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.

Still using Python 3.7? Even Microsoft thinks it is time to move on after the Windows behemoth finally deprecated support for the language in the October 2023 release of its extension for Visual Studio Code. From a report: Python 3.7 reached its end of life in June but remains popular. According to some statistics, many sites use version 3.7 -- 17.2 percent of those using Python 3.x by some estimates. Python 3.6, which reached the end of life in 2021, accounts for 28.9 percent and is still the most popular. Python 3.8 sits between the two, accounting for 23.3 percent.

Doubtless mindful of its popularity, Microsoft confirmed there were no plans to strip the code from the Visual Studio Code extension deliberately, saying: "We expect the extension will continue to work unofficially with Python 3.7 for the foreseeable future." However, there are no guarantees that something won't go wrong without official support. Python has moved to an annual cadence for end of life. Python 3.8 is due to reach end of life in October 2024, meaning that official support in Microsoft's Visual Studio Code extension will end with the first release of 2025, and so on. According to Microsoft, the Python extension for Visual Studio works with all actively supported versions of Python. 3.12 is the latest version and, unsurprisingly, has yet to influence the statistics too much. 3.13 is penciled in for release next year.

"OpenAI has unveiled the Beta version of its Python SDK," reports Analytics India Magazine, "marking a significant step towards enhancing access to the OpenAI API for Python developers." The OpenAI Python library offers a simplified way for Python-based applications to interact with the OpenAI API, while providing an opportunity for early testing and feedback before the official launch of version 1.0. It streamlines the integration process by providing pre-defined classes for API resources, dynamically initialising from API responses, ensuring compatibility across various OpenAI API versions...

Developers can find comprehensive documentation and code examples in the OpenAI Cookbook for various tasks, including classification, clustering, code search, customising embeddings, question answering, recommendations, visualisation of embeddings, and more...

This comes just weeks before OpenAI's first developer conference, OpenAI DevDay.
More details in OpenAI's official announcement at PyPi.org.

"Python 3 was by far the choice over Python 2 in a late-2022 survey of more than 23,000 Python developers," reports InfoWorld, "but the percentage of respondents using Python 2 actually ticked up compared to the previous year." Results of the sixth annual Python Developers Survey, conducted by the Python Software Foundation and software tools maker JetBrains, were released September 27. The Python Developers Survey 2022 report indicates that 93% of respondents had adopted Python 3, while only 7% were still using Python 2. In the 2021 survey, though, 95% used Python 3 while 5% used Python 2. In 2020, Python 3 held a 94% to 6% edge. Dating back to 2017, 75% used Python 3 and 25% used Python 2...

The 2022 report said 29% of respondents still use Python 2 for data analysis, 24% use Python 2 for computer graphics, and 23% used Python 2 for devops. The survey also found that 45% of respondents are still using Python 3.10, which arrived two years ago, while just 2% still use Python 3.5 or lower. (Python 3.11 was released October 24, 2022, right when the survey was being conducted.)
Other findings from the survey:

• 21% said they used Python for work only, while 51% said they used it for work and personal/educational use or side projects, and 21% said they used Python only for personal projects.

• 85% of respondents said Python was their main language (rather than a secondary language).

• The survey also gives the the top "secondary languages" for the surveyed Python developers as JavaScript (37%), HTML/CSS (37%), SQL (35%), Bash/Shell (32%), and then C/C++ (27%).

• When asked what they used Python for most, 22% said "Web Development", 18% said "Data Analysis," 12% said "Machine Learning," and 10% said "DevOps/System Administration/Writing Automation Scripts."

Monday will see the stable release of Python 3.12. Here's an article summarizing what the new version will include:

- enhanced error messages
— performance upgrades
- the introduction of Immortal objects and sub interpreters
- changes to F strings
- modifications related to types and type annotations
- the removal of certain modules
- improvements in type implementations
Modules from the standard library are now suggested as part of the error messages, making it easier for developers to troubleshoot and resolve issues...

Another significant addition in Python 3.12 is the introduction of sub interpreters. Each sub interpreter has its own Global Interpreter Lock, enabling Python to better utilize multiple CPU cores. This feature can significantly enhance the performance of Python programs, especially those that are designed to take advantage of multi-core processors...

The pathlib module now has a walk method, allowing for the exploration of directory trees. This new feature can make it easier for developers to work with file systems in their Python programs. Python 3.12 also supports the ability to monitor calls, returns, lines, exceptions, and other events using instrumentation. This feature can be very useful for debugging and performance tuning.

Long-time Slashdot reader theodp spotted a Reddit Ask Me Anything (AMA) this week with the Microsoft engineering team that created Python in Excel, a new feature that makes it possible to natively combine Python and Excel analytics in Excel workbooks. (Copilot integration is coming soon). Redditors expressed a wish to be able to run Python in environments other than the confines of the locked down, price-to-be-determined Microsoft Azure cloud containers employed by Python in Excel.

But "There were three main reasons behind starting with the cloud (as a GDPR Compliant Microsoft 365 Connected experience) first," MicrosoftExcelTeam explained:

1. Running Python securely on a local machine is a difficult problem. We treat all Python code in the workbook as untrusted, so we execute it in a hypervisor-isolated container on Azure that does not have any outbound network access. Python code and the data that it operates on is sent to be executed in the container. The Microsoft-licensed Python environment in the container is provided by Anaconda and was prepared using their stringent security practices as documented here.

2. Sharing Excel workbooks with others is a really important scenario. We wanted to ensure that the Python code in a workbook you share behaves the same when your teammates open it â" without requiring them to install and manage Python.

3. We need to ensure that the Python in Excel feature always works for our customers. The value of Python is in its ecosystem of libraries, not just in providing a Python interpreter. But managing a local Python environment is challenging even for the most experienced developers. By running on Azure, we remove the need for users or their systems administrators to maintain a local installation of Python on every machine that uses the feature in their organization...


So, how does one balance tradeoffs between increased security and ease-of-maintenance with the loss of functionality and increased costs when it comes to programming language use? Is it okay to just give up on making certain important basic functionality available, as Microsoft is doing here with Python and has done in the past by not supporting Excel VBA in the Cloud and no longer making BASIC available on PCs and Macs?
Microsoft's team added at one point that "For our initial release, we are targeting data analytics scenarios, and bringing the power of Python analytics libraries into Excel.

"We believe the approach weâ(TM)ve taken will appeal to analysts who use both Excel and Python Notebooks in their workflows. Today, these users need to import/export data and have no way of creating a self-contained artifact that can be easily and securely shared with their colleagues."

An anonymous reader shared this report from InfoWorld: JetBrains is previewing a dedicated IDE for the Rust programming language, called RustRover, which combines coding assistance with an integrated Rust toolchain. Available in preview September 13, RustRover is positioned to simplify the Rust coding experience while "unlocking the language's full potential," JetBrains said. Capabilities include real-time feedback, code suggestions, simplified toolchain management, and team collaboration.

Previously, JetBrains offered IntelliJ Rust, an open source Rust plugin for IntelliJ IDEs. But with RustRover, the company aims to provide a dedicated product with enhanced functionality for the growing Rust developer community. JetBrains also has been previewing a multi-language editor and IDE, called JetBrains Fleet, that supports Rust development...

RustRover will have some similarities to JetBrains' other language-specific IDEs including PyCharm for Python, GoLand for Go, and RubyMine for Ruby.
RustRover integrates with version control systems, supporting GitHub and Git.

Last week IEEE Spectrum released its 10th annual rankings of the Top Programming Languages. It choose a top language for each of three categories: actively used among typical IEEE members and working software engineers, in demand by employers, or "in the zeitgeist".

The results? This year, Python doesn't just remain No. 1 in our general "Spectrum" ranking — which is weighted to reflect the interests of the typical IEEE member — but it widens its lead.

Python's increased dominance appears to be largely at the expense of smaller, more specialized, languages. It has become the jack-of-all-trades language — and the master of some, such as AI, where powerful and extensive libraries make it ubiquitous. And although Moore's Law is winding down for high-end computing, low-end microcontrollers are still benefiting from performance gains, which means there's now enough computing power available on a US $0.70 CPU to make Python a contender in embedded development, despite the overhead of an interpreter. Python also looks to be solidifying its position for the long term: Many children and teens now program their first game or blink their first LED using Python. They can then move seamlessly into more advanced domains, and even get a job, with the same language.

But Python alone does not make a career. In our "Jobs" ranking, it is SQL that shines at No. 1. Ironically though, you're very unlikely to get a job as a pure SQL programmer. Instead, employers love, love, love, seeing SQL skills in tandem with some other language such as Java or C++. With today's distributed architectures, a lot of business-critical data live in SQL databases...

But don't let Python and SQL's rankings fool you: Programming is still far from becoming a monoculture. Java and the various C-like languages outweigh Python in their combined popularity, especially for high-performance or resource-sensitive tasks where that interpreter overhead of Python's is still too costly (although there are a number of attempts to make Python more competitive on that front). And there are software ecologies that are resistant to being absorbed into Python for other reasons.
The article cites the statistical analysis/visualization language R, as well as Fortran and Cobol, as languages that are hard to port code from or that have accumulated large already-validated codebases. But Python also remains at #1 in their third "Trending" category — with Java in second there and on the general "IEEE Spectrum" list.

JavaScript appears below Python and Java on all three lists. Java is immediately below them on the Trending and "Jobs" list, but two positions further down on the general "Spectrum" list (below C++ and C).

The metrics used for the calculation include the number of hits on Google, recent questions on Stack Overflow, tags on Discord, mentions in IEEE's library of journal articles and its CareerBuilder job site, and language use in starred GitHub repositories and number of new programming books.

An anonymous reader quotes a report from TechCrunch: Companies increasingly see the value in mining their data for deeper insights. According to a NewVantage survey, 97.6% of major worldwide organizations are focusing investments into big data and AI. But challenges stand in the way of executing big data analytics. One recent poll found that 65% of organizations feel they have "too much" data to analyze. Google's proposed solution is BigQuery Studio, a new service within BigQuery, its fully managed serverless data warehouse, that provides a single experience to edit programming languages including SQL, Python and Spark to run analytics and machine learning workloads at "petabyte scale." BigQuery Studio is available in preview as of this week.

"BigQuery Studio is a new experience that really puts people who are working on data on the one side and people working on AI on the other side in a common environment," Gerrit Kazmaier, VP and GM of data and analytics at Google, told TechCrunch in a phone interview. "It basically provides access to all of the services that those people need to work -- there's an element of simplification on the user experience side." BigQuery Studio is designed to enable users to discover, explore, analyze and predict data. Users can start in a programming notebook to validate and prep data, then open that notebook in other services, including Vertex AI, Google's managed machine learning platform, to continue their work with more specialized AI infrastructure and tooling.

With BigQuery Studio, teams can directly access data wherever they're working, Kazmaier says. And they have added controls for "enterprise-level" governance, regulation and compliance. "[BigQuery Studio shows] how data is being generated to how it's being processed and how it's being used in AI models, which sounds technical, but it's really important," he added. "You can push down code for machine learning models directly into BigQuery as infrastructure, and that means that you can evaluate it at scale."

The creators of four programming languages will appear together onstage for a historic conversation on September 19th.

- Adele Goldberg — Smalltalk
- Guido Van Rossum — Python
- Anders Hejlsberg — Turbo Pascal, C#, TypeScript
- James Gosling — Java

The announcement describes it as "a conversation about programming language design." The charity event brings together this unique group of computer science pioneers, unlike any event held before. These great minds come together for what will surely be a fantastic night of discussion as the panel delves into the past and future of programming language creation.
It's a fundraiser for two groups. NumFOCUS is a nonprofit charity sponsoring nearly all the major tools in the Python data science stack (including jupyter, numpy, pandas, and matplotlib), and it's also the group behind PyData conferences on open source data tools. And the Last Mile Education Fund offers financial support for low-income underrepresented students. It's being billed as the "inaugural charity event" of PyData Seattle.

This happened once before in 2019, when Puget Sound Programming Python arranged a four-way discussion with Python creator Guido van Rossum, Java creator James Gosling, Perl creator Larry Wall, and Anders Hejlsberg (Turbo Pascal, C#, TypeScript). They held a 90-minute discussion about "language design, the universe, and everything" as a benefit for CSforALL (a group promoting computer science classes at every grade level). During that discussion Gosling shared how Java "started out as kind of 'Do a better C', and it got out of control. The rest of the project really ended up just providing the context." And Anders Hejlsberg told the audience that TypeScript was inspired by massive "write-only" JavaScript code bases.

In their discussion on variable typing and its use in IDEs, Gosling mocked what he called the "real men use vi" mentality, leading to a lively back and forth. Perl's Larry Wall later acknowledged the importance of types and the careful consideration that went into implementing them for Perl 6, but also shared his unique perspective as a long-time designer of programming languages. "I think IDEs make language developers lazy."

At the end of the event, they all agreed that the most rewarding part of language design was the people — the excitement, the gratitude, and to see that community helping others in its community.

Meta, intent on making a splash in a generative AI space rife with competition, is on something of an open source tear. From a report: Following the release of AI models for generating text, translating languages and creating audio, the company today open sourced Code Llama, a machine learning system that can generate and explain code in natural language -- specifically English. Akin to GitHub Copilot and Amazon CodeWhisperer, as well as open source AI-powered code generators like StarCoder, StableCode and PolyCoder, Code Llama can complete code and debug existing code across a range of programming languages, including Python, C++, Java, PHP, Typescript, C# and Bash.

"At Meta, we believe that AI models, but large language models for coding in particular, benefit most from an open approach, both in terms of innovation and safety," Meta wrote in a blog post shared with TechCrunch. "Publicly available, code-specific models can facilitate the development of new technologies that improve peoples' lives. By releasing code models like Code Llama, the entire community can evaluate their capabilities, identify issues and fix vulnerabilities." Code Llama, which is available in several flavors, including a version optimized for Python and a version fine-tuned to understand instructions (e.g. "Write me a function that outputs the fibonacci sequence"), is based on the Llama 2 text-generating model that Meta open sourced earlier this month. While Llama 2 could generate code, it wasn't necessarily good code -- certainly not up to the quality a purpose-built model like Copilot could produce.

theodp writes: On Tuesday, Microsoft announced the Public Preview of Python in Excel, which "runs securely on the Microsoft Cloud".

From the Home Office in Redmond: "Python is one of the most popular programming languages today, loved by businesses and students alike and Excel is an essential tool to organize, manipulate and analyze all kinds of data. But, until now, there hasn't been an easy way to make those two worlds work together. Today, we are excited to introduce the Public Preview of Python in Excel -- making it possible to integrate Python and Excel analytics within the same Excel grid for uninterrupted workflow. Python in Excel combines Python's powerful data analysis and visualization libraries with Excel's features you know and love. You can manipulate and explore data in Excel using Python plots and libraries, and then use Excel's formulas, charts and PivotTables to further refine your insights...We're partnering with Anaconda, a leading enterprise grade Python repository used by tens of millions of data practitioners worldwide. Python in Excel leverages Anaconda Distribution for Python running in Azure, which includes the most popular Python libraries such as pandas for data manipulation, statsmodels for advanced statistical modeling, and Matplotlib and seaborn for data visualization....While in Preview, Python in Excel will be included with your Microsoft 365 subscription. After the Preview, some functionality will be restricted without a paid license."

Python creator Guido van Rossum, now a Microsoft Distinguished Engineer, helped define the architecture for Python in Excel and had this to say: "I'm excited that this excellent, tight integration of Python and Excel is now seeing the light of day. I expect that both communities will find interesting new uses in this collaboration, amplifying each partner's abilities. When I joined Microsoft three years ago, I would not have dreamed this would be possible. The Excel team excels!"

An anonymous reader quotes a report from TechCrunch: Google today announced the launch of Project IDX, its foray into offering an AI-enabled browser-based development environment for building full-stack web and multiplatform apps. It currently supports frameworks like Angular, Flutter, Next.js, React, Svelte and Vue, and languages like JavaScript and Dart, with support for Python, Go and others in the works. Google did not build a new IDE (integrated development environment) when it created IDX. Instead, it is using Visual Studio Code -- Open Source as the basis of its project. This surely allowed the team to focus on the integration with Codey, Google's PaLM 2-based foundation model for programming tasks. Thanks to Codey, IDX supports smart code completion, a ChatGPT/Bard-like chatbot that can help developers with general coding questions as well as those related specifically to the code you are working on (including the ability to explain it) and the ability to add contextual code actions like "add comments."

"We spend a lot of time writing code, and recent advances in AI have created big opportunities to make that time more productive," the IDX team explains in today's announcement. "With Project IDX, we're exploring how Google's innovations in AI -- including the Codey and PaLM 2 models powering Studio Bot in Android Studio, Duet in Google Cloud and more -- can help you not only write code faster, but also write higher-quality code." As a cloud-based IDE, it's no surprise that Project IDX integrates with Google's own Firebase Hosting (and Google Cloud Functions) and allows developers to bring in existing code from the GitHub repository. Every workspace has access to a Linux-based VM (virtual machine) and, soon, embedded Android and iOS simulators right in the browser.

Canonical engineering manager Ben Hoyt believes that a variable's name is more important than its type, so "the name should be more prominent and come first in declarations." In many popular programming languages, including C, C++, Java, and C#, when you define a field or variable, you write the type before the name. For example (in C++):

// Struct definition
struct person {
std::string name;
std::string email;
int age;
};

In other languages, including Go, Rust, TypeScript, and Python (with type hints), you write the name before the type. For example (in Go):

// Struct definition
type Person struct {
Name string
Email string
Age int
}

There's a nice answer in the Go FAQ about why Go chose this order: "Why are declarations backwards?". It starts with "they're only backwards if you're used to C", which is a good point — name-before-type has a long history in languages like Pascal. In fact, Go's type declaration syntax (and packages) were directly inspired by Pascal.

The FAQ goes on to point out that parsing is simpler with name-before-type, and declaring multiple variables is less error-prone than in C. In C, the following declares x to be a pointer, but (surprisingly at first!) y to be a normal integer:

int* x, y;

Whereas the equivalent in Go does what you'd expect, declaring both to be pointers:

var x, y *int

The Go blog even has an in-depth article by Rob Pike on Go's Declaration Syntax, which describes more of the advantages of Go's syntax over C's, particularly with arrays and function pointers.

Oddly, the article only hints at what I think is the more important reason to prefer name-before-type for everyday programming: it's clearer.
Hoyt argues a variable's name has more meaning (semantically) — pointing out dynamically-typed languages like Python and Ruby don't even need types, and that languages like Java, Go, C++ and C# now include type inference.

"I think the takeaway is this: we can't change the past, but if you're creating a new language, please put names before types!"

Python's Global Interpreter Lock "allows only one thread to hold the control of the Python interpreter," according to the tutorial site Real Python. (They add, "it can be a performance bottleneck in CPU-bound and multi-threaded code.")

Friday the Python Steering Council "announced its intent to accept PEP 703 (Making the Global Interpreter Lock Optional in CPython), with initial support possibly showing up in the 3.13 release," reports LWN.net.

From the Steering Council's announcement: It's clear that the overall sentiment is positive, both for the general idea and for PEP 703 specifically. The Steering Council is also largely positive on both. We intend to accept PEP 703, although we're still working on the acceptance details...

Our base assumptions are:

- Long-term (probably 5+ years), the no-GIL build should be the only build. We do not want to create a permanent split between with-GIL and no-GIL builds (and extension modules).

- We want to be very careful with backward compatibility. We do not want another Python 3 situation, so any changes in third-party code needed to accommodate no-GIL builds should just work in with-GIL builds (although backward compatibility with older Python versions will still need to be addressed). This is not Python 4. We are still considering the requirements we want to place on ABI compatibility and other details for the two builds and the effect on backward compatibility.

- Before we commit to switching entirely to the no-GIL build, we need to see community support for it. We can't just flip the default and expect the community to figure out what work they need to do to support it. We, the core devs, need to gain experience with the new build mode and all it entails. We will probably need to figure out new C APIs and Python APIs as we sort out thread safety in existing code. We also need to bring along the rest of the Python community as we gain those insights and make sure the changes we want to make, and the changes we want them to make, are palatable.

- We want to be able to change our mind if it turns out, any time before we make no-GIL the default, that it's just going to be too disruptive for too little gain. Such a decision could mean rolling back all of the work, so until we're certain we want to make no-GIL the default, code specific to no-GIL should be somewhat identifiable.
The current plan is to "add the no-GIL build as an experimental build mode, presumably in 3.13... [A]fter we have confidence that there is enough community support to make production use of no-GIL viable, we make the no-GIL build supported but not the default (yet), and set a target date/Python version for making it the default... We expect this to take at least a year or two, possibly more."

"Long-term, we want no-GIL to be the default, and to remove any vestiges of the GIL (without unnecessarily breaking backward compatibility)... We think it may take as much as five years to get to this stage."

An anonymous reader shares this report from Dice.com: C++ is enjoying a surge in popularity, according to the latest update to the TIOBE Index, which tracks programming languages' "buzz."

C++ currently sits right behind C and Python on TIOBE's list. "A few months ago, the programming C++ language claimed position 3 of the TIOBE index (at the expense of Java). But C++ has not finished its rise. C seems to be its next victim," added the note accompanying the data... ["At the moment, the gap between the two is only 0.76%."]

Matlab, Scratch and Rust also match their all time high records at respectively positions #10, #12 and #17.
So here, according to TIOBE, are the 10 most popular programmings languages:

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

The site I Programmer digs deeper: C++ was the only one of the top four languages to see a positive year-on-year change in its percentage rating — adding 0.79% to stand at 10.8%. Python had the smallest loss of the entire Top 20, -0.01% leaving it with a share of 13,42% while Visual Basic had the greatest loss at -2.07%. This, combined with JavaScript gaining 1.34%, led to JavaScript overtaking it to occupy #6, its highest ever ranking in the TIOBE Index.
They also note that COBOL "had a 3-month rise going from a share of 0.41% in April to 0.86% in July which moved it into #20 on the index."

InfoWorld explores how the new Mojo program language "resembles Python, how it's different, and what it has to offer." The newly unveiled Mojo language is being promoted as the best of multiple worlds: the ease of use and clear syntax of Python, with the speed and memory safety of Rust. Those are bold claims, and since Mojo is still in the very early stages of development, it will be some time before users can see for themselves how the language lives up to them. But Mojo's originator — a company named Modular — has provided early access [through a limited-enrollment preview program] to an online playground: a Jupyter Notebook environment where users can run Mojo code and learn about the language's features and behavior...

Mojo can be described as a "superset" of Python. Programs written in Python are valid Mojo programs, although some Python behaviors haven't yet been implemented... It's also possible to use the actual Python runtime for working with existing Python modules, although there is a performance cost. When Mojo introduces new syntax, it's for system-level programming features, chiefly manual memory handling. In other words, you can write Python code (or something almost exactly like it) for casual use cases, then use Mojo for more advanced, performance-intensive programming scenarios... Mojo's other big difference from Python is that Mojo's not interpreted through a runtime, as Python is. Mojo is compiled ahead-of-time to machine-native code, using the LLVM toolchain. To that end, the best performance comes from using features specific to Mojo. Python features are likely to come at the cost of emulating Python's dynamic behaviors, which are inherently slow — or again, by just using the Python runtime.

Many of Mojo's native language features do one of two things. They're either entirely new features not found in Python at all, or expansions of a Python feature that make it more performant, although with less of Python's dynamism.
For example, Mojo has its own fn keyword which defines a function with explicitly-typed and immutable-by-default arguments, and its own struct keyword which is less like a Python class and more like its C/C++ and Rust counterpart "with fixed layouts determined at compile time but optimized for machine-native speed."

But "At a glance, the code closely resembles Python. Even the new Mojo-specific keywords integrate well with existing Python syntax, so you can run your eye down the code and get a general idea of what's happening." And then there's the speed... The notebook demos also give examples of how Mojo code can be accelerated via parallelism, vectorizing, and "tiling" (increasing cache locality for operations). One of the demos, a 128x128 matrix multiplication demo, yielded a claimed 17-times speedup over Python (using the Python runtime in the Mojo playground) by simply running as-is with no special modification. Mojo added 1866x speedup by adding type annotations, 8500x speedup by adding vectorized operations, and 15000x speedup by adding parallelization.

ZDNet asks the million-dollar question. "Despite the potential for vast productivity gains from generative AI tools such as ChatGPT or GitHub Copilot, will technology professionals' jobs actually grow more complicated? " People can now pump out code on demand in an abundance of languages, from Java to Python, along with helpful recommendations. Already, 95% of developers in a recent survey from Sourcegraph report they use Copilot, ChatGPT, and other gen AI tools this way.

But auto-generating new code only addresses part of the problem in enterprises that already maintain unwieldy codebases, and require high levels of cohesion, accountability, and security.

For starters, security and quality assurance tasks associated with software jobs aren't going to go away anytime soon. "For programmers and software engineers, ChatGPT and other large language models help create code in almost any language," says Andy Thurai, analyst with Constellation Research, before talking about security concerns. "However, most of the code that is generated is security-vulnerable and might not pass enterprise-grade code. So, while AI can help accelerate coding, care should be taken to analyze the code, find vulnerabilities, and fix it, which would take away some of the productivity increase that AI vendors tout about."

Then there's code sprawl. An analogy to the rollout of generative AI in coding is the introduction of cloud computing, which seemed to simplify application acquisition when first rolled out, and now means a tangle of services to be managed. The relative ease of generating code via AI will contribute to an ever-expanding codebase — what the Sourcegraph survey authors refer to as "Big Code". A majority of the 500 developers in the survey are concerned about managing all this new code, along with code sprawl, and its contribution to technical debt. Even before generative AI, close to eight in 10 say their codebase grew five times over the last three years, and a similar number struggle with understanding existing code generated by others.

So, the productivity prospects for generative AI in programming are a mixed bag.

In a blog post on Wednesday, Google said Bard is getting better at logic and reasoning. "Google says that now when you ask Bard a 'computational' task like math or string manipulation, instead of showing the output of the language model, that language model will instead write a program, execute that program, and then show the output of that program to the user as an answer," reports Ars Technica. From the report: Google's blog post provides the example input of "Reverse the word 'Lollipop' for me." ChatGPT flubs this question and provides the incorrect answer "pillopoL," because language models see the world in chunks of words, or "tokens," and they just aren't good at this. It gets the output correct as "popilloL," but more interesting is that it also includes the python code it wrote to answer the question. That's neat for programming-minded people to see under the hood, but wow, is that probably the scariest output ever for regular people. It's also not particularly relevant. Imagine if Gmail showed you a block of code when you just asked it to fetch email. It's weird. Just do the job you were asked to do, Bard.

Google likens an AI model writing a program to humans doing long division in that it's a different mode of thinking [...]. Google says this "writing code on the fly" method will also be used for questions like: "What are the prime factors of 15683615?" and "Calculate the growth rate of my savings." The company says, "So far, we've seen this method improve the accuracy of Bard's responses to computation-based word and math problems in our internal challenge datasets by approximately 30%." As usual, Google warns Bard "might not get it right" due to interpreting your question wrong or just, like all of us, writing code that doesn't work the first time. Bard is coding up answers on the fly right now if you want to give it a shot at bard.google.com.

theodp writes: Golf reports that amateur golf legend Rose Zhang will compete for the first time as a professional when she tees off in the first round of the Mizuho Americas Open Thursday. Golf news is rarely fodder for Slashdot discussion, but when the 20-year-old Stanford student (who plans to complete her degree after a leave of absence) was asked by Golf to identify her toughest class, she threw CS under the bus.

"CS 106A," Zhang replied, referring to a computer science course. "Currently and still trying to grind in that class. It's been a little unfortunate for me. I'm not a CS major. Will never code again after this class." Back in April, Zhang expressed some doubts about being able to juggle the demands of an already-renowned golf career and CS 106A. "I'll be super, super busy," Zhang said in an interview. "I'm planning on taking CS 106A. I don't know if it's a smart decision but it's kind of an essential intro CS class into Stanford so I'm going to try to navigate that, balance that out."

The Stanford Daily reports that CS 106A: Programming Methodology is an introductory programming course taken by 1,600+ students from all academic disciplines each year (2015 Slashdot post on CS 106A's growing pains). According to the syllabus, CS 106A "uses the Python programming language" and there's "no prior programming experience required," although the schedule indicates a lot of ground is covered for someone new to coding (the same could be said of Harvard's famed CS50).

Lest some take Zhang to task for the sin of stating programming is hard, consider that Stanford's CS 106A website suggests the same, reporting that the median score on the midterm exam was only 68%, despite a plethora of review materials and sessions. CS 106A students were offered the chance to submit formal 'regrade requests' to try to improve their midterm scores and can also vie for "a Jamba Juice gift card and 100% on the final exam" by entering a Python programming contest -- one prize will be awarded for "Aesthetic merit", another for "Algorithmic sophistication" (a number of runners-up will be awarded "a grade boost similar to getting a + on one of their assignments").