“The downside of not having a clean codebase is worse than ever.”
just watched an interview from @thdxr explaining why OpenCode’s codebase is now cleaner than it’s ever been.
an agent’s performance is a function of its intelligence, your instruction, and its context (the codebase).
that’s why it’s more important than ever to maintain the codebase regularly, establish patterns, and go back to fix some of the old deprecated ways.
Video
Taelin (@VictorTaelin)
I think the reason models feel worse a while after launch is that a model's performance is a function of its raw intelligence, multiplied by the codebase's quality. yet, every time you use AI, your codebase gets a bit worse. sometimes it is invisible, but confusion accumulates, even when behavior remains intact. unused fields, inconsistent variable names, stale comments. the more the AI edits, the more slop-fried your codebase becomes, like a JPEG that is repeatedly compressed. this, in turn, causes more junk to automatically get in the context, bringing you closer to ugly latent space neighborhoods, making it produce worse outputs, til you reach an equilibrium where no more progress is possible. then a new model launches, and its increased slop-resistance counterweights the accumulated junk, and things suddenly start moving forward again. until your repo crosses that model's slop threshold again, and it stops working, again. I think this process is an inherent part of how these things work and GPT 10 or Fable 10 will still feel moronic after 1 month of launch. the only difference is you'd be maintaining a 100k LOC project, but it will still fail to add a new feature in ways that make you facepalm
— https://nitter.net/VictorTaelin/status/2085470478626431203#m