I feel like this kind of tool has been completely obsoleted by LLMs (even local models). I've used similar tools such as tiny for Emacs, which is a DSL for generating text based on numeric ranges. Now, it's simply more efficient to ask AI.
Fun fact: ripgrep started as something to dog-food the regex crate (with a focus on performance). I didn't originally build it to release to end users funnily enough. To be clear, I'm not implying bttf will follow the same path. I honestly probably agree with you at this point. I wouldn't have 1 year ago though.
My big hypothesis is that tokens are going to get much more expensive. Either that or OpenAI/anthropic are going bankrupt. I’m almost excited to find out, I have to admit.
Your remark just reminded me of this, I went a bit off topic, I admit.
I think Gemma 4 is also a good example of a capable small model.
I mention these not only because they're cheap but because they can run on consumer devices. The "every year bigger and more capable SOTA model" trend is mirrored by "the every year smaller and more capable open source model" trend.
I need to find stats on average RAM of personal devices, but I expect it will be so low, we are light years away from running a frontier model (from today) locally on a smartphone, let’s stop dreaming (and I really would love having it).
I do agree local models are progressing and I am to this day in awe at what a 50GB file can do – it still feels like black magic to me.
Also granted, something like Gemma 2 2B seems to have similar performance to ChatGP 3.5 and only require 2GB of RAM. But I think the RAM/performance ratio curve over time is logarithmic and not linear, it’s moving slower and slower.