memory management, based on reference counting rather than garbage collection (GC), eliminated latency spikes caused by GC pauses. This consistency proved critical for a low-latency system at scale. After tuning, Apple reported sub-millisecond 99.9th percentile latencies and a dramatic drop in memory usage: Swift instances consumed hundreds of megabytes, compared to tens of gigabytes with Java.
"While this isn't a sign that Java and similar languages are in decline," concludes InfoQ's article, "there is growing evidence that at the uppermost end of performance requirements, some are finding that general-purpose runtimes no longer suffice."

"Cooper Taylor is only 17 years old, but he's already trying to revolutionize the drone industry," writes Business Insider: His design makes the drone more efficient, customizable, and less expensive to construct, he says. He's built six prototypes, 3D printing every piece of hardware, programming the software, and even soldering the control circuit board. He says building his drone cost one-fifth of the price of buying a comparable machine, which sells for several thousand dollars. Taylor told Business Insider he hopes that "if you're a first responder or a researcher or an everyday problem solver, you can have access to this type of drone."

His innovation won him an $8,000 scholarship in April at the Junior Science and Humanities Symposium, funded by the Defense Department. Then, on May 16, he received an even bigger scholarship of $15,000 from the US Navy, which he won after presenting his research at the Regeneron International Science and Engineering Fair...

It all started when Taylor's little sister got a drone, and he was disappointed to see that it could fly for only about 30 minutes before running out of power. He did some research and found that a vertical take-off and landing, or VTOL, drone would last longer. This type of drone combines the multi-rotor helicopter style with the fixed wings of an airplane, making it extremely versatile. It lifts off as a helicopter, then transitions into plane mode. That way, it can fly farther than rotors alone could take it, which was the drawback to Taylor's sister's drone. Unlike a plane-style drone, though, it doesn't need a runway, and it can hover with its helicopter rotors.
Taylor designed a motor "that could start out helicopter-style for liftoff, then tilt back to become an airplane-style motor," according to the article.

And now this summer he'll be "working on a different drone project through a program with the Reliable Autonomous Systems Lab at the Massachusetts Institute of Technology."

Thanks to Slashdot reader Agnapot for sharing the news.

In 1989 a computer scientist argued that more functionality in software actually lowers usability and practicality — leading to the counterintuitive proposition that "worse is better". But is that still true?

Python's original creator Guido van Rossum addressed the question last month in a lightning talk at the annual Python Language Summit 2025. Guido started by recounting earlier periods of Python development from 35 years ago, where he used UNIX "almost exclusively" and thus "Python was greatly influenced by UNIX's 'worse is better' philosophy"... "The fact that [Python] wasn't perfect encouraged many people to start contributing. All of the code was straightforward, there were no thoughts of optimization... These early contributors also now had a stake in the language; [Python] was also their baby"...

Guido contrasted early development to how Python is developed now: "features that take years to produce from teams of software developers paid by big tech companies. The static type system requires an academic-level understanding of esoteric type system features." And this isn't just Python the language, "third-party projects like numpy are maintained by folks who are paid full-time to do so.... Now we have a huge community, but very few people, relatively speaking, are contributing meaningfully."

Guido asked whether the expectation for Python contributors going forward would be that "you had to write a perfect PEP or create a perfect prototype that can be turned into production-ready code?" Guido pined for the "old days" where feature development could skip performance or feature-completion to get something into the hands of the community to "start kicking the tires". "Do we have to abandon 'worse is better' as a philosophy and try to make everything as perfect as possible?" Guido thought doing so "would be a shame", but that he "wasn't sure how to change it", acknowledging that core developers wouldn't want to create features and then break users with future releases.

Guido referenced David Hewitt's PyO3 talk about Rust and Python, and that development "was using worse is better," where there is a core feature set that works, and plenty of work to be done and open questions. "That sounds a lot more fun than working on core CPython", Guido paused, "...not that I'd ever personally learn Rust. Maybe I should give it a try after," which garnered laughter from core developers.

"Maybe we should do more of that: allowing contributors in the community to have a stake and care".

An anonymous reader shares a report: Slack, an instant-messaging service popular with businesses, recently blocked other software firms from searching or storing Slack messages even if their customers permit them to do so, according to a public disclosure from Slack's owner, Salesforce.

The move, which hasn't previously been reported, could hamper fast-growing artificial intelligence startups that have used such access to power their services, such as Glean. Since the Salesforce change, Glean and other applications can no longer index, copy or store the data they access via the Slack application programming interface on a long-term basis, according to the disclosure. Salesforce will continue allowing such firms to temporarily use and store their customers' Slack data, but they must delete the data, the company said.

Longtime Slashdot reader theodp writes: The old Code.org curriculum page for middle and high school students has been changed to include a new Python Lab in the tech-backed nonprofit's K-12 offerings. Elsewhere on the site, a Computer Science and AI Foundations curriculum is described that includes units on 'Foundations of AI Programming [in Python]' and 'Insights from Data and AI [aka Data Science].' A more-detailed AI Foundations Syllabus 25-26 document promises a second semester of material is coming soon: "This semester offers an innovative approach to teaching programming by integrating learning with and about artificial intelligence (AI). Using Python as the primary language, students build foundational programming skills while leveraging AI tools to enhance computational thinking and problem-solving. The curriculum also introduces students to the basics of creating AI-powered programs, exploring machine learning, and applying data science principles."

Newly-posted videos on Code.org's YouTube channel appear to be intended to support the new Python-based CS & AI course. "Python is extremely versatile," explains a Walmart data scientist to open the video for Data Science: Using Python. "So, first of all, Python is one of the very few languages that can handle numbers very, very well." A researcher at the Univ. of Washington's Institute for Health Metrics and Evaluation (IHME) adds, "Python is the gold standard and what people expect data scientists to know [...] Key to us being able to handle really big data sets is our use of Python and cluster computing." Adding to the Python love, an IHME data analyst explains, "Python is a great choice for large databases because there's a lot of support for Python libraries."

Code.org is currently recruiting teachers to attend its CS and AI Foundations Professional Learning program this summer, which is being taught by Code.org's national network of university and nonprofit regional partners (teachers who signup have a chance to win $250 in DonorsChoose credits for their classrooms). A flyer for a five-day Michigan Professional Development program to prepare teachers for a pilot of the Code.org CS & A course touts the new curriculum as "an alternative to the AP [Computer Science] pathway" (teachers are offered scholarships covering registration, lodging, meals, and workshop materials).

Interestingly, Code.org's embrace of Python and Data Science comes as the nonprofit changes its mission to 'make CS and AI a core part of K-12 education' and launches a new national campaign with tech leaders to make CS and AI a graduation requirement. Prior to AI changing the education conversation, Code.org in 2021 boasted that it had lined up a consortium of tech giants, politicians, and educators to push its new $15 million Amazon-bankrolled Java AP CS A curriculum into K-12 classrooms. Just three years later, however, Amazon CEO Andy Jassy was boasting to investors that Amazon had turned to AI to automatically do Java coding that he claimed would have otherwise taken human coders 4,500 developer-years to complete.

An anonymous reader quotes a report from TechCrunch: Apple is launching what it calls the Foundation Models framework, which the company says will let developers tap into its AI models in an offline, on-device fashion. Onstage at WWDC 2025 on Monday, Apple VP of software engineering Craig Federighi said that the Foundation Models framework will let apps use on-device AI models created by Apple to drive experiences. These models ship as a part of Apple Intelligence, Apple's family of models that power a number of iOS features and capabilities.

"For example, if you're getting ready for an exam, an app like Kahoot can create a personalized quiz from your notes to make studying more engaging," Federighi said. "And because it happens using on-device models, this happens without cloud API costs [] We couldn't be more excited about how developers can build on Apple intelligence to bring you new experiences that are smart, available when you're offline, and that protect your privacy."

In a blog post, Apple says that the Foundation Models framework has native support for Swift, Apple's programming language for building apps for its various platforms. The company claims developers can access Apple Intelligence models with as few as three lines of code. Guided generation, tool calling, and more are all built into the Foundation Models framework, according to Apple. Automattic is already using the framework in its Day One journaling app, Apple says, while mapping app AllTrails is tapping the framework to recommend different hiking routes.

An anonymous reader quotes a report from Ars Technica: OpenAI is now fighting a court order (PDF) to preserve all ChatGPT user logs—including deleted chats and sensitive chats logged through its API business offering -- after news organizations suing over copyright claims accused the AI company of destroying evidence. "Before OpenAI had an opportunity to respond to those unfounded accusations, the court ordered OpenAI to 'preserve and segregate all output log data that would otherwise be deleted on a going forward basis until further order of the Court (in essence, the output log data that OpenAI has been destroying)," OpenAI explained in a court filing (PDF) demanding oral arguments in a bid to block the controversial order.

In the filing, OpenAI alleged that the court rushed the order based only on a hunch raised by The New York Times and other news plaintiffs. And now, without "any just cause," OpenAI argued, the order "continues to prevent OpenAI from respecting its users' privacy decisions." That risk extended to users of ChatGPT Free, Plus, and Pro, as well as users of OpenAI's application programming interface (API), OpenAI said. The court order came after news organizations expressed concern that people using ChatGPT to skirt paywalls "might be more likely to 'delete all [their] searches' to cover their tracks," OpenAI explained. Evidence to support that claim, news plaintiffs argued, was missing from the record because so far, OpenAI had only shared samples of chat logs that users had agreed that the company could retain. Sharing the news plaintiffs' concerns, the judge, Ona Wang, ultimately agreed that OpenAI likely would never stop deleting that alleged evidence absent a court order, granting news plaintiffs' request to preserve all chats.

OpenAI argued the May 13 order was premature and should be vacated, until, "at a minimum," news organizations can establish a substantial need for OpenAI to preserve all chat logs. They warned that the privacy of hundreds of millions of ChatGPT users globally is at risk every day that the "sweeping, unprecedented" order continues to be enforced. "As a result, OpenAI is forced to jettison its commitment to allow users to control when and how their ChatGPT conversation data is used, and whether it is retained," OpenAI argued. Meanwhile, there is no evidence beyond speculation yet supporting claims that "OpenAI had intentionally deleted data," OpenAI alleged. And supposedly there is not "a single piece of evidence supporting" claims that copyright-infringing ChatGPT users are more likely to delete their chats. "OpenAI did not 'destroy' any data, and certainly did not delete any data in response to litigation events," OpenAI argued. "The Order appears to have incorrectly assumed the contrary." One tech worker on LinkedIn suggested the order created "a serious breach of contract for every company that uses OpenAI," while privacy advocates on X warned, "every single AI service 'powered by' OpenAI should be concerned."

Also on LinkedIn, a consultant rushed to warn clients to be "extra careful" sharing sensitive data "with ChatGPT or through OpenAI's API for now," warning, "your outputs could eventually be read by others, even if you opted out of training data sharing or used 'temporary chat'!"

Morgan Stanley has deployed an in-house AI tool called DevGen.AI that has reviewed nine million lines of legacy code this year, saving the investment bank's developers an estimated 280,000 hours by translating outdated programming languages into plain English specifications that can be rewritten in modern code.

The tool, built on OpenAI's GPT models and launched in January, addresses what Mike Pizzi, the company's global head of technology and operations, calls one of enterprise software's biggest pain points -- modernizing decades-old code that weakens security and slows new technology adoption. While commercial AI coding tools excel at writing new code, they lack expertise in older or company-specific programming languages like Cobol, prompting Morgan Stanley to train its own system on its proprietary codebase.

The tool's primary strength, the bank said, lies in creating English specifications that map what legacy code does, enabling any of the company's 15,000 developers worldwide to rewrite it in modern programming languages rather than relying on a dwindling pool of specialists familiar with antiquated coding systems.

Code generation startups are attracting extraordinary investor interest two years after ChatGPT's launch, with companies like Cursor raising $900 million at a $10 billion valuation despite operating with negative gross margins. OpenAI is reportedly in talks to acquire Windsurf, maker of the Codeium coding tool, for $3 billion, while the startup generates $50 million in annualized revenue from a product launched just seven months ago.

These "vibe coding" platforms allow users to write software using plain English commands, attempting to fundamentally change how code gets written. Cursor went from zero to $100 million in recurring revenue in under two years with just 60 employees, though both major startups spend more money than they generate, Reuters reports, citing investor sources familiar with their operations.

The surge comes as major technology giants report significant portions of their code now being AI-generated -- Google claims over 30% while Microsoft reports 20-30%. Meanwhile, entry-level programming positions have declined 24% as companies increasingly rely on AI tools to handle basic coding tasks previously assigned to junior developers.

A new analysis argues that Stack Overflow's decline began years before AI tools delivered the "final blow" to the once-dominant programming forum. The site's monthly questions dropped from a peak of 200,000 to a steep collapse that began in earnest after ChatGPT's 2023 launch, but usage had been declining since 2014, according to data cited in the InfoWorld analysis.

The platform's remarkable reputation system initially elevated it above competitors by allowing users to earn points and badges for helpful contributions, but that same system eventually became its downfall, the piece argues. As Stack Overflow evolved into a self-governing platform where high-reputation users gained moderation powers, the community transformed from a welcoming space for developer interaction into what the author compares to a "Stanford Prison Experiment" where moderators systematically culled interactions they deemed irrelevant.

Stack Overflow remains in the midst of big changes to counter an AI-fueled drop in engagement. So "We're wondering what kind of online communities Stack Overflow users continue to support in the age of AI," writes their senior analyst, "and whether AI is becoming a closer companion than ever before."

For their 15th year of their annual reader survey, this means "we're not just collecting data; we're reflecting on the last year of questions, answers, hallucinations, job changes, tech stacks, memory allocations, models, systems and agents — together..." Is it an AI agent revolution yet? Are you building or utilizing AI agents? We want to know how these intelligent assistants are changing your daily workflow and if developers are really using them as much as these keynote speeches assume. We're asking if you are using these tools and where humans are still needed for common developer tasks.

Career shifts: We're keen to understand if you've considered a career change or transitioned roles and if AI is impacting your approach to learning or using existing tools. Did we make up the difference in salaries globally for tech workers...?
They're also re-visiting "a key finding from recent surveys highlighted a significant statistic: 80% of developers reported being unhappy or complacent in their jobs." This raised questions about changing office (and return-to-office) culture and the pressures of the industry, along with whether there were any insights into what could help developers feel more satisfied at work. Prior research confirmed that flexibility at work used to contribute more than salary to job satisfaction, but 2024's results show us that remote work is not more impactful than salary when it comes to overall satisfaction... [For some positions job satisfaction stayed consistent regardless of salary, though it increased with salary for other positions. And embedded developers said their happiness increased when they worked with top-quality hardware, while desktop developers cited "contributing to open source" and engineering managers were happier when "driving strategy".]

In 2024, our data showed that many developers experienced a pay cut in various roles and programming specialties. In an industry often seen as highly lucrative, this was a notable shift of around 7% lower salaries across the top ten reporting countries for the same roles. This year, we're interested in whether this trend has continued, reversed, or stabilized. Salary dynamics is an indicator for job satisfaction in recent surveys of Stack Overflow users and understanding trends for these roles can perhaps improve the process for finding the most useful factors contributing to role satisfaction outside of salary.
And of course they're asking about AI — while noting last year's survey uncovered this paradox. "While AI usage is growing (70% in 2023 vs. 76% in 2024 planning to or currently using AI tools), developer sentiment isn't necessarily following suit, as 77% in of all respondents in 2023 are favorable or very favorable of AI tools for development compared to 72% of all respondents in 2024." Concerns about accuracy and misinformation were prevalent among some key groups. More developers learning to code are using or are interested in using AI tools than professional developers (84% vs. 77%)... Developers with 10 — 19 years experience were most likely (84%) to name "increase in productivity" as a benefit of AI tools, higher than developers with less experience (<80%)...

Is it an AI agent revolution yet? Are you building or utilizing AI agents? We want to know how these intelligent assistants are changing your daily workflow and if developers are really using them as much as these keynote speeches assume. We're asking if you are using these tools and where humans are still needed for common developer tasks.

AI company Anthropic (founded in 2021 by a team that left OpenAI) is now making about $3 billion a year in revenue, reports Reuters (citing "two sources familiar with the matter.") The sources said December's projections had been for just $1 billion a year, but it climbed to $2 billion by the end of March (and now to $3 billion) — a spectacular growth rate that one VC says "has never happened." A key driver is code generation. The San Francisco-based startup, backed by Google parent Alphabet and Amazon, is famous for AI that excels at computer programming. Products in the so-called codegen space have experienced major growth and adoption in recent months, often drawing on Anthropic's models.
Anthropic sells AI models as a service to other companies, according to the article, and Reuters calls Anthropic's success "an early validation of generative AI use in the business world" — and a long-awaited indicator that it's growing. (Their rival OpenAI earns more than half its revenue from ChatGPT subscriptions and "is shaping up to be a consumer-oriented company," according to their article, with "a number of enterprises" limiting their rollout of ChatGPT to "experimentation.")

Then again, in February OpenAI's chief operating officer said they had 2 million paying enterprise users, roughly doubling from September, according to CNBC. The latest figures from Reuters...

• Anthropic's valuation: $61.4 billion.
• OpenAI's valuation: $300 billion.

A 21-year-old's startup got a $500,000 investment from Y Combinator — after building their web site and prototype mostly with "vibe coding".

NPR explores vibe coding with Tom Blomfield, a Y Combinator group partner: "It really caught on, this idea that people are no longer checking line by line the code that AI is producing, but just kind of telling it what to do and accepting the responses in a very trusting way," Blomfield said. And so Blomfield, who knows how to code, also tried his hand at vibe coding — both to rejig his blog and to create from scratch a website called Recipe Ninja. It has a library of recipes, and cooks can talk to it, asking the AI-driven site to concoct new recipes for them. "It's probably like 30,000 lines of code. That would have taken me, I don't know, maybe a year to build," he said. "It wasn't overnight, but I probably spent 100 hours on that."

Blomfield said he expects AI coding to radically change the software industry. "Instead of having coding assistance, we're going to have actual AI coders and then an AI project manager, an AI designer and, over time, an AI manager of all of this. And we're going to have swarms of these things," he said. Where people fit into this, he said, "is the question we're all grappling with." In 2021, Blomfield said in a podcast that would-be start-up founders should, first and foremost, learn to code. Today, he's not sure he'd give that advice because he thinks coders and software engineers could eventually be out of a job. "Coders feel like they are tending, kind of, organic gardens by hand," he said. "But we are producing these superhuman agents that are going to be as good as the best coders in the world, like very, very soon."
The article includes an alternate opinion from Adam Resnick, a research manager at tech consultancy IDC. "The vast majority of developers are using AI tools in some way. And what we also see is that a reasonably high percentage of the code output from those tools needs further curation by people, by experienced people."

NPR ends their article by noting that this further curation is "a job that AI can't do, he said. At least not yet."

DevNull127 writes: Stack Overflow will test paying experts to answer questions. That's one of many radical experiments they're now trying to stave off an AI-induced death spiral. Questions and answers to the site have plummeted more than 90% since April of 2020. So here's what Stack Overflow will try next.

1. They're bringing back Chat, according to their CEO (to foster "even more connections between our community members" in "an increasingly AI-driven world").

2. They're building a "new Stack Overflow" meant to feel like a personalized portal. "It might collect videos, blogs, Q&A, war stories, jokes, educational materials, jobs... and fold them together into one personalized destination."

3. They're proposing areas more open to discussion, described as "more flexible Stack Exchanges... where users can explore ideas or share opinions."

4. They're also licensing Stack Overflow content to AI companies for training their models.

5. Again, they will test paying experts to answer questions.

Amazon software engineers are reporting that AI tools are transforming their jobs into something resembling the company's warehouse work, with managers pushing faster output and tighter deadlines while teams shrink in size, according to the New York Times.

Three Amazon engineers told the New York Times that the company has raised productivity goals over the past year and expects developers to use AI assistants that suggest code snippets or generate entire program sections. One engineer said his team was cut roughly in half but still expected to produce the same amount of code by relying on AI tools.

The shift mirrors historical workplace changes during industrialization, the Times argues, where technology didn't eliminate jobs but made them more routine and fast-paced. Engineers describe feeling like "bystanders in their own jobs" as they spend more time reviewing AI-generated code rather than writing it themselves. Tasks that once took weeks now must be completed in days, with less time for meetings and collaborative problem-solving, according to the engineers.