I had the opportunity to interview esteemed programmer Steve Yegge for the SourceForge Podcast to ask him all about AI-powered coding assistants and the future of programming. "We're moving from where you have to write the code to where the LLM will write the code and you're just having a conversation with it about the code," said Yegge. "That is much more accessible to people who are just getting into the industry."

Steve has nearly 30 years of programming experience working at Geoworks, Amazon, Google, Grab and now SourceGraph, working to build out the Cody AI assistant platform. Here's his Wikipedia page. He's not shy about sharing his opinions or predictions for the industry, no matter how difficult it may be for some to hear. "I'm going to make the claim that ... line-oriented programming, which we've done for the last 40, 50 years, ... is going away. It is dying just like assembly language did, and it will be completely dead within five years."

You can watch the episode on YouTube and stream on all major podcast platforms. A transcription of the podcast is available here.

Software engineer David Gomes writes in a blog post: I sometimes need to search for a website that will "convert a PNG to SVG", or "remove page from PDF" or "resize svg". And these apps are... okay. I don't really trust most of them with my data, and also a lot of times they just don't work or have too many ads. So, I've been noticing a trend of people just using AI agents to create full blown apps for these simple use cases.

I decided to try it myself for a "resize SVG" app since I recently had to go through a bunch of websites to do this. So, I pulled up Replit Agent and even though I've used it before, it doesn't cease to amaze me just how insanely good it is. The level of polish on this product is unlike any other AI agent out there right now. It starts off by drawing up a plan and asking you for feedback on that plan. Then, it'll just go to town and try to build the app. But what's super clever about it is that the agent asks you for feedback along the way. Effectively, the Replit Agent guides you, not the other way around (as one might have expected).

Anton Shilov reports via Tom's Hardware: FFmpeg is an open-source video decoding project developed by volunteers who contribute to its codebase, fix bugs, and add new features. The project is led by a small group of core developers and maintainers who oversee its direction and ensure that contributions meet certain standards. They coordinate the project's development and release cycles, merging contributions from other developers. This group of developers tried to implement a handwritten AVX512 assembly code path, something that has rarely been done before, at least not in the video industry.

The developers have created an optimized code path using the AVX-512 instruction set to accelerate specific functions within the FFmpeg multimedia processing library. By leveraging AVX-512, they were able to achieve significant performance improvements -- from three to 94 times faster -- compared to standard implementations. AVX-512 enables processing large chunks of data in parallel using 512-bit registers, which can handle up to 16 single-precision FLOPS or 8 double-precision FLOPS in one operation. This optimization is ideal for compute-heavy tasks in general, but in the case of video and image processing in particular.

The benchmarking results show that the new handwritten AVX-512 code path performs considerably faster than other implementations, including baseline C code and lower SIMD instruction sets like AVX2 and SSSE3. In some cases, the revamped AVX-512 codepath achieves a speedup of nearly 94 times over the baseline, highlighting the efficiency of hand-optimized assembly code for AVX-512.

App developers Musi and Sarafan Mobile have sued Apple and Google in California federal court over app removals they claim were unjustified, highlighting tensions over the tech giants' intellectual property enforcement policies. Musi's music-streaming app was removed after YouTube complained about interface infringement, while Sarafan's "Reely" app was taken down following Instagram's claims about logo similarity.

Both developers say the platforms breached their agreements by removing apps without sufficient evidence. The lawsuits underscore broader concerns about Apple and Google's dominance in app distribution. Their private IP dispute systems operate outside traditional legal frameworks, with platforms making unilateral decisions that can effectively shut down businesses, according to University of New Hampshire law professor Peter Karol. [...]

"In a court proceeding, you can see here's a complaint with the allegations, and then we have the defendant respond, and then we have a judge come out with an opinion saying, 'Is the mark valid? Is the mark infringed?'" said Lisa Ramsey, law professor at University of San Diego. Google and Apple's systems, meanwhile, are "a black box."

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

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

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

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

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

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

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

"AI-driven 0-day detection is here," argues a new blog post from ZeroPath, makers of a GitHub app that "detects, verifies, and issues pull requests for security vulnerabilities in your code."

They write that AI-assisted security research "has been quietly advancing" since early 2023, when researchers at the DARPA and ARPA-H's Artificial Intelligence Cyber Challenge demonstrated the first practical applications of LLM-powered vulnerability detection — with new advances continuing. "Since July 2024, ZeroPath's tool has uncovered critical zero-day vulnerabilities — including remote code execution, authentication bypasses, and insecure direct object references — in popular AI platforms and open-source projects." And they ultimately identified security flaws in projects owned by Netflix, Salesforce, and Hulu by "taking a novel approach combining deep program analysis with adversarial AI agents for validation. Our methodology has uncovered numerous critical vulnerabilities in production systems, including several that traditional Static Application Security Testing tools were ill-equipped to find..." TL;DR — most of these bugs are simple and could have been found with a code review from a security researcher or, in some cases, scanners. The historical issue, however, with automating the discovery of these bugs is that traditional SAST tools rely on pattern matching and predefined rules, and miss complex vulnerabilities that do not fit known patterns (i.e. business logic problems, broken authentication flaws, or non-traditional sinks such as from dependencies). They also generate a high rate of false positives.

The beauty of LLMs is that they can reduce ambiguity in most of the situations that caused scanners to be either unusable or produce few findings when mass-scanning open source repositories... To do this well, you need to combine deep program analysis with an adversarial agents that test the plausibility of vulnerabilties at each step. The solution ends up mirroring the traditional phases of a pentest — recon, analysis, exploitation (and remediation which is not mentioned in this post)...

AI-driven vulnerability detection is moving fast... What's intriguing is that many of these vulnerabilities are pretty straightforward — they could've been spotted with a solid code review or standard scanning tools. But conventional methods often miss them because they don't fit neatly into known patterns. That's where AI comes in, helping us catch issues that might slip through the cracks.
"Many vulnerabilities remain undisclosed due to ongoing remediation efforts or pending responsible disclosure processes," according to the blog post, which includes a pie chart showing the biggest categories of vulnerabilities found:

• 53%: Authorization flaws, including roken access control in API endpoints and unauthorized Redis access and configuration exposure. ("Impact: Unauthorized access, data leakage, and resource manipulation across tenant boundaries.")

• 26%: File operation issues, including directory traversal in configuration loading and unsafe file handling in upload features. ("Impact: Unauthorized file access, sensitive data exposure, and potential system compromise.")

• 16%: Code execution vulnerabilities, including command injection in file processing and unsanitized input in system commands. ("Impact: Remote code execution, system command execution, and potential full system compromise.")

The company's CIO/cofounder was "former Red Team at Tesla," according to the startup's profile at YCombinator, and earned over $100,000 as a bug-bounty hunter. (And another co-founded is a former Google security engineer.)

Thanks to Slashdot reader Mirnotoriety for sharing the article.

GitHub Copilot will switch from using exclusively OpenAI's GPT models to a multi-model approach, adding Anthropic's Claude 3.5 Sonnet and Google's Gemini 1.5 Pro. Ars Technica reports: First, Anthropic's Claude 3.5 Sonnet will roll out to Copilot Chat's web and VS Code interfaces over the next few weeks. Google's Gemini 1.5 Pro will come a bit later. Additionally, GitHub will soon add support for a wider range of OpenAI models, including GPT o1-preview and o1-mini, which are intended to be stronger at advanced reasoning than GPT-4, which Copilot has used until now. Developers will be able to switch between the models (even mid-conversation) to tailor the model to fit their needs -- and organizations will be able to choose which models will be usable by team members.

The new approach makes sense for users, as certain models are better at certain languages or types of tasks. "There is no one model to rule every scenario," wrote [GitHub CEO Thomas Dohmke]. "It is clear the next phase of AI code generation will not only be defined by multi-model functionality, but by multi-model choice." It starts with the web-based and VS Code Copilot Chat interfaces, but it won't stop there. "From Copilot Workspace to multi-file editing to code review, security autofix, and the CLI, we will bring multi-model choice across many of GitHub Copilot's surface areas and functions soon," Dohmke wrote. There are a handful of additional changes coming to GitHub Copilot, too, including extensions, the ability to manipulate multiple files at once from a chat with VS Code, and a preview of Xcode support. GitHub also introduced "Spark," a natural language-based app development tool that enables both non-coders and coders to create and refine applications using conversational prompts. It's currently in an early preview phase, with a waitlist available for those who are interested.

Google has integrated AI deeply across its operations, with over 25% of its new code generated by AI. CEO Sundar Pichai announced the milestone during the company's third quarter 2024 earnings call. The Verge reports: AI is helping Google make money as well. Alphabet reported $88.3 billion in revenue for the quarter, with Google Services (which includes Search) revenue of $76.5 billion, up 13 percent year-over-year, and Google Cloud (which includes its AI infrastructure products for other companies) revenue of $11.4 billion, up 35 percent year-over-year. Operating incomes were also strong. Google Services hit $30.9 billion, up from $23.9 billion last year, and Google Cloud hit $1.95 billion, significantly up from last year's $270 million. "In Search, our new AI features are expanding what people can search for and how they search for it," CEO Sundar Pichai says in a statement. "In Cloud, our AI solutions are helping drive deeper product adoption with existing customers, attract new customers and win larger deals. And YouTube's total ads and subscription revenues surpassed $50 billion over the past four quarters for the first time."

It was originally created back in 2005 by Sun Microsystems for its proprietary Solaris Unix systems, "for troubleshooting kernel and application problems on production systems in real time," explains Wikipedia. "DTrace can be used to get a global overview of a running system, such as the amount of memory, CPU time, filesystem and network resources used by the active processes," explains its Wikipedia entry.

But this week, Gentoo announced: The real, mythical DTrace comes to Gentoo! Need to dynamically trace your kernel or userspace programs, with rainbows, ponies, and unicorns — and all entirely safely and in production?! Gentoo is now ready for that!

Just emerge dev-debug/dtrace and you're all set. All required kernel options are already enabled in the newest stable Gentoo distribution kernel...

Documentation? Sure, there's lots of it. You can start with our DTrace wiki page, the DTrace for Linux page on GitHub, or the original documentation for Illumos. Enjoy!
Thanks to Heraklit (Slashdot reader #29,346) for sharing the news.

theodp writes: "Computer science education is a necessity for all students," argues tech-backed nonprofit Code.org in its newly-published 2024 State of Computer Science Education (Understanding Our National Imperative) report. "Students of all identities and chosen career paths need quality computer science education to become informed citizens and confident creators of content and digital tools."

In the 200-page report, Code.org pays special attention to participation in "foundational computer science courses" in high school. "Across the country, 60% of public high schools offer at least one foundational computer science course," laments Code.org (curiously promoting a metric that ignores school size which nonetheless was embraced by Education Week and others).

"A course that teaches foundational computer science includes a minimum amount of time applying learned concepts through programming (at least 20 hours of programming/coding for grades 9-12 high schools)," Code.org explains in a separate 13-page Defining Foundational Computer Science document. Interestingly, Code.org argues that Data and Informatics courses -- in which "students may use Oracle WebDB, SQL, PL/SQL, SPSS, and SAS" to learn "the K-12 CS Framework concepts about data and analytics" -- do not count, because "the course content focuses on querying using a scripting language rather than creating programs [the IEEE's Top Programming Languages 2024 begs to differ]." Code.org similarly dissed the use of the Wolfram Language for broad educational use back in 2016.

With its insistence on the importance of kids taking Code.org-defined 'programming' courses in K-12 to promote computational thinking, it's probably no surprise to see that the data behind the 2024 State of Computer Science Education report was prepared using Python (the IEEE's top programming language) and presented to the public in a Jupyter notebook. Just kidding. Ironically, the data behind the 2024 State of Computer Science Education analysis is prepared and presented by Code.org in a no-code Tableau workbook.

"After two years of being beaten with the memory-safety stick, the C++ community has published a proposal to help developers write less vulnerable code," reports the Register.

"The Safe C++ Extensions proposal aims to address the vulnerable programming language's Achilles' heel, the challenge of ensuring that code is free of memory safety bugs..." Acknowledging the now deafening chorus of calls to adopt memory safe programming languages, developers Sean Baxter, creator of the Circle compiler, and Christian Mazakas, from the C++ Alliance, argue that while Rust is the only popular systems level programming language without garbage collection that provides rigorous memory safety, migrating C++ code to Rust poses problems. "Rust lacks function overloading, templates, inheritance and exceptions," they explain in the proposal. "C++ lacks traits, relocation and borrow checking. These discrepancies are responsible for an impedance mismatch when interfacing the two languages. Most code generators for inter-language bindings aren't able to represent features of one language in terms of the features of another."

Though DARPA is trying to develop better automated C++ to Rust conversion tools, Baxter and Mazakas argue telling veteran C++ developers to learn Rust isn't an answer... The Safe C++ project adds new technology for ensuring memory safety, Baxter explained, and isn't just a reiteration of best practices. "Safe C++ prevents users from writing unsound code," he said. "This includes compile-time intelligence like borrow checking to prevent use-after-free bugs and initialization analysis for type safety." Baxter said that rewriting a project in a different programming language is costly, so the aim here is to make memory safety more accessible by providing the same soundness guarantees as Rust at a lower cost. "With Safe C++, existing code continues to work as always," he explained. "Stakeholders have more control for incrementally opting in to safety."

The next step, Baxter said, involves greater participation from industry to help realize the Safe C++ project. "The foundations are in: We have fantastic borrow checking and initialization analysis which underpin the soundness guarantees," he said. "The next step is to comprehensively visit all of C++'s features and specify memory-safe versions of them. It's a big effort, but given the importance of reducing C++ security vulnerabilities, it's an effort worth making."

An anonymous reader quotes a report from MacRumors: Apple today reminded developers that the EU trader requirement in the European Union is now being enforced. Developers who distribute apps in the EU will now need to share information that includes address, phone number, and email address on the EU App Store. Submitting updates for apps on the App Store in the European Union now requires trader information that's added via App Store Connect, with those details shared on each developer's App Store page. App updates can no longer be submitted without trader information, and starting on February 17, 2025, apps that do not have a trader status set will be removed from the App Store in the EU until trader status is provided and verified.

The Digital Services Act (DSA) in the European Union requires Apple to verify and display trader contact information for all "traders" who are distributing apps on the App Store in the European Union. Developers who make money from the App Store through either an upfront purchase price or through in-app purchases are considered traders, regardless of size. Contact information for each developer that is considered a trader will be publicly available, and there will undoubtedly be some developers that are unhappy with the requirement. Independent developers and small companies may not have dedicated business addresses and phone numbers to provide, and will likely be reluctant to provide their personal contact information. You can learn more about the requirements on Apple's website.

Though native Linux game servers have been scarce over the last two decades, "I've seen people using the Box64 emulator to play x86_64 games on ARM devices," writes Slashdot reader VennStone. "It got me thinking: why not apply this to game servers...?

"I thought it would be fun to see if I could build a super low-power Trackmania 2 server using a Raspberry Pi Zero 2 W."

They dubbed the experiment "Trackberry", and shared all the technical details in a blog post at Interfacing Linux (includinga video). For example, they installed PyEnv so it could create a virtual environment for the PyPlanet server controller. ("That's right, your little Pi Zero 2 W is about to compile some software, slowly....")

But ultimately "it turns out that the A53 can run not only the server but also the server controller, with minimal effort. Five players push one core to around 50% load, while the others handle the database and controller." WHY STOP THERE? There are a gang of x86 Linux servers that could potentially run with Box64. Imagine playing Pirraria, 7 Days to Pi, Counter-Pi 2, Pitorio, and countless others! Granted, you may need a more powerful device than a Raspberry Pi Zero 2 W. I'll leave that research up to you.

My main takeaway from this experiment? Box64 is straight-up Scandinavian witchcraft and is not to be trifled with. Not even a little bit.

That said, it introduces a compelling option for those of us looking to run dedicated game servers that don't require much in the way of system resources. Under load, TrackBerry averages 2.8 watts and, according to the scientific number digits below, ends up running just under $3.00 a year or $0.25 a month. I find the concept of having a stack of microSD cards, each holding a different game server, neat....

You can see TrackBerry in action every Tuesday and Friday on Twitch...

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

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

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

• PHP (dropping from #8 to #15)

• SQL (dropping from #9 to #11)

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

This week (MIT-licensed) WebAssembly runtime Wasmer announced "a major milestone in making any software run with WebAssembly."

The announcement's headline? Running Clang in the browser using WebAssembly... Thanks to the newest release of Wasmer (4.4) and the Wasmer JS SDK (0.8.0) you can now run [compiler front-end] clang anywhere Wasmer runs! This allows compiling C programs from virtually anywhere. Including Javascript and your preferred browser! (we tested Chrome, Safari and Firefox and everything is working like a charm)...

- You can compile C code to WebAssembly easily just using the Wasmer CLI: no toolchains or complex installations needed, install Wasmer and you are ready to go...!

- You can compile C projects directly from JavaScript...!

- We expect online IDEs to start adopting the SDK to allow their users compile and run C programs in the browser....

Do you want to use clang in your Javascript project? Thanks to our newly released Wasmer JS SDK you can do it easily, in both the browser and Node.js/Bun etc... Wasmer's clang can even optimize the file for you automatically using wasm-opt under the hood (Clang automatically detects if wasm-opt is used, and it will be automatically called when optimizing the file). Imagine using Emscripten without needing its toolchain installed — or even better, imagine running Emscripten in the browser.
The announcement looks to a future of compiling native Python libraries, when "any project depending on LLVM can now be easily compiled to WebAssembly..."

"This is the beginning of an awesome journey, we can't wait to see what you create next with this."

80% of software engineers will need to upskill by 2027 to keep pace with generative AI's growing demands, according to Gartner. The consultancy predicts AI will transform the industry in three phases. Initially, AI tools will boost productivity, particularly for senior developers. Subsequently, "AI-native software engineering" will emerge, with most code generated by AI. Long-term, AI engineering will rise as enterprise adoption increases, requiring a new breed of professionals skilled in software engineering, data science, and machine learning.

Long-time Slashdot reader Bismillah writes: Python New Zealand has gone through some rough times lately, with its then-treasurer stealing money from the society.. Things were looking really serious for a while, with Python NZ looking at being liquidated due to the theft of funds.

However, there is a silver lining to the story, as the free and open source movement rallied behind Python NZ and got them out of a serious pickle.
"Our friends at Linux Australia and at the Python Software Foundation went well above and beyond to support us, and save us," says Tom Eastman president of Python New Zealand, in an article from interest.co.nz.

He also says he hopes the treasure is ordered by the court to pay restitution. (In the article the treasurer confirms that he's pleaded guilty to the theft, which took place between February 2019 and October 2023 — leaving Python NZ owing conference supplies around $55,000.) "We had $26 in the bank accounts," Eastman tells the site.

The group now has new transparency and accountability measures...

At its DevDay event today, OpenAI announced that it is giving third-party developers access to its speech-to-speech engine that powers ChatGPT's advanced voice mode. "The move paves the way for a wave of AI apps that offer conversational voice interfaces," reports Axios. From the report: Early testers of the feature include nutrition and fitness app Healthify and Speak, a language learning app. Other new features being made available to developers include the ability to fine tune models based on pictures. In a demo for reporters, OpenAI executives showed an example of the new audio capabilities combined with Twilio's API to allow an AI assistant to call a fictional candy shop and place an order for 400 chocolate covered strawberries.

Developers will only be able to use the voices provided by OpenAI -- the same ones that are options within ChatGPT. While the voice won't be watermarked in any way and developers won't have to make the AI system identify itself, OpenAI says it's against the company's terms of service to use its systems to spam or mislead people.

The Firefox extension for the uBlock Origin Lite content blocker is no longer available. According to Neowin, "Raymond Hill, the maker of the extension, pulled support and moved uBlock Origin Lite to self-hosting after multiple encounters with a 'nonsensical and hostile' review process from the store review team." From the report: It all started in early September when Mozilla flagged every version of the uBlock Origin Lite extension as violating its policies. Reviewers then claimed the extension apparently collected user data and contained "minified, concatenated or otherwise machine-generated code." The developer seemingly debunked those allegations, saying that "it takes only a few seconds for anyone who has even basic understanding of JavaScript to see the raised issues make no sense." Raymond Hill decided to drop the extension from the store and move it to a self-hosted version. This means that those who want to continue using uBlock Origin Lite on Firefox should download the latest version from GitHub (it can auto-update itself).

The last message from the developer in a now-closed GitHub issue shows an email from Mozilla admitting its fault and apologizing for the mistake. However, Raymond still pulled the extension from the Mozilla Add-ons Store, which means you can no longer find it on addons.mozilla.org. It is worth noting that the original uBlock Origin for Firefox is still available and supported.

Uplevel provides insights from coding and collaboration data, according to a recent report from CIO magazine — and recently they measured "the time to merge code into a repository [and] the number of pull requests merged" for about 800 developers over a three-month period (comparing the statistics to the previous three months).

Their study "found no significant improvements for developers" using Microsoft's AI-powered coding assistant tool Copilot, according to the article (shared by Slashdot reader snydeq): Use of GitHub Copilot also introduced 41% more bugs, according to the study...

In addition to measuring productivity, the Uplevel study looked at factors in developer burnout, and it found that GitHub Copilot hasn't helped there, either. The amount of working time spent outside of standard hours decreased for both the control group and the test group using the coding tool, but it decreased more when the developers weren't using Copilot.
An Uplevel product manager/data analyst acknowledged to the magazine that there may be other ways to measure developer productivity — but they still consider their metrics solid. "We heard that people are ending up being more reviewers for this code than in the past... You just have to keep a close eye on what is being generated; does it do the thing that you're expecting it to do?"

The article also quotes the CEO of software development firm Gehtsoft, who says they didn't see major productivity gains from LLM-based coding assistants — but did see them introducing errors into code. With different prompts generating different code sections, "It becomes increasingly more challenging to understand and debug the AI-generated code, and troubleshooting becomes so resource-intensive that it is easier to rewrite the code from scratch than fix it."

On the other hand, cloud services provider Innovative Solutions saw significant productivity gains from coding assistants like Claude Dev and GitHub Copilot. And Slashdot reader destined2fail1990 says that while large/complex code bases may not see big gains, "I have seen a notable increase in productivity from using Cursor, the AI powered IDE." Yes, you have to review all the code that it generates, why wouldn't you? But often times it just works. It removes the tedious tasks like querying databases, writing model code, writing forms and processing forms, and a lot more. Some forms can have hundreds of fields and processing those fields along with doing checks for valid input is time consuming, but can be automated effectively using AI.
This prompted an interesting discussion on the original story submission. Slashdot reader bleedingobvious responded: Cursor/Claude are great BUT the code produced is almost never great quality. Even given these tools, the junior/intern teams still cannot outpace the senior devs. Great for learning, maybe, but the productivity angle not quite there.... yet.

It's damned close, though. GIve it 3-6 months.
And Slashdot reader abEeyore posted: I suspect that the results are quite a bit more nuanced than that. I expect that it is, even outside of the mentioned code review, a shift in where and how the time is spent, and not necessarily in how much time is spent.
Agree? Disagree? Share your own experiences in the comments.

And are developers really saving time with AI coding assistants?