This is the golden age of model training. Some days ago, I decided I wanted a local CPU only model that can perform exceptionally well for English to Bash translation (to avoid the googling for command syntax). I got a bunch of subagents to generate large amount of training data (140k+ samples), got the Qwen 3 0.6B base model, pointed Astra at it, and off to the races. It trained for 2 days (on and off) and I got a surprisingly good model for my task! The total active time I spent was a few hours. And it is still improving, what a time to be alive!
I vaguely recall a project from a while back that did something similar without LLMs.
I’m really pushing my recall, but I want to say it was written in Ruby and stored pre-configured commands that it just did traditional search over.
I vaguely recall it working okay because 99.99% of the questions people asked were the same (“tar command to gzip a directory and strip the prefix” is something I google like once a month).
GodelNumbering · · focus · HN ↗
everforward · · focus · HN ↗
I’m really pushing my recall, but I want to say it was written in Ruby and stored pre-configured commands that it just did traditional search over.
I vaguely recall it working okay because 99.99% of the questions people asked were the same (“tar command to gzip a directory and strip the prefix” is something I google like once a month).