Google is adding a smorgasbord of new features to its AI chatbot Bard, including support for new languages (Japanese and Korean), easier ways to export text to Google Docs and Gmail, visual search, and a dark mode. Most significantly, the company is removing the waitlist for Bard and making the system available in English in 180 countries and territories. From a report: It's also promising future features like AI image generation powered by Adobe and integration with third-party web services like Instacart and OpenTable. Collectively, the news is a shot in the arm for Bard, which was released two months ago for select users in the US and UK. The chatbot -- which Google still stresses is an experiment and not a replacement to its search engine -- has compared poorly to rivals like OpenAI's ChatGPT and Microsoft's new Bing chatbot. Notably, Bard made a factual error in its first-ever public demo (though this problem is common to all such bots). Now, Google is adding a lot of new features as well as upgrading Bard to use its new PaLM 2 language model. This should improve its general answers and usability.

Google says the upgraded Bard is particularly good at tackling coding queries, including debugging and explaining chunks of code in more than 20 languages, so some of today's upgrades are focused on this use case. These include the new dark mode, improved citations for code (which will not only offer sources but also explain the snippets), and a new export button. This can already be used to send code to Google's Colab platform but will now also work with another browser-based IDE, Replit (starting with Python queries).

For 16 years, Rik Farrow has been an editor for the long-running nonprofit Usenix. He's also been a consultant for 43 years (according to his biography at Usenix.org) — and even wrote the 1988 book Unix System Security: How to Protect Your Data and Prevent Intruders.

Today Farrow stopped by Slashdot to share his thoughts on Codon. rikfarrow writes: Researchers at MIT decided to build a compiler focused on speeding up genomics processing... Recently, they have posted their code on GitHub, and I gave it a test drive.
"Managed" languages produce code for a specific runtime (like JavaScript). Now Farrow's article at Usenix.org argues that Codon produces code "much faster than other managed languages, and in some cases faster than C/C++."

Codon-compiled code is faster because "it's compiled, variables are typed at compile time, and it supports parallel execution." But there's some important caveats: The "version of Python" part is actually an important point: the builders of Codon have built a compiler that accepts a large portion of Python, including all of the most commonly used parts — but not all... Duck typing means that the Codon compiler uses hints found in the source or attempts to deduce them to determine the correct type, and assigns that as a static type. If you wanted to process data where the type is unknown before execution, this may not work for you, although Codon does support a union type that is a possible workaround. In most cases of processing large data sets, the types are known in advance so this is not an issue...

Codon is not the same as Python, in that the developers have not yet implemented all the features you would find in Python 3.10, and this, along with duck typing, will likely cause problems if you just try and compile existing scripts. I quickly ran into problems, as I uncovered unsupported bits of Python, and, by looking at the Issues section of their Github pages, so have other people.

Codon supports a JIT feature, so that instead of attempting to compile complete scripts, you can just add a @codon.jit decorator to functions that you think would benefit from being compiled or executed in parallel, becoming much faster to execute...

Whether your projects will benefit from experimenting with Codon will mean taking the time to read the documentation. Codon is not exactly like Python. For example, there's support for Nvidia GPUs included as well and I ran into a limitation when using a dictionary. I suspect that some potential users will appreciate that Codon takes Python as input and produces executables, making the distribution of code simpler while avoiding disclosure of the source. Codon, with its LLVM backend, also seems like a great solution for people wanting to use Python for embedded projects.

My uses of Python are much simpler: I can process millions of lines of nginx logs in seconds, so a reduction in execution time means little to me. I do think there will be others who can take full advantage of Codon.
Farrow's article also points out that Codon "must be licensed for commercial use, but versions older than three years convert to an Apache license. Non-commercial users are welcome to experiment with Codon."

While working at Apple, Chris Lattner designed Swift to "fully leverage the power of LLVM," and "led a team for a while at Google to try to move Swift out of its Apple comfort zone, to become a replacement for Python in AI model development." That's according to a blog post by Jeremy Howard, an advisor to Lattner's Modular AI (which he co-founded in 2022 to build a next-generation AI platform for developers).

"But sadly," Howard writes, Swift "did not receive the support it needed from either Apple or from Google, and it was not ultimately successful." And yet... [W]hilst at Google Chris did develop another project which became hugely successful: MLIR. MLIR is a replacement for LLVM's intermediate representation [or IR] for the modern age of many-core computing and AI workloads. It's critical for fully leveraging the power of hardware like GPUs, TPUs, and the vector units increasingly being added to server-class CPUs.

So, if Swift was "syntax sugar for LLVM", what's "syntax sugar for MLIR"? The answer is: Mojo! Mojo is a brand new language that's designed to take full advantage of MLIR. And also Mojo is Python.

Wait what?

OK let me explain. Maybe it's better to say Mojo is Python++. It will be (when complete) a strict superset of the Python language. But it also has additional functionality so we can write high performance code that takes advantage of modern accelerators...

Whereas Swift was a brand new language packing all kinds of cool features based on latest research in programming language design, Mojo is, at its heart, just Python. This seems wise, not just because Python is already well understood by millions of coders, but also because after decades of use its capabilities and limitations are now well understood. Relying on the latest programming language research is pretty cool, but its potentially-dangerous speculation because you never really know how things will turn out...

A key trick in Mojo is that you can opt in at any time to a faster "mode" as a developer, by using "fn" instead of "def" to create your function. In this mode, you have to declare exactly what the type of every variable is, and as a result Mojo can create optimised machine code to implement your function. Furthermore, if you use "struct" instead of "class", your attributes will be tightly packed into memory, such that they can even be used in data structures without chasing pointers around. These are the kinds of features that allow languages like C to be so fast, and now they're accessible to Python programmers too — just by learning a tiny bit of new syntax...

I can't begin to describe all the little (and big!) ideas throughout Mojo's design and implementation — it's the result of Chris and his team's decades of work on compiler and language design and includes all the tricks and hard-won experience from that time — but what I can describe is an amazing result that I saw with my own eyes.
Mojo hasn't been released to the public yet, (other than an online "playground" with a waitlist where they're "rolling out access slowly.") But the blog post notes that creating a programming language's syntax is usually complex, error-prone, and controversial — a problem Mojo neatly avoids by "outsourcing" its syntax to an existing language, "which also happens to be the most widely used language today."

And "As a compiled language, Mojo's deployment story is basically the same as C," the post argues. [That is, "you can literally just make the compiled program available for direct download. It can be just 100k or so in size, and will launch and run quickly."]

"This means that Mojo is far more than a language for AI/ML applications. It's actually a version of Python that allows us to write fast, small, easily-deployed applications that take advantage of all available cores and accelerators!"

Python 3.12 was released today, with improvements to speed and efficiency, reports InfoWorld. Core developers explained the improvements at this year's PyCon convention in Salt Lake City, Utah, including efforts to reduce Python's memory use, make the interpreter faster, and optimize compilation for more efficient code: Subinterpreters is a mechanism where the Python runtime can have multiple interpreters running together inside a single process, as opposed to each interpreter being isolated in its own process (the current multiprocessing mechanism)... While subinterpreters have been available in the Python runtime for some time now, they haven't had an interface for the end user. Also, the messy state of Python's internals hasn't allowed subinterperters to be used effectively. With Python 3.12, core python developer Eric Snow and his cohort cleaned up Python's internals enough to make subinterpreters useful, and they are adding a minimal module to the Python standard library called interpreters. This gives programmers a rudimentary way to launch subinterpreters and execute code on them.

Snow's own initial experiments with subinterpreters significantly outperformed threading and multiprocessing. One example, a simple web service that performed some CPU-bound work, maxed out at 100 requests per second with threads, and 600 with multiprocessing. But with subinterpreters, it yielded 11,500 requests, and with little to no drop-off when scaled up from one client. The interpreters module has very limited functionality right now, and it lacks robust mechanisms for sharing state between subinterpreters. But Snow believes by Python 3.13 a good deal more functionality will appear, and in the interim developers are encouraged to experiment...

Python 3.11 introduced new bytecodes to the interpreter, called adaptive instructions. These instructions can be replaced automatically at runtime with versions specialized for a given Python type, a process called quickening. This saves the interpreter the step of having to look up what types the objects are, speeding up the whole process enormously. For instance, if a given addition operation regularly takes in two integers, that instruction can be replaced with one that assumes the operands are both integers... Python 3.12 has more adaptive specialization opcodes...
And starting with Python 3.12, object headers now use 96 bytes, which InfoWorld reports is "slightly less than half of what it was before."

An anonymous reader quotes a report from TechCrunch: In pursuit of "safer" text-generating models, Nvidia today released NeMo Guardrails, an open source toolkit aimed at making AI-powered apps more "accurate, appropriate, on topic and secure." Jonathan Cohen, the VP of applied research at Nvidia, says the company has been working on Guardrails' underlying system for "many years" but just about a year ago realized it was a good fit for models along the lines of GPT-4 and ChatGPT. "We've been developing toward this release of NeMo Guardrails ever since," Cohen told TechCrunch via email. "AI model safety tools are critical to deploying models for enterprise use cases."

Guardrails includes code, examples and documentation to "add safety" to AI apps that generate text as well as speech. Nvidia claims that the toolkit is designed to work with most generative language models, allowing developers to create rules using a few lines of code. Specifically, Guardrails can be used to prevent -- or at least attempt to prevent -- models from veering off topic, responding with inaccurate information or toxic language and making connections to "unsafe" external sources. Think keeping a customer service assistant from answering questions about the weather, for instance, or a search engine chatbot from linking to disreputable academic journals. "Ultimately, developers control what is out of bounds for their application with Guardrails," Cohen said. "They may develop guardrails that are too broad or, conversely, too narrow for their use case."

While companies like Zapier are using Guardrails to add a layer of safety to their generative models, Nvidia acknowledges that the toolkit isn't imperfect; it won't catch everything, in other words. Cohen also notes that Guardrails works best with models that are "sufficiently good at instruction-following," a la ChatGPT, and that use the popular LangChain framework for building AI-powered apps. That disqualifies some of the open source options out there. And -- effectiveness of the tech aside -- it must be emphasized that Nvidia isn't necessarily releasing Guardrails out of the goodness of its heart. It's a part of the company's NeMo framework, which is available through Nvidia's enterprise AI software suite and its NeMo fully managed cloud service. Any company can implement the open source release of Guardrails, but Nvidia would surely prefer that they pay for the hosted version instead.

Last year Python's massive PyPI repository of pre-written software packages had 235.7 billion downloads — a 57% annual growth in its download counts and bandwidth. So now Python's nonprofit Python Software Foundation has an announcement.

Their director of infrastructure said today that they're rolling out "the first step in our plan to build financial support and long-term sustainability of PyPI, while simultaneously giving our users one of our most requested features: organization accounts." Organizations on PyPI are self-managed teams, with their own exclusive branded web addresses. Our goal is to make PyPI easier to use for large community projects, organizations, or companies who manage multiple sub-teams and multiple packages.

We're making organizations available to community projects for free, forever, and to corporate projects for a small fee. Additional priority support agreements will be available to all paid subscribers, and all revenue will go right back into PyPI to continue building better support and infrastructure for all our users... Having more people using and contributing to Python every year is an fantastic problem to have, but it is one we must increase organizational capacity to accommodate. Increased revenue for PyPI allows it to become a staffed platform that can respond to support requests and attend to issues in a timeframe that is significantly faster than what our excellent (but thinly spread) largely volunteer team could reasonably handle.

We want to be very clear — these new features are completely optional. If features for larger projects don't sound like something that would be useful to you as a PyPI maintainer, then there is no obligation to create an organization and absolutely nothing about your PyPI experience will change for you.

We look forward to discussing what other features PyPI users would like to see tackled next...

Google is updating its Bard AI chatbot to help developers write and debug code. Rivals like ChatGPT and Bing AI have supported code generation, but Google says it has been "one of the top requests" it has received since opening up access to Bard last month. From a report: Bard can now generate code, debug existing code, help explain lines of code, and even write functions for Google Sheets. "We're launching these capabilities in more than 20 programming languages including C++, Go, Java, Javascript, Python and Typescript," explains Paige Bailey, group product manager for Google Research, in a blog post. You can ask Bard to explain code snippets or explain code within GitHub repos similar to how Microsoft-owned GitHub is implementing a ChatGPT-like assistant with Copilot. Bard will also debug code that you supply or even its own code if it made some errors or the output wasn't what you were looking for.

Amazon is making its AI-powered coding assistant CodeWhisperer free for individual developers, reports the Verge, "undercutting the $10 per month pricing of its Microsoft-made rival." Amazon launched CodeWhisperer as a preview last year, which developers can use within various integrated development environments (IDEs), like Visual Studio Code, to generate lines of code based on a text-based prompt....

CodeWhisperer automatically filters out any code suggestions that are potentially biased or unfair and flags any code that's similar to open-source training data. It also comes with security scanning features that can identify vulnerabilities within a developer's code, while providing suggestions to help close any security gaps it uncovers. CodeWhisperer now supports several languages, including Python, Java, JavaScript, TypeScript, and C#, including Go, Rust, PHP, Ruby, Kotlin, C, C++, Shell scripting, SQL, and Scala.
Here's how Amazon's senior developer advocate pitched the usefulness of their "real-time AI coding companion": Helping to keep developers in their flow is increasingly important as, facing increasing time pressure to get their work done, developers are often forced to break that flow to turn to an internet search, sites such as StackOverflow, or their colleagues for help in completing tasks. While this can help them obtain the starter code they need, it's disruptive as they've had to leave their IDE environment to search or ask questions in a forum or find and ask a colleague — further adding to the disruption. Instead, CodeWhisperer meets developers where they are most productive, providing recommendations in real time as they write code or comments in their IDE. During the preview we ran a productivity challenge, and participants who used CodeWhisperer were 27% more likely to complete tasks successfully and did so an average of 57% faster than those who didn't use CodeWhisperer....

It provides additional data for suggestions — for example, the repository URL and license — when code similar to training data is generated, helping lower the risk of using the code and enabling developers to reuse it with confidence.

The Python Software Foundation is "concerned that proposed EU cybersecurity laws will leave open source organizations and individuals unfairly liable for distributing incorrect code," according to the Register. The PSF reviewed the EU's proposed "Cyber Resilience Act" and "Product Liability Act" and reports "issues that put the mission of our organization and the health of the open-source software community at risk."

From the Register's report: "If the proposed law is enforced as currently written, the authors of open-source components might bear legal and financial responsibility for the way their components are applied in someone else's commercial product," the PSF said in a statement shared on Tuesday by executive director Deb Nicholson. "The existing language makes no differentiation between independent authors who have never been paid for the supply of software and corporate tech behemoths selling products in exchange for payments from end-users...."

The PSF argues the EU lawmakers should provide clear exemptions for public software repositories that serve the public good and for organizations and developers hosting packages on public repositories. "We need it to be crystal clear who is on the hook for both the assurances and the accountability that software consumers deserve," the PSF concludes. The PSF is asking anyone who shares its concerns to convey that sentiment to an appropriate EU Member of Parliament by April 26, while amendments focused on protecting open source software are being considered.

Bradley Kuhn, policy fellow at the Software Freedom Conservancy, told The Register that the free and open source (FOSS) community should think carefully about the scope of the exemptions being sought. "I'm worried that many in FOSS are falling into a trap that for-profit companies have been trying to lay for us on this issue," he said. "While it seems on the surface that a blanket exception for FOSS would be a good thing for FOSS, in fact, this an attempt for companies to get the FOSS community to help them skirt their ordinary product liability. For profit companies that deploy FOSS should have the same obligations for security and certainty for their users as proprietary software companies do."
The article points out that numerous tech organizations are urging clarifications in the proposed regulations, including NLnet Labs and the Eclipse Foundation.

An anonymous reader shares a report: About a year ago, Google announced its Assured Open Source Software (Assured OSS) service, a service that helps developers defend against supply chain security attacks by regularly scanning and analyzing some of the world's most popular software libraries for vulnerabilities. Today, Google is launching Assured OSS into general availability with support for well over a thousand Java and Python packages -- and while Google didn't initially disclose pricing when it first announced the service, the company has now revealed that it will be available for free.

Software development has long depended on third-party libraries (which are often maintained by only a single developer), but it wasn't until the industry got hit with a number of high-profile exploits that everyone (including the White House) perked up and started taking software supply chain security seriously. Now, you can't attend an open source conference without hearing about Software Bills of Materials (SBOMs), artifact registries and similar topics. It's no surprise then that Google, which has long been at the forefront of releasing open-source products, launched a service like Assured OSS.

Google promises that it will constantly keep these libraries up to date (without creating forks) and continuously scan for known vulnerabilities, do fuzz tests to discover new ones and then fix these issues and contribute these fixes back upstream. The company notes that when it first launched the service with around 250 Java libraries, it was responsible for discovering 48% of the new CVEs for these libraries and subsequently addressing them.

An anonymous reader quotes a report from Ars Technica: Debugging a faulty program can be frustrating, so why not let AI do it for you? That's what a developer that goes by "BioBootloader" did by creating Wolverine, a program that can give Python programs "regenerative healing abilities," reports Hackaday. (Yep, just like the Marvel superhero.) "Run your scripts with it and when they crash, GPT-4 edits them and explains what went wrong," wrote BioBootloader in a tweet that accompanied a demonstration video. "Even if you have many bugs it'll repeatedly rerun until everything is fixed."

In the demo video for Wolverine, BioBootloader shows a side-by-side window display, with Python code on the left and Wolverine results on the right in a terminal. He loads a custom calculator script in which he adds a few bugs on purpose, then executes it. "It runs it, it sees the crash, but then it goes and talks to GPT-4 to try to figure out how to fix it," he says. GPT-4 returns an explanation for the program's errors, shows the changes that it tries to make, then re-runs the program. Upon seeing new errors, GPT-4 fixes the code again, and then it runs correctly. In the end, the original Python file contains the changes added by GPT-4.

An anonymous reader shares this report from Tom's Hardware: When we think about Raspberry Pi, we normally picture single-board computers, but the Raspberry Pi Foundation was started to help kids learn about computers and it wants to help whether or not you own its hardware. The non-profit arm of Raspberry Pi this week released its new, browser-based code editor that's designed for young people (or any people) who are learning.

The Raspberry Pi Code Editor, which is considered to be in beta, is available to everyone for free right now at editor.raspberrypi.org. The editor is currently designed to work with Python only, but the organization says that support for other languages such as HTML, JavaScript and CSS is coming....

The Raspberry Pi Foundation already had a nice set of Python tutorials on its site, but it has adapted some of them to open sample code directly in the online editor....The Pi Foundation says that it plans to add a number of features to the Code Editor, including sharing and collaboration. The organization also plans to release the editor as an open-source project so anyone can modify it.
There's a pane showing your code's output when you click the "Run" button (plus a smaller pane for adding additional files to a project).

Tom's Hardware notes that "Since the entire programming experience takes place online, there's no way (at least right now) to use Python to control local hardware on your PC or your Raspberry Pi." But on the plus side, "If you create a free account on raspberrypi.org, which I did, the system will save all of your projects in the cloud and you can reload them any time you want. You can also download all the files in a project as a .zip file."

InfoWorld reports: Zig, a general purpose programming language that interacts with C/C++ programs and promises to be a modern alternative to C, has made an appearance in the Tiobe index of programming language popularity. Zig entered the top 50 in the April edition of the Tiobe Programming Community Index, ranking 46th, albeit with a rating of just 0.19%. By contrast, the Google-promoted Carbon language, positioned as an experimental successor to C++, ranked just 168th.
Tiobe CEO Paul Jansen argues that high-performance languages "are booming due to the vast amounts of data that needs to be processed nowadays. As a result, C and C++ are doing well in the top 10 and Rust seems to be a keeper in the top 20." Zig has all the nice features of C and C++ (such as explicit memory management enhanced with option types) and has abandoned the not-so-nice features (such as the dreadful preprocessing). Entering the top 50 is no guarantee to become a success, but it is at least a first noteworthy step. Good luck Zig!
Tiobe bases its monthly ranking of programming language popularity on search engine results for courses, third party vendors, and engineers. Here's what they's calculated for the most popular programming languages in April of 2023:

• Python
• C
• Java
• C++
• C#
• Visual Basic
• JavaScript
• SQL
• PHP
• Go

April's top 10 was nearly identical to the rankings a year ago, but assembly language fell from 2022's #8 position to #12 in 2023. SQL and PHP rose one rank (into 2023's #8 and #9 positions) — and as in March, the rankings now shows Go as the 10th most popular programming language.

theodp writes: Are you ready to rock it with #datascience?" asks a tweet from Club for the Future, the tax-exempt foundation founded and funded by Jeff Bezos's Blue Origin, which is partnering with Microsoft's Hacking STEM to show how data science is used to determine a Go/No-Go launch of a Blue Origin New Shepard rocket. Interestingly, while Amazon founder Bezos and Microsoft CEO Satya Nadella are big backers of nonprofit Code.org and joined other tech CEOs for CS last fall to get the nation's Governors to "update the K-12 curriculum, for every student in every school to have the opportunity to learn computer science," Microsoft and Blue Origin have opted to teach kids aged 11-15 good old-fashioned Excel skills in their Introduction to the Data Science Process mini-course, not Python or R.

"Excel is a tool used around the world to work with data," Microsoft explains to teachers who have been living under a rock since 1985. "In these activities, students learn how to use Excel and complete all steps of a mission by engaging in the data science process. In this mission, students analyze key weather data in determining flight safety parameters for a New Shepard rocket and ultimately make a Go/No-Go decision for launch. Students learn how to use Excel while engaging in this dynamic Data Science Process activity [which is not unlike PLATO 'data science' activities of 50 years ago]." Blue Origin last September pledged to inspire youth to pursue space STEM careers as part of the Biden Administration's efforts to increase the space industry's capacity to meet the rising demand for the skilled technical workforce.

IEEE Spectrum reports on Codon, a Python compiler specifically developed to, as they put it, "turbocharge Python's Notoriously slow compiler."

"We do type checking during the compilation process, which lets us avoid all of that expensive type manipulation at runtime," says Ariya Shajii, an MIT CSAIL graduate student and lead author on a recent paper about Codon. Without any unnecessary data or type checking during runtime, Codon results in zero overhead, according to Shajii. And when it comes to performance, "Codon is typically on par with C++. Versus Python, what we usually see is 10 to 100x improvement," he says. But Codon's approach comes with its trade-offs. "We do this static type checking, and we disallow some of the dynamic features of Python, like changing types at runtime dynamically," says Shajii. "There are also some Python libraries we haven't implemented yet...."

Codon was initially designed for use in genomics and bioinformatics. "Data sets are getting really big in these fields, and high-level languages like Python and R are too slow to handle terabytes per set of sequencing data," says Shajii. "That was the gap we wanted to fill — to give domain experts who are not necessarily computer scientists or programmers by training a way to tackle large data without having to write C or C++ code." Aside from genomics, Codon could also be applied to similar applications that process massive data sets, as well as areas such as GPU programming and parallel programming, which the Python-based compiler supports. In fact, Codon is now being used commercially in the bioinformatics, deep learning, and quantitative finance sectors through the startup Exaloop, which Shajii founded to shift Codon from an academic project to an industry application.

To enable Codon to work with these different domains, the team developed a plug-in system. "It's like an extensible compiler," Shajii says. "You can write a plug-in for genomics or another domain, and those plug-ins can have new libraries and new compiler optimizations...." In terms of what's next for Codon, Shajii and his team are currently working on native implementations of widely used Python libraries, as well as library-specific optimizations to get much better performance out of these libraries. They also plan to create a widely requested feature: a WebAssembly back end for Codon to enable running code on a Web browser.

A YouTuber has published a video where he tricks ChatGPT into generating usable Windows 95 activation keys. Tom's Hardware reports: After asking Open AI's chatbot directly for Windows 95 keys, he received an expected reasoned refusal. YouTuber Enderman then asked the same thing but from a different angle. The result was a success which was somewhat limited by ChatGPT's ability to process natural language requests into formulas. [...] Some of the tested results were checked by attempting to activate a fresh Windows 95 install in a virtual machine. While the keys passed a casual inspection, it turns out that only about 1-in-30 keys seem to work as expected.

So what is the problem with these keys? Enderman complains that "the only issue keeping ChatGPT from successfully generating valid Windows 95 keys almost every attempt is the fact that it can't count the sum of digits and it doesn't know divisibility." In the five-digit string divisible by seven section, the AI appears to provide a stream of random numbers that don't pass this simple mathematical test. The report adds: "[W]hile quizzing ChatGPT about key generating may be fun, it would have probably been more productive to manipulate the AI into writing a Python script to generate a conforming key or to DIY it."

"With the recent attention on ChatGPT and OpenAI's release of their APIs, many developers have developed clients for modern platforms to talk to this super smart AI chatbot," writes maker/retro coding enthusiast yeokm1 . "However I'm pretty sure almost nobody has written one for a vintage platform like MS-DOS."

They share a blog post with all the details — including footage of their client ultimately running on a vintage IBM PC from 1984 (with a black and orange monitor and those big, boxy keys). "3.5 years ago, I wrote a Slack client to run on Windows 3.1," the blog post explains. "I thought to try something different this time and develop for an even older platform as a challenge."

One challenge was just finding a networking API for DOS. But everything came together, with the ChatGPT-for-DOS app written using Visual Studio Code text editor (testing on a virtual machine running DOS 6.22), parsing the JSON output from OpenAI's Chat Completion API. "And before you ask, I did not use ChatGPT for help to code this app in any way," the blog post concludes. But after the app was working, he used it to ask ChatGPT how one would build such an app — and ChatGPT erroneously suggested breezily that he just try accessing OpenAI's Python API from the DOS command line.

"What is the AI smoking...?"

Microsoft's GitHub unit created one of the first widely deployed programs using OpenAI's language-generation tools -- an app called Copilot that helped software developers write computer code. Now GitHub is adding a chat and voice feature that will let programmers ask how to accomplish certain coding tasks. From a report: The new version announced Wednesday is called Copilot X, which GitHub Chief Executive Officer Thomas Dohmke said he demonstrated to one of his children by asking it how to program a snake game in Python. The chat window can provide explanations of what segments of code are meant to do, create ways to test the code and propose fixes for bugs. Developers can also give instructions or ask questions using their voice.

GitHub first previewed Copilot in 2021 and widely released it last year. The initial product contained a completion tool that suggested snippets of programming code as a software developer typed. It attracted hundreds of thousands of developers by November and its product name had become short-hand for Microsoft's strategy to deploy these kinds of assistive technologies to a wide array of its products, from Office software to security programs. Now that OpenAI's ChatGPT chatbot has made a splash in popular culture, companies are trying to follow Microsoft in embedding the research lab's tools into products and business strategies. At the same time, rivals such as Alphabet's Google are releasing chatbot competitors.

Codon is a new "high-performance Python compiler that compiles Python code to native machine code without any runtime overhead," according to its README file on GitHub. Typical speedups over Python are on the order of 10-100x or more, on a single thread. Codon's performance is typically on par with (and sometimes better than) that of C/C++. Unlike Python, Codon supports native multithreading, which can lead to speedups many times higher still.
Its development team includes researchers from MIT's Computer Science and Artificial Intelligence lab, according to this announcement from MIT shared by long-time Slashdot reader Futurepower(R): The compiler lets developers create new domain-specific languages (DSLs) within Python — which is typically orders of magnitude slower than languages like C or C++ — while still getting the performance benefits of those other languages. "We realized that people don't necessarily want to learn a new language, or a new tool, especially those who are nontechnical. So we thought, let's take Python syntax, semantics, and libraries and incorporate them into a new system built from the ground up," says Ariya Shajii SM '18, PhD '21, lead author on a new paper about the team's new system, Codon. "The user simply writes Python like they're used to, without having to worry about data types or performance, which we handle automatically — and the result is that their code runs 10 to 100 times faster than regular Python. Codon is already being used commercially in fields like quantitative finance, bioinformatics, and deep learning."

The team put Codon through some rigorous testing, and it punched above its weight. Specifically, they took roughly 10 commonly used genomics applications written in Python and compiled them using Codon, and achieved five to 10 times speedups over the original hand-optimized implementations.... The Codon platform also has a parallel backend that lets users write Python code that can be explicitly compiled for GPUs or multiple cores, tasks which have traditionally required low-level programming expertise.... Part of the innovation with Codon is that the tool does type checking before running the program. That lets the compiler convert the code to native machine code, which avoids all of the overhead that Python has in dealing with data types at runtime.

"Python is the language of choice for domain experts that are not programming experts. If they write a program that gets popular, and many people start using it and run larger and larger datasets, then the lack of performance of Python becomes a critical barrier to success," says Saman Amarasinghe, MIT professor of electrical engineering and computer science and CSAIL principal investigator. "Instead of needing to rewrite the program using a C-implemented library like NumPy or totally rewrite in a language like C, Codon can use the same Python implementation and give the same performance you'll get by rewriting in C. Thus, I believe Codon is the easiest path forward for successful Python applications that have hit a limit due to lack of performance."

The other piece of the puzzle is the optimizations in the compiler. Working with the genomics plugin, for example, will perform its own set of optimizations that are specific to that computing domain, which involves working with genomic sequences and other biological data, for example. The result is an executable file that runs at the speed of C or C++, or even faster once domain-specific optimizations are applied.

"Google's Go language has re-entered the top 10 of the Tiobe index of programming language popularity, after a nearly six-year absence," reports InfoWorld: Go ranks 10th in the March edition of the index, after placing 11th the previous month. The language last appeared in the top 10 in July 2017.

The re-emergence of Go in the March 2023 index is being attributed to its popularity with software engineers and its strength in combining the right features, namely built-in concurrency, garbage collection, static typing, and good performance. Google's backing also helps, improving long-term trust in the language, Tiobe said.
The languages Go beat out include "assembly language" at #11, followed by MATLAB, Delphi/Object Pascal, Scratch, and Classic Visual Basic.

Here's the complete top-ten most popular programming languages, according to TIOBE:

• Python
• C
• Java
• C++
• C#
• Visual Basic
• JavaScript
• SQL
• PHP
• Go