I work in customer support and tech support. I can see it now, people will start using AI assistants to order things and contact companies with problems. That will probably be frustrating, it will be like an office assistant ordering something for their boss but they don’t understand what or why they’re ordering.

One wonders who would have the time, interest and money to setup and control AI to do all this… One wonders 🤔 and the one remembers -just as a random example- Microsoft funding SCO with tens of millions of dollars right after which it attacked Linux with fake copyright claims for years, after which Microsoft extorted large corporations into switching to Microsoft platforms. Also, why controls GitHub now? Anyway, I digress.

OS will deal with this, I imagine it won’t be too hard to setup tools that will deal with this shit, but I’m so sick and tired of continuously having to deal with this shit. Can we just formonce have something nice?

Just keep the bug-reporting bots pre-occupied by having them talk to the CI Bot. Problem solved b

Another step in the slow death of the open WWW… Are we all gonna retreat to smaller, more controlled environments?

My project has merge requests by AI that have conflicts and don’t make sense.

So…ye

One thing I’ve also noticed is people doing code reviews using ai to pad their stats or think they are helping out. At best it’s stating the obvious, wasting resources to point out what doesn’t need pointing out. At worst it’s a giant waste of time based on total bullshit the ai made up.

I kinda understand why people would think LLMs are able to generate and evaluate code. Because they throw simple example problems at them and they solve them without much issue. Sometimes they make obvious mistakes, but these are easily corrected. This makes people think LLMs are basically able to code, if it can solve even some harder example problems, surely they are at least as good as beginner programmers right? No, wrong actually. The reason the LLM can solve the example problem, is because that example (or a variation) was contained within its training data. It knows the answer not by deduction or by reason, it knows the answer by memorization. Once you start actually programming in the real world, it’s nothing like the examples. You need to account for an existing code base, with existing rules, standards and limitations. You need to evaluate which solution out of your toolbox to apply. Need to consider the big picture as well as small details. You need to think of the next guy working with the code, because more often than not, that next guy is you. LLMs crumble in a situation like this, they don’t know about all the unspoken things, they haven’t trained on the code base you are working with.

There’s a book I’m fond of called Patterns of Enterprise Application Architecture by Martin Fowler. I always used to joke it contained the answer to any problem a software engineer ever comes across. The only trick is to choose the correct answer. LLMs are like this, they have all these patterns memorized and choose which answer best fits the question. But it doesn’t understand why, what the upsides and downsides are for your specific situation. What the implications of the selected answer are going forward. Or why this pattern over another. When the LLM answers you can often prompt it to produce an answer with a completely different pattern applied. In my opinion it’s barely more useful than the book and in many ways much worse.