The K6 is not programmable out-of-the-box, but it seems some people have successfully installed QMK firmware on it (with caveats); https://github.com/CanUnesi/QMK-on-K6/

If you just want to try the numpad thing out, it might be easier to use something like AutoHotKey. Here’s an example of a script that uses CapsLock to toggle the numpad layer.

Of course, AHK scripts only work with the computer you’ve installed them to, whereas using programmable firmware will work regardless of which device you connect your keyboard to.

I do have to grind through entering a list of numbers

Have you considered a numpad layer? They’re great. All of the speed and convenience with none of the wasted space or extra arm movements.

I have a numpad for both hands on my Redox :-)

Huh… Do you also move the cursor with your keyboard? And if so, don’t you have an RMB key? If you don’t use a mouse, do you use a GUI that isn’t suited for keyboard navigation?

Okay I’ll bite…: How are PgUp and PgDown yellow, when Menu is missing from the list?

Backed by VC, so you know they’re just waiting for an exit

That’s because ChatGPT and LLM’s are not oracles. They don’t take into account whether the text they generate is factually correct, because that’s not the task they’re trained for. They’re only trained to generate the next statistically most likely word, then the next word, and then the next one…

You can take a parrot to a math class, have it listen to lessons for a few months and then you can “have a conversation” about math with it. The parrot won’t have a deep (or any) understanding of math, but it will gladly replicate phrases it has heard. Many of those phrases could be mathematical facts, but just because the parrot can recite the phrases, doesn’t mean it understands their meaning, or that it could even count 3+3.

LLMs are the same. They’re excellent at reciting known phrases, even combining popular phrases into novel ones, but even then the model lacks any understanding behind the words and sentences it produces.

If you give an LLM a task in which your objective is to receive factually correct information, you might as well be asking a parrot - the answer may well be factually correct, but it just as well might be a hallucination. In both cases the responsibility of fact checking falls 100% on your shoulders.

So even though LLMs aren’t good for information retreival, they’re exceptionally good at text generation. The ideal use-cases for LLMs thus lie in the domain of text generation, not information retreival or facts. If you recognize and understand this, you’re all set to use ChatGPT effectively, because you know what kind of questions it’s good for, and with what kind of questions they’re absolutely useless.