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I just made a (IMHO) cool test with OpenAI/Linux/TCL-TK:

"write a TCL/tk script file that is a "frontend" to the ls command: It should provide checkboxes and dropdowns for the different options available in bash ls and a button "RUN" to run the configured ls command. The output of the ls command should be displayed in a Text box inside the interface. The script must be runnable using tclsh"

It didn't get it right the first time (for some reason wants to put a `mainloop` instruction) but after several corrections I got an ugly but pretty functional UI.

Imagine a Linux Distro that uses some kind of LLM generated interfaces to make its power more accessible. Maybe even "self healing".

LLMs don't stop amazing me personally.




The issue (and I think what's behind the thinking of AI skeptics) is previous experience with the sharp edge of the Pareto principle.

Current LLMs being 80% to being 100% useful doesn't mean there's only 20% effort left.

It means we got the lowest-hanging 80% of utility.

Bridging that last 20% is going to take a ton of work. Indeed, maybe 4x the effort that getting this far required.

And people also overestimate the utility of a solution that's randomly wrong. It's exceedingly difficult to build reliable systems when you're stacking a 5% wrong solution on another 5% wrong solution on another 5% wrong solution...


Thank You! You have explained the exact issue I (and probably many others) are seeing trying to adopt AI for work. It is because of this I don't worry about AI taking our jobs for now. You still need somewhat foundational knowledge in whatever you are trying to do in order to get that remaining 20%. Sometimes this means pushing back against the AI's solution, other times it means reframing the question, and other times its just giving up and doing the work yourself. I keep seeing all these impressive toy demos and my experience (Angular and Flask dev) seem to indicate that it is not going to replace any subject matter expert anytime soon. (And I am referring to all the three major AI players as I regularly and religiously test all their releases).

>And people also overestimate the utility of a solution that's randomly wrong. It's exceedingly difficult to build reliable systems when you're stacking a 5% wrong solution on another 5% wrong solution on another 5% wrong solution...

I call this the merry go round of hell mixed with a cruel hall of mirrors. LLM spits out a solution with some errors, you tell it to fix the errors, it produces other errors or totally forgets important context from one prompt ago. You then fix those issues, it then introduces other issues or messes up the original fix. Rinse and repeat. God help you if you don't actually know what you are doing, you'll be trapped in that hall of mirrors for all of eternity slowly losing your sanity.


and here we are arguing for internet points.


Much more meaningful to this existentialist.


It can work with things of very limited scope, like that you describe.

I wrote some data visualizations with Claude and aider.

For anything that someone would actually pay for (expecting the robustness of paid-for software) I don’t think we’re there.

The devil is in the details, after all. And detail is what you lose when running reality through a statistical model.


Why make tool when you can just ask AI to give you filelist or files that you need?




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