[jboggan]

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.

[kodesko]

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.

[jboggan]

You're probably on to something with the value of disagreement. I think it's one reason why chatting with current models doesn't create the same stimulation as rubber-ducking used to bring. The models are typically too quick to agree and amplify what you think rather than truly break it down and push back.

And thanks for saying it should have worked, I agree. My chagrin has increased over the years as I have realized the magnitude of my ill-timing.

[baddash]

perhaps it is time to resurrect Duckly queue Frankenstein music and thunder in background