I use local models + openrouter free ones.

My monthly spend on ai models is < $1

I'm not cheap, just ahead of the curve. With the collapse in inference cost, everything will be this eventually

I'll basically do

    $ man tool | <how do I do this with the tool>

or even

    $ cat source | <find the flags and give me some documentation on how to use this>

Things I used to do intensively I now do lazily.

I've even made a IEITYuan/Yuan-embedding-2.0-en database of my manpages with chroma and then I can just ask my local documentation how I do something conceptually, get the man pages, inject them into local qwen context window using my mansnip llm preprocessor, forward the prompt and then get usable real results.

In practice it's this:

    $ what-man "some obscure question about nfs" 
    ...chug chug chug (about 5 seconds)...

    <answer with citations back to the doc pages>

Essentially I'm not asking the models to think, just do NLP and process text. They can do that really reliably.

It helps combat a frequent tendency for documentation authors to bury the most common and useful flags deep in the documentation and lead with those that were most challenging or interesting to program instead.

I understand the inclination it's just not all that helpful for me