Being able to ask questions about non-trivial models as you build them, really diving into the details of exactly how certain performance improvements work and what trade offs there are, and even just getting feed back on your approach is a huge improvement in my ability to really land a solid understanding of the problem and my solution before writing a line of code
crystal_revenge on Hacker News
In the thread “AI is ushering in a “tiny team” era”, replying to this comment
Helped: Asking questions about non-trivial models and getting immediate feedback on approach.
Got in the way: Vibe-coding is laughably awful for unique large ML systems.
… I'm often building pretty sophisticated models that also need to scale, and nearly all SO/Google-able resources tend to be stuck at the level of "fit/predict" thinking that so many DS people remain limited to. Being able to ask questions about non-trivial models as you build them, really diving into the details of exactly how certain performance improvements work and what trade offs there are, and even just getting feed back on your approach is a huge improvement in my ability to really land a solid understanding of the problem and my solution before writing a line of code. Additionally, it's incredibly easy to make a simple mistake when modeling a complex problem and getting that immediate feedback is a kind of debugging you can otherwise only get on teams with multiple highly-skill people on them (which at a certain level is a luxury reserved only for people working a large companies). …