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

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

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

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

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

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

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

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

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

Google computer scientists have been using LLMs to streamline internal code migrations, achieving significant time savings of up to 89% in some cases. The findings appear in a pre-print paper titled "How is Google using AI for internal code migrations?" The Register reports: Their focus is on bespoke AI tools developed for specific product areas, such as Ads, Search, Workspace and YouTube, instead of generic AI tools that provide broadly applicable services like code completion, code review, and question answering. Google's code migrations involved: changing 32-bit IDs in the 500-plus-million-line codebase for Google Ads to 64-bit IDs; converting its old JUnit3 testing library to JUnit4; and replacing the Joda time library with Java's standard java.time package. The int32 to int64 migration, the Googlers explain, was not trivial as the IDs were often generically defined (int32_t in C++ or Integer in Java) and were not easily searchable. They existed in tens of thousands of code locations across thousands of files. Changes had to be tracked across multiple teams and changes to class interfaces had to be considered across multiple files. "The full effort, if done manually, was expected to require hundreds of software engineering years and complex crossteam coordination," the authors explain.

For their LLM-based workflow, Google's software engineers implemented the following process. An engineer from Ads would identify an ID in need of migration using a combination of code search, Kythe, and custom scripts. Then an LLM-based migration toolkit, triggered by someone knowledgeable in the art, was run to generate verified changes containing code that passed unit tests. Those changes would be manually checked by the same engineer and potentially corrected. Thereafter, the code changes would be sent to multiple reviewers who are responsible for the portion of the codebase affected by the changes. The result was that 80 percent of the code modifications in the change lists (CLs) were purely the product of AI; the remainder were either human-authored or human-edited AI suggestions.

"We discovered that in most cases, the human needed to revert at least some changes the model made that were either incorrect or not necessary," the authors observe. "Given the complexity and sensitive nature of the modified code, effort has to be spent in carefully rolling out each change to users." Based on this, Google undertook further work on LLM-driven verification to reduce the need for detailed review. Even with the need to double-check the LLM's work, the authors estimate that the time required to complete the migration was reduced by 50 percent. With LLM assistance, it took just three months to migrate 5,359 files and modify 149,000 lines of code to complete the JUnit3-JUnit4 transition. Approximately 87 percent of the code generated by AI ended up being committed with no changes. As for the Joda-Java time framework switch, the authors estimate a time saving of 89 percent compared to the projected manual change time, though no specifics were provided to support that assertion.

An anonymous reader quotes a report from The Guardian: People who get their coffee hit in the morning reap benefits that are not seen in those who have shots later in the day, according to the first major study into the health benefits of the drink at different times. Analysis of the coffee consumption of more than 40,000 adults found that morning coffee drinkers were 16% less likely to die of any cause and 31% less likely to die from cardiovascular disease during a 10-year follow-up period than those who went without. But the benefits to heart health appeared to vanish in people who drank coffee throughout the day, the researchers found, with medical records showing no significant reduction in mortality for all-day drinkers compared with those who avoided coffee. [...]

The study suggests that a morning dose of coffee is better for the heart than an evening one, but it does not explain why. One possible explanation is that drinking coffee later in the day can disrupt circadian rhythms and levels of hormones such as melatonin. This in turn affects sleep, inflammation and blood pressure, all of which can harm heart health. In an accompanying editorial, Prof Thomas Luscher, a consultant cardiologist at the Royal Brompton and Harefield hospitals in London, notes that many all-day drinkers sleep poorly, adding that coffee seems to suppress melatonin, a hormone that is important for inducing sleep in the brain. The effects are driven largely by caffeine, but coffee contains hundreds of other bioactive compounds that affect our physiology. The researchers say some substances in the blood that drive inflammation often peak in the morning and could be countered by anti-inflammatory compounds in a morning coffee. "This explanation applies to both caffeinated and decaffeinated coffee," they write. "Overall, we must accept the now substantial evidence that coffee drinking, particularly in the morning hours, is likely to be healthy," Luscher writes. "Thus, drink your coffee, but do so in the morning!"

The study has been published in the European Heart Journal.

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

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

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

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

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

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

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

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

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

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

Not every tech "advent calendar" involves programming puzzles. Instead the geek tradition of programming-language advent calendars "seems to have started way back in 2000," according to one history, "when London-based programmer Mark Fowler launched a calendar highlighting a different Perl module each day."

So the tradition continues...

• Nearly a quarter of a century later, there's still a Perl Advent Calendar, celebrating tips and tricks like "a few special packages waiting under the tree that can give your web applications a little extra pep in their step."

• And of course there's a separate advent calendar for Raku programmers.

• Since 2009 web performance consultant (and former Yahoo and Facebook engineer) Stoyan Stefanov has been pulling together an annual Web Performance calendar with helpful blog posts.

• There's also a JVM Advent calendar with daily helpful hints for Java programmers.

• Another advent calendar promises daily posts about C#.

• The HTMHell site — which bills itself as "a collection of bad practices in HTML, copied from real websites" — is celebrating the season with the "HTMHell Advent Calendar," promising daily articles on security, accessibility, UX, and performance.

• There's even an advent calendar with tips for the reverse-engineering framework Radar.

• There's still a lovely web-design themed calendar at Designcember.com.

And meanwhile developers at the Svelte frontend framework are actually promising to release something new each day, "whether it's a new feature in Svelte or SvelteKit or an improvement to the website!"

But not every tech advent calendar is about programming...

• Adafruit's managing director is publishing a Retrocomputing Advent Calendar — daily looks at the ghosts of computers past.

• The Atlantic continues its 17-year tradition of a Space Telescope advent calendar, featuring daily images from both NASA's Hubble telescope and James Webb Space Telescope

• The gaming blog Rock Paper Shotgun has been counting down their favorite games of 2024...

An anonymous reader quotes a report from VICE: It's early on a Sunday morning in late 1994, and you're shuffling your way through Fitzrovia in Central London, bloodstream still rushing after a long night at Bagley's. The sun comes up as you come down. You navigate side streets that you know like the back of your hand. But your hand's stamped with a party logo. And your brain's kaput. Coffee... yes, coffee. Good idea. Suddenly, you find yourself outside a teal blue cafe. Walking in is like entering an alien world; rows of club kids, tech heads, and game developers sit in front of desktops, lost in the primitive version of some new reality. Tentacular cables hang from the ceiling. Ambient techno reverberates from wall to wall. Cigarette smoke fills the air.

Welcome to Cyberia, the world's first internet cafe. Which, if you're too young to remember, are basically cafes with computers in them. It all began when Eva Pascoe, a Polish computing student living in London, crossed paths with Tim Berners Lee and other early internet mavericks at the dawn of the 90s. "I was very interested in cyberfeminism and wanted to figure out how women could reclaim tech," she recalls. The internet was still in its infancy. Diabolically slow dial-up modems only emerged around 1992; the World Wide Web was a pipe dream until 1993 and hardly anyone had the internet at home. But there wasn't just a lack of javascript; Eva remembers there being no good java, either. "There were no coffee shops in London," she says, which today seems ludicrous. "Just greasy spoons and everyone drank tea. I wanted a European-style cafe."

Linking up with like-minded pioneers David Rowe and husband and wife Keith and Gene Teare, Eva found a spot on the corner of Whitfield Street and launched Cyberia there in 1994. With Hackers-style aesthetics and futuristic furniture, it was based around a U-shaped layout that meant visitors could see each other's screens. "I wanted women to feel safe, because a lot of the stuff on the net was dodgy," she explains. Many of Eva's mates chipped in to help out -- architects, interior designers, graphic artists, publishers, and ravers among them.

And then there was the Amish community in Pennsylvania. Eva had to fly out there to negotiate for the "Cyberia.com" domain name they had bought. "It was a proper barn with horse carts and a wall of modems as they were running a bulletin board and an early ecommerce company. Apparently, there was always one family nominated to be the tech support," she remembers. Back in London, Cyberia quickly became a hotspot. "Virtually the second we opened, we had three lines deep around the block," she says. It's hard to imagine, but nowhere else in the world was doing what they were doing. It was the world's first cybercafe. "If you wanted to collect your emails, we were the only place in town," Eva says. Cyberia opened around 20 cafes worldwide, including branches in Bangkok, Paris, and Rotterdam. "For a fleeting moment it became like a sexier version of Richard Branson's Virgin empire: there was Cyberia Records, Cyberia Channel (a pioneering streaming service), Cyberia Payments, the Cyberia magazine, a Cyberia show on UK TV -- even a Cyberia wedding," writes VICE's Kyle MacNeill. He attended Cyberia's 30th birthday party in September and spoke with some of the cafe's original innovators, "shooting the shit about the good times and the not-so-good coffee."

The Tiobe index tries to track the popularity of programming languages by counting the number of search results for the language's name followed by the word "programming" (on 25 different search engines). And this month there were some surprises...

By TIOBE's reckoning, compared to a year ago PHP has now fallen from #7 to #12, while Delphi/Object Pascal shot up five spots from #16 to #11. In that same year, Fortran jumped from #12 to #8 — while both Visual Basic and SQL dropped down a single rank. Toward the top of the list, C actually fell from the #2 spot over the last 12 months to the #4 spot.

And Go just reached the #7 rank on the TIOBE's ranking of programming language popularity — "an all time high for Go," according to TIOBE CEO Paul Jansen. In this month's note, he explains what he thinks is unusual about this — starting by saying that Go programs are both fast, and easy in many ways — easy to deploy, easy to learn, and easy to understand. Python for instance is easy to learn but not fast, and deployment for larger Python programs is fragile due to dependencies on all kind of versioned libraries in the environment.

If compared to Rust for instance (another contender for a top position), Go is a tiny bit slower, but the Go programs are much easier to understand. The next hurdle for Go in the TIOBE index is JavaScript at position #6. That will be a tough one to pass. JavaScript is ubiquitous in software development, although for larger JavaScript systems we see a shift to TypeScript nowadays.
"If annual trends continue this way, Go will bypass JavaScript within 3 years," TIOBE's CEO predicts. (Adding "Let's see what the future has in store for Go...") Although the Go team actually has specific plans for the future, according to a blog post this week celebrating Go's 15th anniversary: We're working on making Go better for AI — and AI better for Go — by enhancing Go's capabilities in AI infrastructure, applications, and developer assistance. Go is a great language for building production systems, and we want it to be a great language for building production AI systems, too... For AI applications, we will continue building out first-class support for Go in popular AI SDKs, including LangChainGo and Genkit. And from its very beginning, Go aimed to improve the end-to-end software engineering process, so naturally we're looking at bringing the latest tools and techniques from AI to bear on reducing developer toil, leaving more time for the fun stuff — like actually programming!
TIOBE's top 10 programming language rankings for the month of November:

1. Python
2. C++
3. Java
4. C
5. C#
6. JavaScript
7. Go
8. Fortran
9. Visual Basic
10. SQL

"Java application security would be enhanced through two proposals aimed at resisting quantum computing attacks," reports InfoWorld, "one plan involving digital signatures and the other key encapsulation." The two proposals reside in the OpenJDK JEP (JDK Enhancement Proposal) index.

The Quantum-Resistant Module-Lattice-Based Digital Signature Algorithm proposal calls for enhancing the security of Java applications by providing an implementation of the quantum-resistant module-latticed-based digital signature algorithm (ML-DSA). ML-DSA would secure against future quantum computing attacks by using digital signatures to detect unauthorized modifications to data and to authenticate the identity of signatories. ML-DSA was standardized by the United States National Institute of Standards and Technology (NIST) in FIPS 204.

The Quantum-Resistant Module-Lattice-Based Key Encapsulation Mechanism proposal calls for enhancing application security by providing an implementation of the quantum-resistant module-lattice-based key encapsulation mechanism (ML-KEM). KEMs are used to secure symmetric keys over insecure communication channels using public key cryptography. ML-KEM is designed to be secure against future quantum computing attacks and was standardized by NIST in FIPS 203.

An anonymous reader quotes a report from Oregon Live: Intel told employees this week that it will bring back free coffee and tea at its work sites, one of many benefits the chipmaker eliminated last summer as it sought to slash $10 billion from its annual budget. "Although Intel still faces cost challenges, we understand that small comforts play a significant role in our daily routines," Intel wrote on its internal messaging forum, called Circuit. "We know this is a small step, but we hope it is a meaningful one in supporting our workplace culture." Intel declined comment. The company did not resume offering free fruit, another perk eliminated last summer. Employees say privately that morale has been devastated by Intel's poor financial performance and by cutbacks aimed at returning the business to profitability.

[...] Christy Pambianchi, Intel's chief people officer, told employees that Intel had been spending $100 million annually on free and discounted food and beverages and couldn't afford to keep doing that. "Until we get into a better financial health position, we need to be suspending those," Pambianchi said, according to an account of the meeting reviewed by The Oregonian/OregonLive. By Wednesday the company had reversed itself, committing to keep its employees caffeinated. In August, Intel announced plans to lay off over 16,000 employees, representing more than 15% of its global workforce. Its stock dropped to a 50-year low following the announcement. Starting November 8, Nvidia will replace the chipmaker on the Dow Jones Industrial Average.

GitHub released its annual "State of the Octoverse" report this week. And while "Systems programming languages, like Rust, are also on the rise... Python, JavaScript, TypeScript, and Java remain the most widely used languages on GitHub."

In fact, "In 2024, Python overtook JavaScript as the most popular language on GitHub." They also report usage of Jupyter Notebooks "skyrocketed" with a 92% jump in usage, which along with Python's rise seems to underscore "the surge in data science and machine learning on GitHub..." We're also seeing increased interest in AI agents and smaller models that require less computational power, reflecting a shift across the industry as more people focus on new use cases for AI... While the United States leads in contributions to generative AI projects on GitHub, we see more absolute activity outside the United States. In 2024, there was a 59% surge in the number of contributions to generative AI projects on GitHub and a 98% increase in the number of projects overall — and many of those contributions came from places like India, Germany, Japan, and Singapore...

Notable growth is occurring in India, which is expected to have the world's largest developer population on GitHub by 2028, as well as across Africa and Latin America... [W]e have seen greater growth outside the United States every year since 2013 — and that trend has sped up over the past few years.
Last year they'd projected India would have the most developers on GitHub #1 by 2027, but now believe it will happen a year later. This year's top 10?

1. United States
2. India
3. China
4. Brazil
5. United Kingdom
6. Russia
7. Germany
8. Indonesia
9. Japan
10. Canada

Interestingly, the UK's population ranks #21 among countries of the world, while Germany ranks #19, and Canada ranks #36.)

GitHub's announcement argues the rise of non-English, high-population regions "is notable given that it is happening at the same time as the proliferation of generative AI tools, which are increasingly enabling developers to engage with code in their natural language." And they offer one more data point: GitHub's For Good First Issue is a curated list of Digital Public Goods that need contributors, connecting those projects with people who want to address a societal challenge and promote sustainable development...

Significantly, 34% of contributors to the top 10 For Good Issue projects... made their first contribution after signing up for GitHub Copilot.
There's now 518 million projects on GitHub — with a year-over-year growth of 25%...

Boeing received an email from the chief pilot at Ethiopian Airlines on December 1, 2018 with several questions, reports the New York Times (alternate URL here). "in essence the pilot was asking for direction. If we see a series of warnings on the new 737 Max, he posed, what do we do?" What ensued was an email conversation among a number of Boeing senior officials about whether they could answer the pilot's questions without violating international restrictions on disseminating information about a crash while it was still under investigation. That restriction was in play because a 737 Max flown by Lion Air had crashed a few weeks earlier leaving Indonesia. The inquiry from Ethiopian Airlines would prove chillingly prescient because just months later one of its 737s would go down because of a flight control malfunction similar to the one that led to the Lion Air crash. The Ethiopian Airlines crash would kill everyone on board and leave questions about whether Boeing had done everything it could to inform pilots of what it had learned about the malfunction and how to handle it.

In response to the inquiry from Ethiopian Airlines, Boeing's chief pilot, Jim Webb, proposed to his colleagues that he thank the airline for attending a previous briefing on the flight control system, called MCAS, but otherwise decline to answer the pilot's first two questions and just refer the airline to training materials and previously issued guidance. Most of those on the email agreed.
Boeing's eventual response? "I can only address the current system and the Operations Manual Bulletin. The first two questions directly relate to the accident scenario; therefore, I will be unable to address them here." The Times adds that Boeing's chief pilot Jim Webb then "ended the email by stating that if airline officials had any additional questions about the bulletin and system, they should feel free to reach out....

"It is impossible to know whether any pilots with Ethiopian Arlines would have acted differently if Webb's reply had been more forthcoming. But Boeing's limited response to an airline seeking help highlights a missed opportunity to collaborate on safety and to pass along lessons Boeing had collected following the Lion Air jet's crash into the Java Sea on Oct. 29, 2018."

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

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

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

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

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

• PHP (dropping from #8 to #15)

• SQL (dropping from #9 to #11)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

theodp writes: Typos happen to the best of us, but spelling still counts when it comes to software development. So, it's kind of surprising to see that both Amazon CEO Andy Jassy and former AWS CEO Adam Selipsky failed to notice an embarrassing typo in a demo video they offered to their millions of followers on social media as evidence of Amazon Q AI's Java upgrade capabilities, which Amazon has been trumpeting for months in SEC filings, shareholder communication, and Amazon's latest earnings call with Wall Street analysts.

Just 37 seconds into the demo of the software that Amazon says saved it 4,500 developer-years of work and provided an additional $260M in annualized efficiency gains, Amazon Q kicks off the Java upgrade conversation by saying, "I can help you upgrade your Jave [sic] 8 and 11 codebases to Java 17." The embarrassing misspelling did prompt Twitter user @archo5dev to alert Jassy to the typo, but there's been no response yet from Jassy, who boasted that Amazon developers were unable to find any mistakes in Q's work in "79% of the auto-generated code reviews."

It's probably worth noting that both Jassy and Selipsky opted to showcase a drop-dead simple demo of Amazon Q Code Transformation rather than some of the lengthier and less-magical demos of the product.

Long-time Slashdot reader theodp shared this anecdote about Amazon's GenAI assistant for software development, Amazon Q: On Thursday, Amazon CEO Andy Jassy took to Twitter to boast that using Amazon Q to do Java upgrades has already saved Amazon from having to pay for 4,500 developer-years of work. ("Yes, that number is crazy but, real," writes Jassy). And Jassy says it also provided Amazon with an additional $260M in annualized efficiency gains from enhanced security and reduced infrastructure costs.

"Our developers shipped 79% of the auto-generated code reviews without any additional changes," Jassy explained. "This is a great example of how large-scale enterprises can gain significant efficiencies in foundational software hygiene work by leveraging Amazon Q."

Jassy — who FORTUNE reported had no formal training in computer science — also touted Amazon Q's Java upgrade prowess in his Letter to Shareholders earlier this year, as has Amazon in its recent SEC filings ("today, developers can save months using Q to move from older versions of Java to newer, more secure and capable ones; in the near future, Q will help developers transform their .net code as well"). Earlier this week, Business Insider reported on a leaked recording of a fireside chat in which AWS CEO Matt Garman predicted a paradigm shift in coding as a career in the foreseeable future with the prevalence of AI. According to Garman, "If you go forward 24 months from now, or some amount of time — I can't exactly predict where it is — it's possible that most developers are not coding."

The Japanese government has released details of an app that verifies the legitimacy of its troubled My Number Card -- a national identity document. From a report: Beginning in 2015, every resident of Japan was assigned a 12 digit My Number that paved the way for linking social security, taxation, disaster response and other government services to both the number itself and a smartcard. The plan was to banish bureaucracy and improve public service delivery -- but that didn't happen.

My Number Card ran afoul of data breaches, reports of malfunctioning card readers, and database snafus that linked cards to other citizens' bank accounts. Public trust in the scheme fell, and adoption stalled. Now, according to Japan's Digital Ministry, counterfeit cards are proliferating to help miscreant purchase goods -- particularly mobile phones -- under fake identities. Digital minister Taro Kono yesterday presented his solution to the counterfeits: a soon to be mandatory app that confirms the legitimacy of the card. The app uses the camera on a smartphone to read information printed on the card -- like date of birth and name. It compares those details to what it reads from info stored in the smartcard's resident chip, and confirms the data match without the user ever needing to enter their four-digit PIN.

An anonymous reader quotes a report from The Conversation, written by Michael W. Crowder, Professor of Chemistry and Biochemistry and Dean of the Graduate School at Miami University: For many people, the aroma of freshly brewed coffee is the start of a great day. But caffeine can cause headaches and jitters in others. That's why many people reach for a decaffeinated cup instead. I'm a chemistry professor who has taught lectures on why chemicals dissolve in some liquids but not in others. The processes of decaffeination offer great real-life examples of these chemistry concepts. Even the best decaffeination method, however, does not remove all of the caffeine -- about 7 milligrams of caffeine usually remain in an 8-ounce cup. Producers decaffeinating their coffee want to remove the caffeine while retaining all -- or at least most -- of the other chemical aroma and flavor compounds.

Decaffeination has a rich history, and now almost all coffee producers use one of three common methods. All these methods, which are also used to make decaffeinated tea, start with green, or unroasted, coffee beans that have been premoistened. Using roasted coffee beans would result in a coffee with a very different aroma and taste because the decaffeination steps would remove some flavor and odor compounds produced during roasting. Here's a summary of each method discussed by Dr. Crowder:

The Carbon Dioxide Method: Developed in the early 1970s, the carbon dioxide method uses high-pressure CO2 to extract caffeine from moistened coffee beans, resulting in coffee that retains most of its flavor. The caffeine-laden CO2 is then filtered out using water or activated carbon, removing 96% to 98% of the caffeine with minimal CO2 residue.

The Swiss Water Process: First used commercially in the early 1980s, the Swiss water method uses hot water and activated charcoal filters to decaffeinate coffee, preserving most of its natural flavor. This chemical-free approach removes 94% to 96% of the caffeine by soaking the beans repeatedly until the desired caffeine level is achieved.

Solvent-Based Methods: Originating in the early 1900s, solvent-based methods use organic solvents like ethyl acetate and methylene chloride to extract caffeine from green coffee beans. These methods remove 96% to 97% of the caffeine through either direct soaking in solvent or indirect treatment of water containing caffeine, followed by steaming and roasting to ensure safety and flavor retention.

"It's chemically impossible to dissolve out only the caffeine without also dissolving out other chemical compounds in the beans, so decaffeination inevitably removes some other compounds that contribute to the aroma and flavor of your cup of coffee," writes Dr. Crowder in closing. "But some techniques, like the Swiss water process and the indirect solvent method, have steps that may reintroduce some of these extracted compounds. These approaches probably can't return all the extra compounds back to the beans, but they may add some of the flavor compounds back."