| I, too, wrote a rambling take on the potential for LLM-induced stack ossification about 6 months ago: https://paritybits.me/stack-ossification/ At the time, I had given in to Claude 3.5's preference for python when spinning up my first substantive vibe-coded app. I'd never written a line of python before or since, but I just let the waves carry me. Claude and I vibed ourselves into a corner, and given my ignorance, I gave up on fixing things and declared the software done as-is. I'm now the proud owner of a tiny monstrosity that I completely depend on - my own local whisper dictation app with a system tray. I've continued to think about stack ossification since. Still feels possible, given my recent frustration trying to use animejs v4 via an LLMs. There's a substantial api change between animejs v3 and v4, and no amount of direction or documentation placed in context could stop models from writing against the v3 api. I see two ways out of the ossification attractor. The obvious, passive, way out: frontier models cross a chasm with respect to 'putting aside' internalized knowledge (from the training data) in favor of in-context directions or some documentation-RAG solutions. I'm not terribly optimistic here - these models are hip-shooters by nature, and it feels to me that as they get smarter, this reflex feels stronger rather than weaker. Though: Sonnet 4 is generally a better instruction-follower than 3.7, so maybe. The less obvious way out, which I hope someone is working on, is something like massive model-merging based on many cached micro fine-tunes against specific dependency versions, so that each workspace context can call out to modestly customized LLMs (LoRA style) where usage of incorrect versions of your dependencies has specifically been fine-tuned out. |