In 2017 LLMs weren't powerful enough to generate working code on their own, but my goal was to at least create a chatbot that could help you rubber-duck-debug your way to a solution. Unfortunately the tech wasn't quite strong enough for that, and not enough engineers even knew what rubber-duck-debugging was. RIP Duckly.
Trying to train an LLM on two 1080ti's on the StackOverflow corpus in my living room was a vibe though. Good times.
Duckly deserved to actually work. There’s a small irony here: the closest study I found to this, robots specifically built to simulate attentive listening, found they performed no better than an actual inanimate rubber duck for adult engineers. The mechanical signal of listening doesn’t seem to be the active ingredient. Makes me wonder if Duckly would have needed real disagreement to close a gap a duck can’t, not just better natural language.
OMG - strong vibes to Einstein crediting Michele Besso, his colleague at the Swiss Patent Office, with helping him discussing some concepts in the special relativity paper: see at the end of the paper https://www.fourmilab.ch/etexts/einstein/specrel/specrel.pdf
I started my web dev career in 1999 so my main code references were a combination of O’reilly and “for dummies” books. As a wet behind the ears engineer I’d find myself regularly walking over to my more senior friend Dan’s cubicle for help.
Half the time on the walk over, trying to frame the question in my mind I’d figure out the answer or at least next step. It got to the point where Dan would see me heading towards him and suddenly turn around and he’d as “Figure it out?” And I’d throw him a thumbs up on the way back to my desk.
The move from thinking to semantic conversion is important for investigation/introspection.
Arguing with yourself also seems to engage your brains "theory of mind" centers, so different pathways get activated to examine the problem space.
The problem with Ai is the fact that it hallucinates and if you're doing anything truly novel in an integration or framing sense it bottoms out very quickly and can't engage. A human operator can decompose the problem and get accuracy checks for known areas in the training data of course.
Now to be I'm not saying Ai can't produce novel work on the edge but in my experience it is antagonistic towards those goals.
Case in point, CRDTs, many don't use tombstones but they are the minority, and if you try iterate a new CRDT off of one that doesn't use tombstones, let's say diamond-types, it will keep pulling you back to tombstones.
The problem is that the number of humans who understand dynamic investigation and the push pull of exploring an idea you don't hold with someone has always been very small, and now with reflexive internet argument culture driving how we view "debate" and "discussion".
I don't know if we've reduced the leisure to think or what but things are not great for finding speculative thinking partners.
Trying to train an LLM on two 1080ti's on the StackOverflow corpus in my living room was a vibe though. Good times.