Nice to see some of us are still around!
I think there’s definitely truth in what you’re saying, but that’s not the whole story. To me, LLMs are tools—"stupid" tools in the sense that they don't actually understand what they're doing, they just predict the next token. They will never replace us humans!
But they do pack an enormous amount of knowledge, which makes them awesome partners. You don’t need to be an absolute genius; you just pair up with an LLM, and boom—you've got a winning combo. Of course, we still need to learn how to tame the machine, understand its limits, and figure out how to team up with it. That’s actually one of my goals while playing around with Xymon.
Xymon’s challenges are complex. Solving these puzzles is way harder than building something small from scratch. You quickly run into the "context" problem: your prompt or the LLM doesn’t have the whole Xymon codebase under its hood, and you can’t just dump all of it in there at once. At some point, the AI loses the big picture. A human can keep track of it (even if we tend to forget things too sometimes!).
Still, this is my playground, so come along and play with me and the Xymon code! Don’t think for a second that past developers never made mistakes—trust me, they made plenty! And they didn't have the full picture either. Some features are redundant, and some code breaks other things. That’s just the reality of development, so come join the fun!
Per our policy, we always say it takes two people to agree to push some code. I’m still fully convinced this rule is super powerful. Unfortunately, I’ve been pushing a ton of PRs lately simply because there’s no one around to review them. Because of that, I produce a lot of code and have gotten pretty good at making it clean, but I'm honestly not as sharp when it comes to reviewing others' work, just because I rarely get to practice it.
Let’s be real: porting existing features isn’t always the most thrilling task, and doing code reviews to test things isn't exactly the most exciting adventure of your life. But I’m sticking to the plan for now! We started implementing Terabithia, and I really want to see this through. All the improvements in that branch are great ideas that solve real-world problems. It’s pure gold.
I’ve already explored IPv6, TLS, APIs, CMake builds, better probes, improved graphing, custom tests, and even hooking things up with LLMs. So yes, there is plenty of cool stuff to do—come join the ride!

Bruno

Le 10.09.2026 à 22:08, Michael T Pins via Xymon a écrit :
Roland Rosenfeld writes:


Maybe I'm too old and pessimistic, but I don't like the new workflow,
where IA generates tons of code and comments, that I have to read and
review.  I understand that this may be the future, but the frequency
that it generates code doesn't match my performance of code review.


      

        
Maybe it's also a problem, that I'm not a C developer.  I can read and
understand C (and write small fixes), but this usually takes much
time.  So it looks that the AI outclasses me, which doesn't feel good.


      

      

      

        
That said, the project needs more C developers that are able to review
the AI generated stuff...


      

      
I did another round of testing chatbot generated code a few months ago.
It was all still, at best, junior level programmer grade code.

Not only was the code non-portable (it wouldn't even compile on FreeBSD),
but it had numerous use-after-free errors, and a variety of other bugs.
All in about 500 lines of code.  Personally, I find I'm still better
off writing the code myself rather than asking a chatbot then having to
audit the code and clean up the mess.

This isn't actually surprising - despite what far too many people
believe, there is no intelligence in an "AI" chatbot.  The code a chatbot
generates will never be any better than the code it was trained on,
and they were all trained primarily on what they scraped from GitHub.
Considering how bad much of the GitHub code is, expecting an LLM trained
on it to produce quality code is almost laughable.





      

        
Except this, I still ask myself what all this AI generated code means
to the LICENSE/Copyright of the project.
COPYING file is GPL-2, most source files are Copyright Henrik Storner
<henrik@storner.dk>, but what about the code generated by AI?  At
least the tests reference GPL-2+, but no copyright or author is
mentioned.  So can this really be GPL-2+ without a copyright owner?


      

      

      

        
I'm not sure about all this...


      

      
Considering how an LLM works, there is a non-trivial chance it will
generate code at times that is line for line identical to what it was
trained on.  Who owns the copyright on that code?

All that said, if someone finds using a chatbot to generate code helpful,
and someone is willing and able to fully audit the resulting code,
go for it.

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