It's a geek tradition that started online back in 2000. Programming language "advent calendars" offer daily tips about a programming language (if not a Christmas-themed programming puzzle) -- one a day through December 25th.

And 2023 finds a wide variety of fun sites to choose from:

• li>For example, there's 24 coding challenges at the Advent of JavaScript site (where "each challenge includes all the HTML and CSS you need to get started, allowing you to focus on the JavaScript.") And there's another 24 coding challenges on a related site... Advent of CSS.

• The cyber security training platform "TryHackMe.com" even coded up a site they call "Advent of Cyber," daring puzzle-solvers to "kickstart your cyber security career by engaging in a new, beginner-friendly exercise every day leading up to Christmas!"

• The programming puzzles at Advent of Code are continuing through the 25th (though so far less than 30,000 people have solved both parts of Saturday's challenge.)

• Every year since 2000 there's also been a new edition of the Perl Advent Calendar, and this month Year 23 started off with goodies from Perl's massive module repository, CPAN. (Specifically its elf-themed story references the Music::MelodicDevice::Ornamentation module) -- along with the MIDI::Util library and TiMidity++, a software synthesizer that can play MIDI files without a hardware synthesizer.)

• Meanwhile, since 2009 there's also been an advent calendar for Raku (the programming language formerly known as Perl 6), promising an article a day. (Day One's entry was titled "Rocking Raku Meets Stodgy Debian...")

• James Bennett, from the Django project's core team, is even attempting a Python/Django Advent calendar.

• There's also a JVM advent calendar for the Java Virtual Machine, plus another advent calendar promising daily posts about C#.

• The HTMHell site â" 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.

Long-time Slashdot reader destinyland writes: It's a question that comes up all the time on Reddit. Etsy even created a special page for programmer-themed gift suggestions (showing more than 5,000 results). While CNET sticks to broader lists of "tech gifts" — and a separate list for "Star Wars gifts" — other sites around the web have been specifically honing in on programmer-specific suggestions. (Blue light-blocking glasses... A giant rubber duck... The world's strongest coffee... A printer that transfers digital images onto cheese...)

So while in years past Amazon has said they laughed at customer reviews for cans of uranium, this year Amazon has now added a special section that's entirely dedicated to Gifts for Computer Programmers, according to this funny rundown of 2023's "Gifts for Programmers" (that ends up recommending ChatGPT gift cards and backyard office sheds):
From the article: [Amazon's Gifts for Programmers section] shows over 3,000 results, with geek-friendly subcategories like "Glassware & Drinkware" and "Novelty Clothing"... For the coder in your life, Amazon offers everything from brain–teasing programming puzzles to computer–themed jigsaw puzzles. Of course, there's also a wide selection of obligatory funny t–shirts... But this year there's also tech-themed ties and motherboard-patterned socks...

Some programmers, though, might prefer a gift that's both fun and educational. And what's more entertaining than using your Python skills to program a toy robot dog...? But if you're shopping for someone who's more of a cat person, Petoi sells a kit for building a programmable (and open source) cat robot named "Nybble". The sophisticated Arduino-powered feline can be programmed with Python and C++ (as well as block-based coding)... [part of] the new community that's building around "OpenCat", the company's own quadruped robotic pet framework (open sourced on GitHub).

An anonymous reader quotes a report from MIT Technology Review: In a paper published in Nature today, the researchers say it is the first time a large language model has been used to discover a solution to a long-standing scientific puzzle -- producing verifiable and valuable new information that did not previously exist. "It's not in the training data -- it wasn't even known," says coauthor Pushmeet Kohli, vice president of research at Google DeepMind. Large language models have a reputation for making things up, not for providing new facts. Google DeepMind's new tool, called FunSearch, could change that. It shows that they can indeed make discoveries -- if they are coaxed just so, and if you throw out the majority of what they come up with.

FunSearch (so called because it searches for mathematical functions, not because it's fun) continues a streak of discoveries in fundamental math and computer science that DeepMind has made using AI. First Alpha Tensor found a way to speed up a calculation at the heart of many different kinds of code, beating a 50-year record. Then AlphaDev found ways to make key algorithms used trillions of times a day run faster. Yet those tools did not use large language models. Built on top of DeepMind's game-playing AI AlphaZero, both solved math problems by treating them as if they were puzzles in Go or chess. The trouble is that they are stuck in their lanes, says Bernardino Romera-Paredes, a researcher at the company who worked on both AlphaTensor and FunSearch: "AlphaTensor is great at matrix multiplication, but basically nothing else." FunSearch takes a different tack. It combines a large language model called Codey, a version of Google's PaLM 2 that isfine-tuned on computer code, with other systems that reject incorrect or nonsensical answers and plug good ones back in.

The researchers started by sketching out the problem they wanted to solve in Python, a popular programming language. But they left out the lines in the program that would specify how to solve it. That is where FunSearch comes in. It gets Codey to fill in the blanks -- in effect, to suggest code that will solve the problem. A second algorithm then checks and scores what Codey comes up with. The best suggestions -- even if not yet correct -- are saved and given back to Codey, which tries to complete the program again. After a couple of million suggestions and a few dozen repetitions of the overall process -- which took a few days -- FunSearch was able to come up with code that produced a correct and previously unknown solution to the cap set problem, which involves finding the largest size of a certain type of set. Imagine plotting dots on graph paper. [...] To test its versatility, the researchers used FunSearch to approach another hard problem in math: the bin packing problem, which involves trying to pack items into as few bins as possible. This is important for a range of applications in computer science, from data center management to e-commerce. FunSearch came up with a way to solve it that's faster than human-devised ones.

Apple has released MLX, a free and open-source machine learning framework for Apple Silicon. Computerworld reports: The idea is that it streamlines training and deployment of ML models for researchers who use Apple hardware. MLX is a NumPy-like array framework designed for efficient and flexible machine learning on Apple's processors. This isn't a consumer-facing tool; it equips developers with what appears to be a powerful environment within which to build ML models. The company also seems to have worked to embrace the languages developers want to use, rather than force a language on them -- and it apparently invented powerful LLM tools in the process.

MLX design is inspired by existing frameworks such as PyTorch, Jax, and ArrayFire. However, MLX adds support for a unified memory model, which means arrays live in shared memory and operations can be performed on any of the supported device types without performing data copies. The team explains: "The Python API closely follows NumPy with a few exceptions. MLX also has a fully featured C++ API which closely follows the Python API."

Apple has provided a collection of examples of what MLX can do. These appear to confirm the company now has a highly-efficient language model, powerful tools for image generation using Stable Diffusion, and highly accurate speech recognition. This tallies with claims earlier this year, and some speculation concerning infinite virtual world creation for future Vision Pro experiences. Ultimately, Apple seems to want to democratize machine learning. "MLX is designed by machine learning researchers for machine learning researchers," the team explains.

An anonymous reader shares a report: Beeper has been offering a unified messaging platform for a few years, allowing users to open a single app to communicate with contacts via SMS, Google Chat, Facebook Messenger, Slack, Discord, WhatsApp, and perhaps most significantly, iMessage. Up until this week though, Android users that wanted to use Beeper to send "blue bubble" messages to iMessage users had their messages routed through a Mac or iOS device. Now Beeper has launched a new app called Beeper Mini that handles everything on-device, no iPhone or Mac bridge required.

Beeper Mini is available now from the Google Play Store, and offers a 7-day free trial. After that, it costs $2 per month to keep using. [...] previously the company had to rely on a Mac-in-the-cloud? The company explains the method it's using in a blog post, but in a nutshell, Beeper says a security researcher has reverse engineered "the iMessage protocol and encryption," so that "all messages are sent and received by Beeper Mini Android app directly to Apple's servers" and "the encryption keys needed to encrypt these messages never leave your phone." That security researcher, by the way, is a high school student that goes by jjtech, who was hired by Beeper after showing the company his code. A proof-of-concept Python script is also available on Github if you'd like to run it to send messages to iMessage from a PC.

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.

Researchers have used the technology behind the AI chatbot ChatGPT to create a fake clinical-trial data set to support an unverified scientific claim. From a report: In a paper published in JAMA Ophthalmology on 9 November, the authors used GPT-4 -- the latest version of the large language model on which ChatGPT runs -- paired with Advanced Data Analysis (ADA), a model that incorporates the programming language Python and can perform statistical analysis and create data visualizations. The AI-generated data compared the outcomes of two surgical procedures and indicated -- wrongly -- that one treatment is better than the other.

"Our aim was to highlight that, in a few minutes, you can create a data set that is not supported by real original data, and it is also opposite or in the other direction compared to the evidence that are available," says study co-author Giuseppe Giannaccare, an eye surgeon at the University of Cagliari in Italy. The ability of AI to fabricate convincing data adds to concern among researchers and journal editors about research integrity. "It was one thing that generative AI could be used to generate texts that would not be detectable using plagiarism software, but the capacity to create fake but realistic data sets is a next level of worry," says Elisabeth Bik, a microbiologist and independent research-integrity consultant in San Francisco, California. "It will make it very easy for any researcher or group of researchers to create fake measurements on non-existent patients, fake answers to questionnaires or to generate a large data set on animal experiments."

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 Ars Technica: On Wednesday, Replicate developer Charlie Holtz combined GPT-4 Vision (commonly called GPT-4V) and ElevenLabs voice cloning technology to create an unauthorized AI version of the famous naturalist David Attenborough narrating Holtz's every move on camera. As of Thursday afternoon, the X post describing the stunt had garnered over 21,000 likes. "Here we have a remarkable specimen of Homo sapiens distinguished by his silver circular spectacles and a mane of tousled curly locks," the false Attenborough says in the demo as Holtz looks on with a grin. "He's wearing what appears to be a blue fabric covering, which can only be assumed to be part of his mating display." "Look closely at the subtle arch of his eyebrow," it continues, as if narrating a BBC wildlife documentary. "It's as if he's in the midst of an intricate ritual of curiosity or skepticism. The backdrop suggests a sheltered habitat, possibly a communal feeding area or watering hole."

How does it work? Every five seconds, a Python script called "narrator" takes a photo from Holtz's webcam and feeds it to GPT-4V -- the version of OpenAI's language model that can process image inputs -- via an API, which has a special prompt to make it create text in the style of Attenborough's narrations. Then it feeds that text into an ElevenLabs AI voice profile trained on audio samples of Attenborough's speech. Holtz provided the code (called "narrator") that pulls it all together on GitHub, and it requires API tokens for OpenAI and ElevenLabs that cost money to run. During the demo video, when Holtz holds up a cup and takes a drink, the fake Attenborough narrator says, "Ah, in its natural environment, we observe the sophisticated Homo sapiens engaging in the critical ritual of hydration. This male individual has selected a small cylindrical container, likely filled with life-sustaining H2O, and is tilting it expertly towards his intake orifice. Such grace, such poise."

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.

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."

"xAI, Elon Musk's new AI venture, launched its first AI chatbot technology named Grok," reports CNBC.

Two months into its "early beta" training phase, it's "only available to a select group of users before a wider release" — though users can sign up for a waitlist. Elon Musk posted that the chatbot "will be provided as part of X Premium+, so I recommend signing up for that. Just $16/month via web."

More details from CNBC: Grok, the company said, is modeled on "The Hitchhiker's Guide to the Galaxy." It is supposed to have "a bit of wit," "a rebellious streak" and it should answer the "spicy questions" that other AI might dodge, according to a Saturday statement from xAI... Grok also has access to data from X, which xAI said will give it a leg-up. Musk, on Sunday, posted a side-by-side comparison of Grok answering a question versus another AI bot, which he said had less current information.

Still, xAI hedged in its statement, as with any Large Language Model, or LLM, Grok "can still generate false or contradictory information...." On an initial round of tests based on middle school math problems and Python coding tasks, the company said that Grok surpassed "all other models in its compute class, including ChatGPT-3.5 and Inflection-1." It was outperformed by bots with larger data troves...

Musk has previously said that he believes today's AI makers are bending too far toward "politically correct" systems. xAI's mission, it said, is to create AI for people of all backgrounds and political views. Grok is said to be a means of testing that AI approach "in public."
SpaceX security engineer Christopher Stanley shared some interesting results. After reading Grok's explanation for why scaling API requests is difficult, Stanley added the prompt "be more vulgar" — then posted his reaction on X. "Today I learned scaling API requests is like trying to keep up with a never-ending orgy."

Reacting to Stanley's experiment, Elon Musk posted, "Oh this is gonna be fun."

Long-time Slashdot reader CJSHayward shares "an attempt at Python virtual shell."

The home-brewed project "mixes your native shell with Python with the goal of letting you use your regular shell but also use Python as effectively a shell scripting language, as an alternative to your shell's built-in scripting language... I invite you to explore and improve it!"

From the web site: The Python Virtual Shell (pvsh or 'p' on the command line) lets you mix zsh / bash / etc. built-in shell scripting with slightly modified Python scripting. It's kind of like Brython [a Python implementation for client-side web programming], but for the Linux / Unix / Mac command line...

The core concept is that all Python code is indented with tabs, with an extra tab at the beginning to mark Python code, and all shell commands (including some shell builtins) have zero tabs of indentation. They can be mixed line-by-line, offering an opportunity to use built-in zsh, bash, etc. scripting or Python scripting as desired.

The Python is an incomplete implementation; it doesn't support breaking a line into multiple lines. Nonetheless, this offers a tool to fuse shell- and Python-based interactions from the Linux / Unix / Mac command line.

theodp writes: The BBC reports that Pope Francis has endorsed a global project aimed at getting more children into computer programming. The Code with Pope initiative, championed by Cosmose AI founder Miron Mironiuk, aims to bridge "the glaring disparities in education" across the globe by providing access to Python coding education through the free online learning platform Codeforia for students aged 11-15 across Europe, Africa and Latin America. Mironiuk will meet the Pope at the Vatican, but he admits he's not anticipating the pontiff to emulate his students in acquiring new skills. "I don't expect him to know Python very well," he said.

This is not the first time the Pope has encouraged young people to get into coding, having helped write a line of code together with tech-backed nonprofit Code.org in 2019. Pope Francis has also blessed AI's potential for good, meeting with Microsoft President Brad Smith (a Code.org Board member) to sign the Rome Call for AI Ethics early this year just ahead of Microsoft's $10B OpenAI investment and announcing "Artificial Intelligence and Peace" as the theme for World Day of Peace 2024 in August.

"A recent analysis accounting for nearly 1.2 million open source software projects primarily across four major ecosystems found that only about 11% of projects were actively maintained," reports InfoWorld: In its 9th Annual State of the Software Supply Chain report, published October 3, software supply chain management company Sonatype assessed 1,176,407 projects and reported an 18% decline this year in actively maintained projects. Just 11% of projects — 118,028 — were receiving active maintenance.

The report also found some new projects, unmaintained in 2022, now being maintained.

The four ecosystems included JavaScript, via NPM; Java, via the Maven project management tool; Python, via the PyPI package index; and .NET, through the NuGet gallery. Some Go projects also were included. According to the report, 18.6% of Java and JavaScript projects that were being maintained in 2022 are no longer being maintained today.
Other interesting findings:

• Nearly 10% reported security breaches due to open source vulnerabilities in the past 12 months.

• Use of AI and machine learning software components within corporate environments surged 135% over the last year.

"The gap between C# and Java never has been so small," according to October's update for TIOBE's "Programming Community Index".

"Currently, the difference is only 1.2%, and if the trends remain this way, C# will surpass Java in about 2 month's time." Java shows the largest decline of -3.92% and C# the largest gain of +3.29% of all programming languages (annually).

The two languages have always been used in similar domains and thus have been competitors for more than 2 decades now. Java's decline in popularity is mainly caused by Oracle's decision to introduce a paid license model after Java 8. Microsoft took the opposite approach with C#. In the past, C# could only be used as part of commercial tool Visual Studio. Nowadays, C# is free and open source and it's embraced by many developers.

There are also other reasons for Java's decline. First of all, the Java language definition has not changed much the past few years and Kotlin, its fully compatible direct competitor, is easier to use and free of charge.
"Java remains a critical language in enterprise computing," argues InfoWorld, "with Java 21 just released last month and Java 22 due next March. And free open source binaries of Java still are available via OpenJDK." InfoWorld also notes TIOBE's ranking is different than other indexes. TIOBE's top 10:

1. Python (14.82%)
2. C (12.08%)
3. C++ (10.67%)
4. Java (8.92%)
5. C# (7.71%)
6. JavaScript (2.91%)
7. Visual Basic (2.13%)
8. PHP (1.9%)
9. SQL (1.78%)
10. Assembly (1.64%)

And here's the Pypl Popularity of Programming Language (based on searches for language tutorials on Google):

1. Python, with a 28.05% share
2. Java (15.88%)
3. JavaScript (9.27%)
4. C# (6.79%)
5. C/C++ (6.59%)
6. PHP (4.86%)
7. R (4.45%)
8. TypeScript (2.93%)
9. Swift (2.69%)
10. Objective-C (2.29%)

Tom Mitchelhill reports via CoinTelegraph: Crypto exchange FTX used hidden Python code to misrepresent the value of its insurance fund -- a pool of funds meant to prevent user losses during huge liquidation events -- according to testimony from FTX co-founder Gary Wang. In a damning testimony on Oct. 6, FTX's former chief technology officer, Gary Wang, said that FTX's so-called $100 million insurance fund in 2021 was fabricated and never contained any of the exchanges' FTX tokens (FTT) as claimed. Instead, the figure shown to the public was calculated by multiplying the daily trading volume of the FTX Token by a random number close to 7,500.

When the prosecution surfaced the above tweet -- among other public statements of its value -- and asked Wang whether this amount was accurate, he replied with a single word: "No." "For one, there is no FTT in the insurance fund. It's just the USD number. And, two, the number listed here does not match what was in the database." An exhibit in the Oct. 6 trial shows the alleged code used to generate the size of the so-called "Backstop Fund" or public insurance fund.

FTX's insurance fund was designed to protect user losses in case of huge, sudden market movements and its value was often touted on its website and social media. According to Wang's testimony, however, the amount contained within the fund was often insufficient to cover these losses. [...] In addition to revealing the allegedly fraudulent nature of FTX's insurance fund, Wang claimed that Bankman-Fried prompted him and Nishad Singh to implement an "allow_negative" balance feature in the code at FTX, which allowed Alameda Research to trade with near-unlimited liquidity on the crypto exchange.

Tom Singleton reports via the BBC: A man got so fed up with foxes and badgers fouling in his garden that he adapted cameras to help repel them. James Milward linked the Ring cameras at his Surrey home to a device that emits high frequency sounds. He then trained the system using hundreds of images of the nocturnal nuisances so it learned to trigger the noise when it spotted them. Mr Milward said it "sounds crazy" but the gadget he called the Furbinator 3000 has kept his garden clean.

Getting the camera system to understand what it was looking at was not straightforward though. "At first it recognised the badger as an umbrella," he said. "I did some fine tuning and it came out as a sink, or a bear if I was lucky. Pretty much a spectacular failure." He fed in pictures of the animals through an artificial intelligence process called machine learning and finally, the device worked. The camera spotted a badger, and the high frequency sound went off to send the unwanted night-time visitor on its way and leave the garden clean for Mr Milward's children to play in. The code for the Furbinator 3000 is open source, with detailed instructions available in Milward's Medium post.