"Every day, 3 trillion dollars worth of transactions are handled by a 64-year-old programming language that hardly anybody knows anymore," writes PC Magazine. But most school's don't teach the mainframe programming language COBOL any more, and "COBOL cowboys" are aging out of the workforce, with replacements in short supply.

"This is precisely the kind of problem that IBM thinks it can fix with AI." IBM's approach is fairly straightforward: Rather than relying exclusively on a limited pool of human programmers to solve the problem, it built a generative AI-powered code assistant (watsonx) that helps convert all that dusty old COBOL code to a more modern language, thereby saving coders countless hours of reprogramming. In extremely simplified terms, the process is similar to feeding an essay written in English into ChatGPT and asking it to translate certain paragraphs into Esperanto. It allows programmers to take a chunk of COBOL and enlist watsonx to transform it into Java.

But of course, it's not quite that simple in practice... After IBM and the customer have a thorough understanding of the application landscape, the data flow, and the existing dependencies, "we help them refactor their applications," says IBM's Vice President of Product Management, IT Automation, Keri Olson. "That is, breaking it down into smaller pieces, which the customer can selectively choose, at that point, to do the modernization from COBOL to Java." Skyla Loomis, IBM's Vice President of IBM Z Software adds, "But you have to remember that this is a developer assistant tool. It's AI assisted, but it still requires the developer. So yes, the developer is involved with the tooling and helping the customers select the services."

Once the partnership between man and machine is established, the AI steps in and says, 'Okay, I want to transform this portion of code. The developer may still need to perform some minor editing of the code that the AI provides, Loomis explains. "It might be 80 or 90 percent of what they need, but it still requires a couple of changes. It's a productivity enhancement — not a developer replacement type of activity."
The article quotes a skeptical Gartner Distinguished Vice President and Analyst, who notes that IBM "has no case studies, at this time, to validate its claims."

The Go team conducted a survey of Go Developers in August — and has just released the results. Among the findings: "90% of survey respondents saying they felt satisfied while working with Go during the prior year," while 6% said they were dissastified. Further, the number of people working with Go continues to increase; we see evidence of this from external research like Stack Overflow's Developer Survey (which found 14% of professional developers worked with Go during the past year, a roughly 15% year-over-year increase), as well as analytics for go.dev (which show an 8% rise in visitors year-over-year). Combining this growth with a high satisfaction score is evidence that Go continues to appeal to developers, and suggests that many developers who choose to learn the language feel good about their decision long afterwards...

As in prior years, the majority of survey respondents told us they work with Go on Linux (63%) and macOS (58%) systems... We do continue to see that newer members of the Go community are more likely to be working with Windows than more experienced Go developers. We interpret this as a signal that Windows-based development is important for onboarding new developers to the Go ecosystem, and is a topic our team hopes to focus on more in 2024...

While x86-compatible systems still account for the majority of development (89%), ARM64 is also now used by a majority of respondents (56%). This adoption appears to be partly driven by Apple Silicon; macOS developers are now more likely to say they develop for ARM64 than for x86-based architectures (76% vs. 71%). However, Apple hardware isn't the only factor driving ARM64 adoption: among respondents who don't develop on macOS at all, 29% still say they develop for ARM64.
The most-preferred code editors among the surveyed Go programmers were VS Code (44%), GoLand (31%), Vim/Neovim (16%), and Emacs (3%). 52% of the survey's respondents actually selected "very satisfied" for their feelings about Go — the highest possible rating.

Other interesting findings:

• " The top requests for improving toolchain warnings and errors were to make the messages more comprehensible and actionable; this sentiment was shared by developers of all experience levels, but was particularly strong among newer Go developers."

• "Three out of every four respondents work on Go software that also uses cloud services; this is evidence that developers see Go as a language for modern, cloud-based development."

• The experimental gonew tool (which offers predefined templates for instantiating new Go projects) "appears to solve critical problems for Go developers (especially developers new to Go) and does so in a way that matches their existing workflows for starting a new project. Based on these findings, we believe gonew can substantially reduce onboarding barriers for new Go developers and ease adoption of Go in organizations."

• And when it comes to AI, "Go developers said they are more interested in AI/ML tooling that improves the quality, reliability, and performance of code they write, rather than writing code for them."

AI coding assistants are here to stay -- but just how big a difference they make is still unclear. From a report: Thomas Dohmke, GitHub's CEO: "You've got a lot of tabs open, you're planning a vacation, maybe you're reading the news. At last you copy the text you need and go back to your code, but it's 20 minutes later and you lost the flow." The key idea behind Copilot and other programs like it, sometimes called code assistants, is to put the information that programmers need right next to the code they are writing.

The tool tracks the code and comments (descriptions or notes written in natural language) in the file that a programmer is working on, as well as other files that it links to or that have been edited in the same project, and sends all this text to the large language model behind Copilot as a prompt. (GitHub co-developed Copilot's model, called Codex, with OpenAI. It is a large language model fine-tuned on code.) Copilot then predicts what the programmer is trying to do and suggests code to do it. This round trip between code and Codex happens multiple times a second, the prompt updating as the programmer types. At any moment, the programmer can accept what Copilot suggests by hitting the tab key, or ignore it and carry on typing. The tab button seems to get hit a lot. A study of almost a million Copilot users published by GitHub and the consulting firm Keystone Strategy in June -- a year after the tool's general release -- found that programmers accepted on average around 30% of its suggestions, according to GitHub's user data.

[...] Copilot has changed the basic skills of coding. As with ChatGPT or image makers like Stable Diffusion, the tool's output is often not exactly what's wanted -- but it can be close. "Maybe it's correct, maybe it's not -- but it's a good start," says Arghavan Moradi Dakhel, a researcher at Polytechnique Montreal in Canada who studies the use of machine-learning tools in software development. Programming becomes prompting: rather than coming up with code from scratch, the work involves tweaking half-formed code and nudging a large language model to produce something more on point.

"Advent of Code" has begun. New programming puzzles will appear every day until Christmas at AdventOfCode.com — and the annual event (first started in 2015) has grown into a worldwide phenomenon. This year's first puzzle has been completed by over 150,000 programmers (with another 115,652 completing Day Two's puzzle). And 108,000 fans have also joined the Advent of Code subReddit.

Contest-related comments are popping up all around the web. Some participants are live streaming their puzzle-solving efforts on Twitch. Self-described computer nerd Gary Grady is tweeting cartoons about each day's puzzle. JetBrains is even giving away some prizes in their "Advent of Code with Kotlin" event. And JetBrains developer advocate Sebastian Aigner is also hosting daily livestreams about each puzzle.

It's hard to overstate how big this event has become. This year's event attracted 60 sponsors, including Kotlin (for the third consecutive year), as well as Spotify, Shopify, and Sony Interactive Entertainment (as well as JPMorgan Chase, Bank of America, and American Express). Individual donors can get a special badge next to their name, and there's also a shop selling coffee mugs and t-shirts. But at its core is real-world developer Eric Wastl (plus a team of loyal beta-testers) sharing his genuine fondness for computer programming. Wastl is also the creator of a satirical web page for the fast, lightweight, cross-platform framework Vanilla JS ("so popular that browsers have been automatically loading it for over a decade") and also curates a collection of "things in PHP which make me sad".

And you can find him on X sharing encouraging comments for this year's participants.

"Structured concurrency is a new way to use multithreading in Java," reports InfoWorld.

"It allows developers to think about work in logical groups while taking advantage of both traditional and virtual threads." Available in preview in Java 21, structured concurrency is a key aspect of Java's future, so now is a good time to start working with it... Java's thread model makes it a strong contender among concurrent languages, but multithreading has always been inherently tricky. Structured concurrency allows you to use multiple threads with structured programming syntax. In essence, it provides a way to write concurrent software using familiar program flows and constructs. This lets developers focus on the business at hand, instead of the orchestration of threading.

As the JEP for structured concurrency says, "If a task splits into concurrent subtasks then they all return to the same place, namely the task's code block." Virtual threads, now an official feature of Java, create the possibility of cheaply spawning threads to gain concurrent performance. Structured concurrency provides the simple syntax to do so. As a result, Java now has a unique and highly-optimized threading system that is also easy to understand...

Between virtual threads and structured concurrency, Java developers have a compelling new mechanism for breaking up almost any code into concurrent tasks without much overhead... Any time you encounter a bottleneck where many tasks are occurring, you can easily hand them all off to the virtual thread engine, which will find the best way to orchestrate them. The new thread model with structured concurrency also makes it easy to customize and fine-tune this behavior. It will be very interesting to see how developers use these new concurrency capabilities in our applications, frameworks, and servers going forward.
It involves a new class StructuredTaskScope located in the java.util.concurrent library. (InfoWorld points out that "you'll need to use --enable-preview and --source 21 or --source 22 to enable structured concurrency.")

Their reporter shared an example on GitHub, and there's more examples in the Java 21 documentation. "The structured concurrency documentation includes an example of collecting subtask results as they succeed or fail and then returning the results."

The Linux Foundation's own "Open Software Security foundation" has an associated project called Alpha-Omega funded by Microsoft, Google, and Amazon with a mission to catalyze sustainable security improvements to critical open source projects and ecosystems.

It was established nearly two years ago in February of 2022 — and this month announced plans to continue supporting the Rust Foundation Security Initiative: 2022 was also the first full year of operation for the Rust Foundation — an independent nonprofit dedicated to stewarding the Rust programming language and supporting its global community. Given the considerable growth and rising popularity of the Rust programming language in recent years, it has never been more critical to have a healthy and well-funded foundation in place to help ensure the safety and security of this important language.

When the Rust Foundation emerged, OpenSSF recognized a shared vision of global open source security baked into their organizational priorities from day one. These shared security values were the driving force behind Alpha-Omega's decision to grant $460k USD to the Rust Foundation in 2022. This funding helped underwrite their Security Initiative — a program dedicated to improving the state of security within the Rust programming language ecosystem and sowing security best practices within the Rust community. The Security Initiative began in earnest this past January and has now been in operation for a full year with many achievements to note and exciting plans in development.

While security is a clear priority of the Rust language itself and can be seen in its memory safety-critical features, the Rust Project cannot reasonably be expected to foster long term, sustainable security without proper support and funding. Indeed, there is still a pervasive attitude across technology that cybersecurity is being managed and prioritized by "someone else." The unfortunate impact of this attitude is that critical security work often falls on overburdened and under-resourced open source maintainers. By prioritizing the Security Initiative during their first full year in operation, the Rust Foundation has taken on the responsibility of overseeing — and supporting — security improvements within the Rust ecosystem while ensuring meaningful progress...

Alpha-Omega is excited to announce our second year of supporting the Rust Foundation Security Initiative. We believe that this funding will build on the good work and momentum established by the Rust Foundation in 2023. Through this partnership, we are helping relieve maintainer burdens while paving an important path towards a healthier and more secure future within the Rust ecosystem.
Meanwhile, this month the Rust Foundation announced that downloads from Rust's package repository crates.io have now reached 45 billion — and that the foundation is "committed to facilitating the healthy growth of Rust through funding and resources for the community and the Project.

"After conducting initial planning and research and getting approval from our board of directors, we are pleased to announce our intention to help fulfill this commitment by developing a Rust Foundation training and certification program." We continue to be supportive of anyone creating Rust training and education materials. In fact, we are proud to have provided funding to a few individuals involved in this work via our Community Grants Program. Our team is also aware that commercial Rust training courses already exist and that global training entities are already developing their own Rust-focused programs. Given the value of Rust in professional open source, this makes sense. However, we are eager to introduce a program that will allow us to direct profits back into the Rust ecosystem.

As a nonprofit organization, we sit in a unique position thanks to the tools, connections, insights, administrative support, and resources at our disposal — all of which will add value to course material aimed at professional development and adoption. We see our forthcoming program as one tool of many that can be used to verify skills for prospective employers, and for those employers to build out their professional teams of Rust expertise. We will remain supportive of existing training programs offered by Rust Foundation member companies and we'll look for ways to ensure this remains the case as program development progresses... There is no set launch date for the Rust Foundation training and certification program yet, but we plan to continue laying high-quality groundwork in Q4 of 2023 and the first half of 2024.

An anonymous reader quotes a report from Hackaday: The BBC has a long history of teaching the world about computers. The broadcaster's name was proudly displayed on the BBC Micro, and BBC Basic was the programming language developed especially for that computer. Now, BBC Basic is back and running on a whole mess of modern platforms. BBC Basic for SDL 2.0 will run on Windows, MacOS, x86 Linux, and even Raspberry Pi OS, Android, and iOS. Desktop versions of the programming environment feature a BASIC editor that has syntax coloring for ease of use, along with luxury features like search and replace that weren't always available at the dawn of the microcomputer era. Meanwhile, the smartphone versions feature a simplified interface designed to work better in a touchscreen environment.

It's weird to see, but BBC Basic can actually do some interesting stuff given the power of modern hardware. It can address up to 256 MB of memory, and work with far more advanced graphical assets than would ever have been possible on the original BBC Micro. If you honed your programming skills on that old metal, you might be impressed with what they can achieve with BBC Basic in a new, more powerful context.

Slashdot reader sysadminafterdark writes: Released on November 26, 2020, PHP 8 brought many optimizations and powerful features to the language.Fast forward to today, and PHP 8 is getting the boot in favor of 8.1, 8.2, and 8.3 with 8.4 in development. This leaves some websites at risk of breaking and potential security issues. Hearing of this news, I upgraded my own blog and wrote an article on how to add the Remi repository and update. I run Enterprise Linux (The best distro out there) so if you are standing up new boxes, just keep in mind the PHP in the repo is deprecated.

Long-time Slashdot reader destinyland writes: The Linux Foundation recently funded a new "security developer in residence" position for Python. (It's funded through the Linux Foundation's own "Open Software Security foundation", which has a stated mission of partnering with open source project maintainers "to systematically find new, as-yet-undiscovered vulnerabilities in open source code, and get them fixed to improve global software supply chain security.") The position went to the lead maintainer for the HTTP client library urllib3, the most downloaded package on the Python Package Index with over 10 billion downloads. But he hopes to create a ripple effect by demonstrating the impact of security investments in critical communities — ultimately instigating a wave of improvements to all software supply chains. (And he's also documenting everything for easy replication by other communities...)

So far he's improved the security of Python's release processes with signature audits and security-hardening automation. But he also learned that CVE numbers were being assigned to newly-discovered vulnerabilities by the National Cyber Security Division of the America's Department of Homeland Security — often without talking to anyone at the Python project. So by August he'd gotten the Python Software Foundation authorized as a CVE Numbering Authority, which should lead to more detailed advisories (including remediation information), now reviewed and approved by Python's security response teams.

"The Python Software wants to help other Open Source organizations, and will be sharing lessons learned," he writes in a blog post. And he now says he's already been communicating with the Curl program about his experiences to help them take the same step, and even authored a guide to the process for other open source projects.

GameMaker announced that it will be free to use for noncommercial, non-console projects, breaking away from Unity and its massive pricing controversy that saw game developers boycotting the engine. The company is also "eliminating its indie / creator tier monthly subscription fee in favor of a one-time paid licensing fee of $99," reports The Verge. "Additionally, if you're currently enrolled at the indie / creator tier and wish to pay the licensing fee, the subscription fees you've paid will be discounted from the price." The Verge: Russell Kay, head of GameMaker, said that the changes were a way for the company to express its thanks to users, explaining that, since 2021, GameMaker has seen its user base triple in size. Kay also had some subtle but effective shade for GameMaker's competitors. "We have seen other platforms making awkward moves with their pricing and terms, so we thought, what if we did the opposite, something that could actually be good for developers?" Kay wrote in the announcement.

Though customers currently enrolled in an enterprise-level subscription will see no changes to their plans, it seems like GameMaker is counting on the pricing update to draw more people to the software. "Our success is measured by the number of people making games!" Kay wrote.

Python "come with downsides," argues a new article in Communications of the ACM. "Its programs tend to run slowly, and because it is inefficient at running processes in parallel, it is not well suited to some of the latest AI programming."

"Hoping to overcome those difficulties, computer scientist Chris Lattner set out to create a new language, Mojo, which offers the ease of use of Python, but the performance of more complex languages such as C++ or Rust." Lattner tells the site "we don't want to break Python, we want to make Python better," while software architect Doug Meil says Mojo is essentially "Python for AI... and it's going to be way faster in scale across multiple hardware platforms." Lattner teamed up with Tim Davis, whom he had met when they both worked for Google, to form Modular in January 2022. The company, where Lattner is chief executive officer and Davis chief product officer, provides support for companies working on AI and is developing Mojo.

A modern AI programming stack generally has Python on top, Lattner says, but because that is an inefficient language, it has C++ underneath to handle the implementation. The C++ then must communicate with performance accelerators or GPUs, so developers add a platform such as Compute Unified Device Architecture (CUDA) to make efficient use of those GPUs. "Mojo came from the need to unify these three different parts of the stack so that we could build a unified solution that can scale up and down," Lattner says. The result is a language with the same syntax as Python, so people used to programming in Python can adopt it with little difficulty, but which, by some measures, can run up to 35,000 times faster. For AI, Mojo is especially fast at performing the matrix multiplications used in many neural networks because it compiles the multiplication code to run directly on the GPU, bypassing CUDA...

"Increasingly, code is not being written by computer programmers. It's being written by doctors and journalists and chemists and gamers," says Jeremy Howard, an honorary professor of computer science at the University of Queensland, Australia, and a co-founder of fast.ai, a. "All data scientists write code, but very few data scientists would consider themselves professional computer programmers." Mojo attempts to fill that need by being a superset of Python. A program written in Python can be copied into Mojo and will immediately run faster, the company says. The speedup comes from a variety of factors. For instance, Mojo, like other modern languages, enables threads, small tasks that can be run simultaneously, rather than in sequence. Instead of using an interpreter to execute code as Python does, Mojo uses a compiler to turn the code into assembly language.

Mojo also gives developers the option of using static typing, which defines data elements and reduces the number of errors... "Static behavior is good because it leads to performance," Lattner says. "Static behavior is also good because it leads to more correctness and safety guarantees."
Python creator Guido van Rossum "says he is interested to watch how Mojo develops and whether it can hit the lofty goals Lattner is setting for it..." according to the article, " but he emphasizes that the language is in its early stages and, as of July 2023, Mojo had not yet been made available for download."


In June, Lattner did an hour-long interview with the TWIML AI podcast. And in 2017 Chris Lattner answered questions from Slashdot's readers.

An anonymous reader shared this report from InfoWorld: JetBrains' Kotlin language, a Java rival endorsed by Google for Android mobile development, continues to scale up Tiobe's index of language popularity, reaching the 15th spot in the November 2023 rankings...

Software quality services company Tiobe cites Kotlin advantages including interoperability with Java and unrivaled Android accommodations as reasons for the language's rise. Kotlin, Tiobe CEO Paul Jansen said, also fits in with a modern programming culture of expressive languages that have a strong type system and avoid null pointer exceptions by design. "Based on my experience, I am pretty sure Kotlin can reach a top 10 position," Jansen said. It remains to be seen if it can ever scale as high as a top four slot, he added...

In the rival Pypl Popularity of Programming languages index this month, Kotlin was ranked 13th with a 1.76% share, having slipped slightly year-over-year.
Kotlin's rank on the TIOBE index rose three positions in the last month — after rising two positions the month before. TIOBE's CEO says the language has now achieved its highest ranking ever on the index, surpassing 2017's "first wave of Kotlin popularity...when Google announced first class support for Kotlin on Android."

Rust now ranks #20 on the index, behind Delphi/Object Pascal, Swift, Ruby, and R.

Here's TIOBE November rankings for top-20 most popular programming languages:

1. Python
2. C
3. C++
4. Java
5. C#
6. JavaScript
7. PHP
8. Visual Basic
9. SQL
10. Assembly Language
11. Scratch
12. Fortran
13. Go
14. MATLAB
15. Kotlin
16. Delphi/Object Pascal
17. Swift
18. Ruby
19. R
20. Rust

The Python community is staying busy.

• Three weeks ago a new podcast launched with Python core developer/steering council member Pablo Galindo and Python developer-in-residence Åukasz Langa.

• This month the Python Sofware Foundation announced its seventh Python Developers Survey.

• And for the fifth year in a row the foundation partnered with JetBrains to offer a 30% discount on JetBrains' dedicated Python Integrated Development Environment "PyCharm" — with all proceeds benefitting the Python Software Foundation ("The discount will be automatically applied when you check out. The promotion will only be available through November 27th...")
  
  A PSF blog post about the fundraiser notes you can also just make a direct donation to the Python Software Foundation, or sign up to become a foundation member to "help us sustain what we do."

An anonymous reader quotes a report from The Intercept: A joint project of Human Rights Watch and New York University to document human rights abuses in the Democratic Republic of the Congo has been taken offline after exposing the identities of thousands of vulnerable people, including survivors of mass killings and sexual assaults. The Kivu Security Tracker is a "data-centric crisis map" of atrocities in eastern Congo that has been used by policymakers, academics, journalists, and activists to "better understand trends, causes of insecurity and serious violations of international human rights and humanitarian law," according to the deactivated site. This includes massacres, murders, rapes, and violence against activists and medical personnel by state security forces and armed groups, the site said. But the KST's lax security protocols appear to have accidentally doxxed up to 8,000 people, including activists, sexual assault survivors, United Nations staff, Congolese government officials, local journalists, and victims of attacks, an Intercept analysis found. Hundreds of documents -- including 165 spreadsheets -- that were on a public server contained the names, locations, phone numbers, and organizational affiliations of those sources, as well as sensitive information about some 17,000 "security incidents," such as mass killings, torture, and attacks on peaceful protesters.

The data was available via KST's main website, and anyone with an internet connection could access it. The information appears to have been publicly available on the internet for more than four years. [...] The spreadsheets, along with the main KST website, were taken offline on October 28, after investigative journalist Robert Flummerfelt, one of the authors of this story, discovered the leak and informed Human Rights Watch and New York University's Center on International Cooperation. HRW subsequently assembled what one source close to the project described as a "crisis team." Last week, HRW and NYU's Congo Research Group, the entity within the Center on International Cooperation that maintains the KST website, issued a statement that announced the takedown and referred in vague terms to "a security vulnerability in its database," adding, "Our organizations are reviewing the security and privacy of our data and website, including how we gather and store information and our research methodology." The statement made no mention of publicly exposing the identities of sources who provided information on a confidential basis. [...] The Intercept has not found any instances of individuals affected by the security failures, but it's currently unknown if any of the thousands of people involved were harmed. "We deeply regret the security vulnerability in the KST database and share concerns about the wider security implications," Human Rights Watch's chief communications officer, Mei Fong, told The Intercept. Fong said in an email that the organization is "treating the data vulnerability in the KST database, and concerns around research methodology on the KST project, with the utmost seriousness." Fong added, "Human Rights Watch did not set up or manage the KST website. We are working with our partners to support an investigation to establish how many people -- other than the limited number we are so far aware of -- may have accessed the KST data, what risks this may pose to others, and next steps. The security and confidentiality of those affected is our primary concern."

Despite more than a decade of reminding, prodding, and downright nagging, a surprising number of developers still can't bring themselves to keep their code free of credentials that provide the keys to their kingdoms to anyone who takes the time to look for them. From a report: The lapse stems from immature coding practices in which developers embed cryptographic keys, security tokens, passwords, and other forms of credentials directly into the source code they write. The credentials make it easy for the underlying program to access databases or cloud services necessary for it to work as intended. [...]

The number of studies published since following the revelations underscored just how common the practice had been and remained in the years immediately following Uber's cautionary tale. Sadly, the negligence continues even now. Researchers from security firm GitGuardian this week reported finding almost 4,000 unique secrets stashed inside a total of 450,000 projects submitted to PyPI, the official code repository for the Python programming language. Nearly 3,000 projects contained at least one unique secret. Many secrets were leaked more than once, bringing the total number of exposed secrets to almost 57,000.

Programmer and writer James Somers, writing for New Yorker: Yes, our jobs as programmers involve many things besides literally writing code, such as coaching junior hires and designing systems at a high level. But coding has always been the root of it. Throughout my career, I have been interviewed and selected precisely for my ability to solve fiddly little programming puzzles. Suddenly, this ability was less important.

I had gathered as much from Ben (friend of the author), who kept telling me about the spectacular successes he'd been having with GPT-4. It turned out that it was not only good at the fiddly stuff but also had the qualities of a senior engineer: from a deep well of knowledge, it could suggest ways of approaching a problem. For one project, Ben had wired a small speaker and a red L.E.D. light bulb into the frame of a portrait of King Charles, the light standing in for the gem in his crown; the idea was that when you entered a message on an accompanying Web site the speaker would play a tune and the light would flash out the message in Morse code. (This was a gift for an eccentric British expat.) Programming the device to fetch new messages eluded Ben; it seemed to require specialized knowledge not just of the microcontroller he was using but of Firebase, the back-end server technology that stored the messages. Ben asked me for advice, and I mumbled a few possibilities; in truth, I wasn't sure that what he wanted would be possible. Then he asked GPT-4. It told Ben that Firebase had a capability that would make the project much simpler. Here it was -- and here was some code to use that would be compatible with the microcontroller.

Afraid to use GPT-4 myself -- and feeling somewhat unclean about the prospect of paying OpenAI twenty dollars a month for it -- I nonetheless started probing its capabilities, via Ben. We'd sit down to work on our crossword project, and I'd say, "Why don't you try prompting it this way?" He'd offer me the keyboard. "No, you drive," I'd say. Together, we developed a sense of what the A.I. could do. Ben, who had more experience with it than I did, seemed able to get more out of it in a stroke. As he later put it, his own neural network had begun to align with GPT-4's. I would have said that he had achieved mechanical sympathy. Once, in a feat I found particularly astonishing, he had the A.I. build him a Snake game, like the one on old Nokia phones. But then, after a brief exchange with GPT-4, he got it to modify the game so that when you lost it would show you how far you strayed from the most efficient route. It took the bot about ten seconds to achieve this. It was a task that, frankly, I was not sure I could do myself.

In chess, which for decades now has been dominated by A.I., a player's only hope is pairing up with a bot. Such half-human, half-A.I. teams, known as centaurs, might still be able to beat the best humans and the best A.I. engines working alone. Programming has not yet gone the way of chess. But the centaurs have arrived. GPT-4 on its own is, for the moment, a worse programmer than I am. Ben is much worse. But Ben plus GPT-4 is a dangerous thing.

Started in 2013, "Hour of Code" is an annual tradition started by the education non-profit Code.org (which provides free coding lessons to schools). Its FAQ describes the December event for K-12 students as "a worldwide effort to celebrate computer science, starting with 1-hour coding activities," and over 100 million schoolkids have participated over the years.

This year's theme will be "Creativity With AI," and the "computer vision" lesson includes a short video (less than 7 minutes) featuring a Tesla Autopilot product manager from its computer vision team. "I build self-driving cars," they say in the video. "Any place where there can be resources used more efficiently I think is a place where technology can play a role. But of course one of the best, impactful ways of AI, I hope, is through self-driving cars." (The video then goes on to explain how lots of training data ultimately generates a statistical model, "which is just a fancy way of saying, a guessing machine.")

The 7-minute video is part of a larger lesson plan (with a total estimated time of 45 minutes) in which students tackle a fun story problem. If a sports arena's scoreboard is showing digital numbers, what series of patterns would a machine-vision system have to recognize to identify each digit. (Students are asked to collaborate in groups.) And it's just one of seven 45-minute lessons, each one accompanied by a short video. (The longest video is 7 minutes and 28 seconds, and all seven videos, if watched back-to-back, would run for about 31 minutes.)

Not all the lessons involve actual coding, but the goal seems to be familiarizing students (starting at the 6th grade level) with artificial intelligence of today, and the issues it raises. The second-to-last lesson is titled "Algorithmic Bias" — with a video including interviews with an ethicist at Open AI and professor focused on AI from both MIT and Stanford. And the last lesson — "Our AI Code of Ethics" — challenges students to assemble documents and videos on AI-related "ethical pitfalls," and then pool their discoveries into an educational resource "for AI creators and legislators everywhere."

This year's installment is being billed as "the largest learning event in history." And it's scheduled for the week of December 4 so it coincides with "Computer Science Education Week" (a CS-education event launched in 2009 by the Association for Computing Machinery, with help from partners including Intel, Microsoft, Google, and the National Science Foundation).

The senior security editor at Ars Technica writes: Highly invasive malware targeting software developers is once again circulating in Trojanized code libraries, with the latest ones downloaded thousands of times in the last eight months, researchers said Wednesday.

Since January, eight separate developer tools have contained hidden payloads with various nefarious capabilities, security firm Checkmarx reported. The most recent one was released last month under the name "pyobfgood." Like the seven packages that preceded it, pyobfgood posed as a legitimate obfuscation tool that developers could use to deter reverse engineering and tampering with their code. Once executed, it installed a payload, giving the attacker almost complete control of the developerâ(TM)s machine. Capabilities include:


- Exfiltrate detailed host information
- Steal passwords from the Chrome web browser
- Set up a keylogger
- Download files from the victim's system
- Capture screenshots and record both screen and audio
- Render the computer inoperative by ramping up CPU usage, inserting a batch script in the startup directory to shut down the PC, or forcing a BSOD error with a Python script
- Encrypt files, potentially for ransom
- Deactivate Windows Defender and Task Manager
- Execute any command on the compromised host


In all, pyobfgood and the previous seven tools were installed 2,348 times. They targeted developers using the Python programming language... Downloads of the package came primarily from the US (62%), followed by China (12%) and Russia (6%)
Ars Technica concludes that "The never-ending stream of attacks should serve as a cautionary tale underscoring the importance of carefully scrutinizing a package before allowing it to run."

In Chrome, JavaScript (and WebAssembly) code are both executed by Google's open source V8 engine — which already has garbage-collecting capabilities. "This means developers making use of, for example, PHP compiled to Wasm, end up shipping a garbage collector implementation of the ported language (PHP) to the browser that already has a garbage collector," writes Google developer advocate Thomas Steiner, "which is as wasteful as it sounds."

"This is where WasmGC comes in." WebAssembly Garbage Collection (or WasmGC) is a proposal of the WebAssembly Community Group [which] adds struct and array heap types, which means support for non-linear memory allocation... In simplified terms, this means that with WasmGC, porting a programming language to WebAssembly means the programming language's garbage collector no longer needs to be part of the port, but instead the existing garbage collector can be used.
Sometime on Halloween, Steiner wrote that in Chrome, WebAssembly garbage collection is now enabled by default. But then he explored what this means for high-level programming languages (with their own built-in garbage collection) being compiled into WebAssembly: To verify the real-world impact of this improvement, Chrome's Wasm team has compiled versions of the Fannkuch benchmark (which allocates data structures as it works) from C, Rust, and Java. The C and Rust binaries could be anywhere from 6.1 K to 9.6 K depending on the various compiler flags, while the Java version is much smaller at only 2.3 K! C and Rust do not include a garbage collector, but they do still bundle malloc/free to manage memory, and the reason Java is smaller here is because it doesn't need to bundle any memory management code at all. This is just one specific example, but it shows that WasmGC binaries have the potential of being very small, and this is even before any significant work on optimizing for size.
The blog post includes two examples of WasmGC-ported programming languages in action:

• "One of the first programming languages that has been ported to Wasm thanks to WasmGC is Kotlin in the form of Kotlin/Wasm."

• "The Dart and Flutter teams at Google are also preparing support for WasmGC. The Dart-to-Wasm compilation work is almost complete, and the team is working on tooling support for delivering Flutter web applications compiled to WebAssembly."

This week GitHub announced the approaching general availability of the GPT-4-powered GitHub Copilot Chat in December "as part of your existing GitHub Copilot subscription" (and "available at no cost to verified teachers, students, and maintainers of popular open source projects.")

And this "code-aware guidance and code generation" will also be integrated directly into github.com, "so developers can dig into code, pull requests, documentation, and general coding questions with Copilot Chat providing suggestions, summaries, analysis, and answers." With GitHub Copilot Chat we're enabling the rise of natural language as the new universal programming language for every developer on the planet. Whether it's finding an error, writing unit tests, or helping debug code, Copilot Chat is your AI companion through it all, allowing you to write and understand code using whatever language you speak...

Copilot Chat uses your code as context, and is able to explain complex concepts, suggest code based on your open files and windows, help detect security vulnerabilities, and help with finding and fixing errors in code, terminal, and debugger...

With the new inline Copilot Chat, developers can chat about specific lines of code, directly within the flow of their code and editor.
InfoWorld notes it will chat in "whatever language a developer speaks." (And that Copilot Chat will also be available in GitHub's mobile app.) But why wait until December? GitHub's blog post says that Copilot Chat "will come to the JetBrains suite of IDEs, available in preview today."

GitHub also plans to introduce "slash commands and context variables" for GitHub Copilot, "so fixing or improving code is as simple as entering /fix and generating tests now starts with /tests."

"With Copilot in the code editor, in the CLI, and now Copilot Chat on github.com and in our mobile app, we are making Copilot ubiquitous throughout the software development lifecycle and always available in all of GitHub's surface areas..."

CNBC adds that "Microsoft-owned GitHub" also plans to introduce "a more expensive Copilot assistant" in February "for developers inside companies that can explain and provide recommendations about internal source code."

Wednesday's blog post announcing these updates was written by GitHub's CEO, who seemed to be predicting an evolutionary leap into a new future. "Just as GitHub was founded on Git, today we are re-founded on Copilot." He promised they'd built on their vision of a future "where AI infuses every step of the developer lifecycle." Open source and Git have fundamentally transformed how we build software. It is now evident that AI is ushering in the same sweeping change, and at an exponential pace... We are certain this foundational transformation of the GitHub platform, and categorically new way of software development, is necessary in a world dependent on software. Every day, the world's developers balance an unsustainable demand to both modernize the legacy code of yesterday and build our digital tomorrow. It is our guiding conviction to make it easier for developers to do it all, from the creative spark to the commit, pull request, code review, and deploy — and to do it all with GitHub Copilot deeply integrated into the developer experience.
And if you're worried about the security of AI-generated code... Today, GitHub Copilot applies an LLM-based vulnerability prevention system that blocks insecure coding patterns in real-time to make GitHub Copilot's suggestions more secure. Our model targets the most common vulnerable coding patterns, including hardcoded credentials, SQL injections, and path injections. GitHub Copilot Chat can also help identify security vulnerabilities in the IDE, explain the mechanics of a vulnerability with its natural language capabilities, and suggest a specific fix for the highlighted code.
But for Enterprise accounts paying for GitHub Advanced Security, there's also an upgrade coming: "new AI-powered application security testing features designed to detect and remediate vulnerabilities and secrets in your code." (It's already available in preview mode.)

GitHub even announced plans for a new AI assistant in 2024 that generates a step-by-step plan for responding to GitHub issues. (GitHub describes it as "like a pair programming session with a partner that knows about every inch of the project, and can follow your lead to make repository-wide changes from the issue to the pull request with the power of AI.")

CNBC notes that AI-powered coding assistants "are still nascent, though, with less than 10% enterprise adoption, according to Gartner, a technology industry research firm."

But last month Microsoft CEO Satya Nadella told analysts GitHub Copilot already had one million paying users...

And GitHub's blog post concludes, "And we're just getting started."