[bpavuk]

YESSSS!!! speed is THE way! I like my boilerplate POJOs/data classes generated at breakneck pace of 300+ tok/s, Flash-Lite is more useful than GPT-5.5 for me this way. if it's too slow, you just stay in that goddamn async death loop

[embedding-shape]

> I like my boilerplate POJOs/data classes generated at breakneck pace of 300+ tok/s

Regardless of speed, use the LLM to eliminate the need for boilerplate rather than just creating more code faster.

> if it's too slow, you just stay in that goddamn async death loop

Things get slow when you're ballooning the size of your code, files, design and architecture, and things get more involved and complicated, piling fast hacks on top of fast hacks and everything get brittle.

Slow is fast, longer-term anyways.

[bpavuk]

previously, mugging through docs to turn them into serializables for some API took weeks of grueling work if you wanted to cover an entire API surface that's as big as, say, GitHub's. nowadays, just "copy Markdown" from the very same GitHub, put 10-12 data classes, and let LLM extrapolate from there. with Gemini's 65.5k max token output, that is just several prompts and about two hours. that's the boilerplate. there is practically no way to automate this unless GitHub adopts OpenAPI spec in a way that's not buggy, so that we can just hit an endpoint and point procedural source generators at them

[embedding-shape]

> that's the boilerplate

Sounds like you're trying to just re-implement a HTTP API, not really boilerplate.

Boilerplate is code you could have avoided written, but you take the "temporary" shortcut of copy-pasting the code instead of building a proper abstraction. This is what I'm talking about is the wrong direction.

[elxr]

For boilerplate, yeah. But when asking research or exploratory questions, or weighing whether a feature is well designed, or asking "can I implement _x_ feature using these libraries without introducing unnecessary complexity", then GPT-5.5 medium is still fast enough.

10-20 seconds times a couple turns on a new feature isn't bad. Kimi is also similarly fast if not faster.

I do agree with smaller models for more constrained/routine tasks though.

[bpavuk]

well, I can usually think for myself or hit someone up in Discord (or Teams, if it's for a living) and in a worst case (that person just deflects to AI anyway) just save some token budget for myself

[elxr]

I always think for myself too, but when learning to do something I've never implemented before, it's nice to have little sanity checks using something with the reasoning ability (plus the fast natural language search on hundreds of pages of documentation) of a model like GPT-5.5.

Every line I put in my app, I still reason about myself. But when deciding between 5+ ways of building some random, non-straightforward feature, it's nice to have what's essentially a "mentor" AI.