Porting COBOL Code And The Trouble With Ditching Domain Specific Languages

Whenever the topic is raised in popular media about porting a codebase written in an ‘antiquated’ programming language like Fortran or COBOL, very few people tend to object to this notion. After all, what could be better than ditching decades of crusty old code in a language that only your grandparents can remember as being relevant? Surely a clean and fresh rewrite in a modern language like Java, Rust, Python, Zig, or NodeJS will fix all ailments and make future maintenance a snap?

For anyone who has ever had to actually port large codebases or dealt with ‘legacy’ systems, their reflexive response to such announcements most likely ranges from a shaking of one’s head to mad cackling as traumatic memories come flooding back. The old idiom of “if it ain’t broke, don’t fix it”, purportedly coined in 1977 by Bert Lance, is a feeling that has been shared by countless individuals over millennia. Even worse, how can you ‘fix’ something if you do not even fully understand the problem?

In the case of languages like COBOL this is doubly true, as it is a domain specific language (DSL). This is a very different category from general purpose system programming languages like the aforementioned ‘replacements’. The suggestion of porting the DSL codebase is thus to effectively reimplement all of COBOL’s functionality, which should seem like a very poorly thought out idea to any rational mind.

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Illustration of author surveying the fruits of his labor by Bomberanian

Learning Linux Kernel Modules Using COM Binary Support

Have you ever felt the urge to make your own private binary format for use in Linux? Perhaps you have looked at creating the smallest possible binary when compiling a project, and felt disgusted with how bloated the ELF format is? If you are like [Brian Raiter], then this has led you down many rabbit holes, with the conclusion being that flat binary formats are the way to go if you want sleek, streamlined binaries. These are formats like COM, which many know from MS-DOS, but which was already around in the CP/M days. Here ‘flat’ means that the entire binary is loaded into RAM without any fuss or foreplay.

Although Linux does not (yet) support this binary format, the good news is that you can learn how to write kernel modules by implementing COM support for the Linux kernel. In the article [Brian] takes us down this COM rabbit hole, which involves setting up a kernel module development environment and exploring how to implement a binary file format. This leads us past familiar paths for those who have looked at e.g. how the Linux kernel handles the shebang (#!) and ‘misc’ formats.

On Windows, the kernel identifies the COM file by its extension, after which it gives it 640 kB & an interrupt table to play with. The kernel module does pretty much the same, which still involves a lot of code.

Of course, this particular rabbit hole wasn’t deep enough yet, so the COM format was extended into the .♚ (Unicode U+265A) format, because this is 2025 and we have to use all those Unicode glyphs for something. This format extension allows for amazing things like automatically exiting after finishing execution (like crashing).

At the end of all these efforts we have not only learned how to write kernel modules and add new binary file formats to Linux, we have also learned to embrace the freedom of accepting the richness of the Unicode glyph space, rather than remain confined by ASCII. All of which is perfectly fine.

Top image: Illustration of [Brian Raiter] surveying the fruits of his labor by [Bomberanian]

The Incomplete JSON Pretty Printer (Brought To You By Vibes)

Incomplete JSON (such as from a log that terminates unexpectedly) doesn’t parse cleanly, which means anything that usually prints JSON nicely, won’t. Frustration with this is what led [Simon Willison] to make The Incomplete JSON pretty printer, a single-purpose web tool that pretty-prints JSON regardless of whether it’s complete or not.

Making a tool to solve a particular issue is a fantastic application of software, but in this case it also is a good lead-in to some thoughts [Simon] has to share about vibe coding. The incomplete JSON printer is a perfect example of vibe coding, being the product of [Simon] directing an LLM to iteratively create a tool and not looking at the actual code once.

Sometimes, however the machine decides to code something is fine.

[Simon] shares that the term “vibe coding” was first used in a social media post by [Andrej Karpathy], who we’ve seen shared a “hello world” of GPT-based LLMs as well as how to train one in pure C, both of which are the product of a deep understanding of the subject (and fantastically educational) so he certainly knows how things work.

Anyway, [Andrej] had a very specific idea he was describing with vibe coding: that of engaging with the tool in almost a state of flow for something like a weekend project, just focused on iterating one’s way to what they want without fussing the details. Why? Because doing so is new, engaging, and fun.

Since then, vibe coding as a term seems to get used to refer to any and all AI-assisted coding, a subject on which folks have quite a few thoughts (many of which were eagerly shared on a recent Ask Hackaday on the subject).

Of course human oversight is critical to a solid and reliable development workflow. But not all software is the same. In the case of the Incomplete JSON Pretty Printer, [Simon] really doesn’t care what the code actually looks like. He got it made in a short amount of time, the tool does exactly what he wants, and it’s hard to imagine the stakes being any lower. To [Simon], however the LMM decided to do things is fine, and there’s a place for that.

Using Integer Addition To Approximate Float Multiplication

Once the domain of esoteric scientific and business computing, floating point calculations are now practically everywhere. From video games to large language models and kin, it would seem that a processor without floating point capabilities is pretty much a brick at this point. Yet the truth is that integer-based approximations can be good enough to hit the required accuracy. For example, approximating floating point multiplication with integer addition, as [Malte Skarupke] recently had a poke at based on an integer addition-only LLM approach suggested by [Hongyin Luo] and [Wei Sun].

As for the way this works, it does pretty much what it says on the tin: adding the two floating point inputs as integer values, followed by adjusting the exponent. This adjustment factor is what gets you close to the answer, but as the article and comments to it illustrate, there are plenty of issues and edge cases you have to concern yourself with. These include under- and overflow, but also specific floating point inputs.

Unlike in scientific calculations where even minor inaccuracies tend to propagate and cause much larger errors down the line, graphics and LLMs do not care that much about float point precision, so the ~7.5% accuracy of the integer approach is good enough. The question is whether it’s truly more efficient as the paper suggests, rather than a fallback as seen with e.g. integer-only audio decoders for platforms without an FPU.

Since one of the nice things about FP-focused vector processors like GPUs and derivatives (tensor, ‘neural’, etc.) is that they can churn through a lot of data quite efficiently, the benefits of shifting this to the ALU of a CPU and expecting (energy) improvements seem quite optimistic.

Ask Hackaday: Vibe Coding

Vibe coding is the buzzword of the moment. What is it? The practice of writing software by describing the problem to an AI large language model and using the code it generates. It’s not quite as simple as just letting the AI do your work for you because the developer is supposed to spend time honing and testing the result, and its proponents claim it gives a much more interactive and less tedious coding experience. Here at Hackaday, we are pleased to see the rest of the world catch up, because back in 2023, we were the first mainstream hardware hacking news website to embrace it, to deal with a breakfast-related emergency.

Jokes aside, though, the fad for vibe coding is something which should be taken seriously, because it’s seemingly being used in enough places that vibe coded software will inevitably affect our lives.  So here’s the Ask Hackaday: is this a clever and useful tool for making better software more quickly, or a dangerous tool for creating software nobody quite understands, containing bugs which could cause a disaster?

Our approach to writing software has always been one of incrementally building something from the ground up, which satisfies the need. Readers will know that feeling of being in touch with how a project works at all levels, with a nose for immediately diagnosing any problems that might occur. If an AI writes the code for us, the feeling is that we might lose that connection, and inevitably this will lead to less experienced coders quickly getting out of their depth. Is this pessimism, or the grizzled voice of experience? We’d love to know your views in the comments. Are our new AI overlords the new senior developers? Or are they the worst summer interns ever?

FreeDOS 1.4 Released

Even in 2025 there are still many applications for a simple Disk Operating System (DOS), whether this includes running legacy software (including MS-DOS games & Windows 3.x), or (embedded) systems running new software where the overhead of a full-fat Linux or BSD installation would be patently ridiculous.

This is where the FreeDOS project provides a modern, fully supported DOS, with the recent 1.4 release adding a whole range of features and updates to existing components like the FreeCOM command shell. This is the first stable release since 1.3 was released in 2022.

FreeDOS saw its first release in 1994 and has become the de facto replacement for MS-DOS — featuring many improvements to make it work well on modern hardware and a package manager to manage installed software much like on Linux & BSD. The new kernel didn’t quite make it into this release, but it and some other items will be available in the monthly test builds.

You can download the new 1.4 release here, with live & installer CD images, a USB installer and even a Floppy Edition available. System requirements include an (Intel) x86 CPU, a BIOS (or legacy UEFI mode), 640 kB of RAM and 20 MB of storage.

Golang On The PS2

A great many PlayStation 2 games were coded in C++, and there are homebrew SDKs that let you work in C. However, precious little software for the platform was ever created in Golang. [Ricardo] decided this wouldn’t do, and set about making the language work with Sony’s best-selling console of all time. 

Why program a PS2 in Go? Well, it can be easier to work with than some other languages, but also, there’s just value in experimenting in this regard. These days, Go is mostly just used on traditional computery platforms, but [Ricardo] is taking it into new lands with this project.

One of the challenges in getting Go to run on the PS2 is that the language was really built to live under a full operating system, which the PS2 doesn’t really have. However, [Ricardo] got around this by using TinyGo, which is designed for compiling Go on simpler embedded platforms. It basically takes Go code, turns it into an intermediate representation, then compiles binary code suitable for the PS2’s Emotion Engine (which is a MIPS-based CPU).

The specifics of getting it all to work are quite interesting if you fancy challenges like these. [Ricardo] was even able to get to an effective Hello World point and beyond. There’s still lots to do, and no real graphical fun yet, but the project has already passed several key milestones. It recalls us of when we saw Java running on the N64. Meanwhile, if you’re working to get LOLCODE running on the 3DO, don’t hesitate to let us know!