fidzu : debian

Thirty years ago I became a Debian developer. Twelve years ago I left the project. I left because it seemed that the Debian ship had become too slow to turn, too barnacled with a series of individually OK decisions that each added a little bit of friction and a little less flexability. That made Debian strongly what it is, but prevented it from fruitfully exploring the vast possibility space of what it could be.

Debian will probably resolve today to allow LLM use in Debian development. I'm writing before the vote results are in, but will only post this afterwards. (Update: as expected) It's not my place any longer to try to steer the ship. But I'm still a passenger and I still have opinions, and I still pass by well-worn parts of the rigging that I put up decades ago, and remember what I was trying to accomplish back then.

When I think about LLMs in Debian development, I mostly think about debhelper and what it accomplished. The debian/rules files back when I joined the project were long and complex, full of weird boilerplate, and often you'd copy one and modify it to try to get something that could build a package without too much work. Debhelper first regularized the boilerplate, so packages had rules files that were a succession of dh_ commands, and then it scapped almost all of the boilerplate, reducing the files to the minimum possible. What was left was 3 lines of unncessary boilerplate, there only to satisfy a legalistic reading of a policy document. Changing that to eliminate the boilerplate was already impossible, even though the actual benefit would have been large over the many thousands of packages in the distribution.

What LLMs in Debian development will do, I fear, is eliminate any incentive to scrap boilerplate or reform policies that require a lot of other senseless human effort. If I had had access to LLMs 30 years ago, I might have just had them generate the rules files, replate with complexity. So they will make Debian even more firmly what it is, and ever less likely to explore what it could become.

Unfortunately, one of the things that Debian is, is almost unable to manage packaging modern dependency trees. While more recent distributions like Guix can recursively import dependencies from a dozen programming languages' package repositories, with a result that is generally acceptable to add to the distribution, Debian's policies don't make that very possible for a progam to accomplish. Perhaps some will use LLMs to do that. If they succeeed, Debian will become dependent on proprietary software for development, while still needing people in the loop, doing even less appealing scut-work.

I could speak of other harms, but that alone is enough that I'm sure that, if I had not left the project twelve years ago, I would be leaving it soon. As a passenger, I imagine I'll spend time aboard still from time to time, but it's certainly time to hop off in different places and look around and relish the different ways.

I lost a parent yesterday, and I'm trying hard not to think of the results today as having lost a child, though I spent 18 years helping Debian grow up. That would be too unbearably painful. I respect that Debian is navigating a choice that may have no right answer. Whichever particular compromise is arrived at today, it will still be up to individuals to make choices about what they do and accept. Debian has always been more than the sum of its policies, not just a ship, but a crew. I will always love you.

29 Aug 2026 12:27am GMT

An updated version of the corels package is now on CRAN! The 'Certifiably Optimal RulE ListS (Corels)' learner provides interpretable decision rules with an optimality guarantee-a nice feature which sets it apart in machine learning. You can learn more about corels at its UBC site.

This released fixes an issue discovered on one of the test machines used by Brian Ripley. If and when C compiler flags are set locally that are in fact upsetting the C++ compiler, then the build fails. While not an issue for years and not reproducible on (vanilla) Debian, Ubuntu or Fedora machines it does indeed balk at his end as e.g. the flag -Werror=implicit-function-declaration he sets for C is incompatible with the current C++ compiler. The fault was our: CFLAGS was passed on to PKG_CXXFLAGS letting C options seep into C++ deployment. This has been corrected: we only deal in C++ flags now.

Courtesy of my CRANberries, there is also a diffstat report for this release.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. If you like this or other open-source work I do, you can sponsor me at GitHub.

28 Aug 2026 8:48pm GMT

It is widely accepted that there is an AI bubble in the financial markets at the moment. The moderate opinion is however that LLMs are constantly improving and will eventually take over more and more tasks from humans and increase productivity. But are LLMs actually getting smarter, or just better at fooling us?

There is a growing faction of technical experts that argue that LLMs are actually so bad for real progress, that they are banning their use and requiring human-only work to ensure quality and efficient use of humans' time. A recent review of AI policies of 120 open source projects by Rakshit Yadav shows that 37 chose to have a total AI ban. In the Linux kernel AI-assisted contributions are allowed, but the LLM used needs to be attributed for transparency, while projects like GCC, QEMU, SDL, Gentoo, Zig and Ghostty have adopted policies to reject all AI-assisted contributions. There are also development platforms such as Codeberg and Sourcehut and app stores like Flathub that have banned AI use to generate software, documentation, bug reports, review comments and basically anything that is intended for humans to read. The projects that allow AI use typically still require that there must be a human-in-the-loop and the submitter must have read and filtered everything the LLM spits out before another human is exposed to it, in an effort to contain the spread of AI slop.

Right now, the Linux distribution Debian is having a vote among its developers on whether AI should be allowed or banned for use to contribute to Debian. One of the proposals on the ballot is a total ban of AI for code, documentation, translations, bug reports and more. The initial reaction from most people is astonishment - why don't these techies want to use the latest and greatest technology mankind has produced so far? Is it that they don't want Debian to improve faster with the help of AI? Or is it actually so that LLMs are a scam and incapable of being truly useful for Debian? These people are distinguished experts in their own field, and certainly not stupid, so it is worth pausing to understand why they are proposing AI banning policies.

Also, keep in mind that the AI datacenters themselves run on Debian or other Linux-based systems. All the open source software in the world has been fed to LLMs and software development is one of the main use cases for AI currently. So why is it that the maintainers of many open source projects don't want to receive LLM-assisted contributions, despite the LLMs basically all running on top of those same software stacks and having been trained on how to do software development using the very same open source software codebases?

Why LLMs are so deceptive

The output of an LLM often looks very compelling, professional and correct. Humans have evolved to trust or distrust new information based on easy to detect secondary factors like what authority the speaker holds, or how confidently and eloquently the message is conveyed. Humans are however very bad at fact-checking and cross-referencing new information, as it requires a lot of effort, and humans like saving energy and being as lazy as possible.

Information asymmetry

The less you know about something, the easier it is to fool you on that topic. Nobel prizes in economics have been given in for research on how information asymmetry distorts markets and leads to suboptimal outcomes. In the field of software engineering we have now witnessed a flood of aspiring software developers using AI to create software that looks like it might work, but that is actually full of flaws. These people are well-intended, but they simply lack the expertise to understand what they are actually doing, and don't possess the necessary judgement to decide when an LLM spits out something truly useful and when it is creating mostly garbage. This asymmetry in expertise I think explains the majority of the conflict currently witnessed in open source projects - the senior developers are flooded with requests to review code that is bad and a waste of time for everyone involved, while availability of AI grows the pool of people who could contribute and create more "code slop" at an ever-increasing speed.

The information asymmetry could to some degree be evened out if seniors teach juniors to do software engineering well, but it is of course not feasible to quickly mass educate everyone. Also, it seems that many don't want to learn but instead expect to have all understanding outsourced to LLMs. Many seniors have noticed this and have stopped teaching juniors as the seniors don't like the feeling of having their time wasted by teaching people who don't want to learn. Juniors probably all understand that it would be better to learn to design and write software yourself, but using LLMs just feels too easy. I can fully relate to why people choose to take the path of least resistance. Unfortunately, that path often leads to a dead end.

Humans fall too easily for anthropomorphism

The human brain is wired to think that inanimate objects are alive and have feelings. Small children talk to their stuffed animals as if they were real, and lots of adults experience feelings of things happening in their surroundings due to some acts of gods or elves being angry or whatever. When we see a machine writing just like a human, or even more convincingly hear it talk and respond to our talk like a living thing, our brain automatically starts assuming it is a living thing with intelligence and feelings.

The fact that these creatures live in the abstract "cloud" and only appear through a portal we hold in our palm and behave in a way that was designed for maximum engagement makes the illusion even stronger. I recommend people try out running LLMs locally on their laptop to see the "raw" thing spitting out tokens and have some of the illusion shattered.

Also stop saying "please" to an LLM. It does not have any feelings.

Understanding "temperature"

In my experience understanding the concept of temperature in LLMs helps see why an LLM might confidently generate a plausible-looking but totally wrong code change. The large language models are statistical machines that, based on the input (previous tokens) to the neural network, try to predict what to output (next token). When running an LLM, if the temperature is configured to be zero, the output is very predictable and always follows the paths of the strongest connections (a.k.a. weights) between nodes and layers of the neural network. Unlike in living creatures where the brain learns and changes all the time, the weights of an LLM can only change during training. When an LLM is in "normal" use (during inference, generating next tokens) the weights are fixed, and if temperature is zero, the answer to a specific question will always be exactly the same. This is of course a bit boring and too machine-like, so typically LLMs have a bit of temperature set, which introduces random variation in what connections the neural network traverses.

Again, I recommend people try running small LLMs locally where temperature and other settings are fully exposed and configurable to see this themselves. It is a good antidote to falling for the illusion that LLMs would actually be intelligent.

Why benchmarks don't tell the whole story

If LLMs continue to produce so much garbage, why are benchmarks showing that they are constantly improving? AI models are indeed improving all the time. For example the CAIS AI dashboard visualizes how frontier models have evolved in the past few years. However, the best models still have a pass rate of only about 50% on the Humanity's Last Exam. On SWE-bench the best model today resolves just under 77%. That means there is a significant number of times when the AI is wrong. This matches my personal experiences, and the renowned Greg Kroah-Hartman recently wrote on the Linux developers mailing list that "even with the best of the current and next generation tools, at least 1/3 of the results they generate are flat out wrong or harmful".

When generating cat videos the error rate does not matter, but in engineering, things absolutely must be correct. Sure, humans also make mistakes, but well educated and properly incentivized humans are so much more capable than LLMs in many regards. We can achieve complex things that work reliably, such as operating worldwide commercial air traffic without planes falling down every day.

There are currently a lot of humans who are incentivized to maintain the narrative that general artificial intelligence is coming soon and will take over everything. In fact, the whole financial system is currently skewed towards such a vision because the promise of falling labour costs and increased profits and monopolistic control of everything attracts capital like nothing before.

In this environment we need to remember that machines and economic systems are ultimately servants of humans, and not the other way around.

It's just a tool

LLMs are not a scam, but a useful tool and technology that has its uses. But the idea that AI has or will surpass humans any time soon in either capabilities or efficiency is simply not true, and we should listen to the people who created humanity's so far most complex systems (computers and software), who are saying that LLMs are in many cases so bad, that it might be better to ban them in certain places completely for the time being than to waste far more valuable human time on reading the text and code they generate.

The time asymmetry is not a new phenomenon as there has been various "script kiddies" for a long time. As an example, a person running a memory leak scanner without understanding the results and spending 10 minutes to file a bug report could force an open soruce maintainer to spend an hour on proving and explaining that the finding is false. What is new is how much the AI users blindly trust the outputs they get, and open source is uniquely vulnerable as there are no managers protecting developers use of time.

What I do, recommend, and expect to see in the next stages

I am using AI tools daily, and constantly experimenting with new models and new ways to use them. Sometimes they work, and often they don't. Sometimes looping AI on itself can make it fix its own errors, but sometimes it just gets derailed and will never arrive at the correct solution. When an LLM fails to make a calendar entry for the right time based on reading my email it is easy for me to spot that it is wrong. I try to avoid using LLMs for anything where I can't exercise judgement myself on whether the result was correct or not.

I also really hope that other people would not send me anything where their own effort was less than the effort I have to make reading and understanding it. This principle is not new - many have heard the requirement that reading code must require less effort than what it took to write it.

I have always kept a high bar on software code and asked fellow developers to make sure their code is well structured, easy to follow and documented. LLMs unfortunately make it easier for people to cheat in this regard, but if cheating is easier, maybe the punishment and deterrence needs to be higher now too. Now with many open source projects adopting policies that put guardrails on AI use, I expect we will soon start witnessing cases where the policies are enforced and it will be interesting to see how violations are judged.

As a society we might also need to develop new social standards and rules in what is acceptable treatment of other humans in human-to-machine interactions, and perhaps also new standards in showing what humans are responsible for what machine as the machines start acting more and more independently. I encourage people to take part in these discussions, and in case of doubt, err on the side that favors real human interactions. Contrary to what many business people seem to think, and even though I am in general a techno-optimist myself, I don't feel there is any need to rush with AI adoption.

28 Aug 2026 12:00am GMT