I’ll take it. You’re probably thinking of embeddings or some other non-LLM AI approach. I guess my point is that the current economic availability of LLMs and lack of experience and time to explore cheaper alternatives is why this project landed how it did currently.

Right after that quoted sentence, it says “this project uses as little LLM as possible.” I stand by that, as I cut as much LLM as I was able.

Fair. Btw you can get some functionality out of this without LLMs:

Job discovery with automated filtering doesn’t use LLMs. Then, you can pass the screening without an LLM step (just pass “H” instead of “L” on the prompt to make a human screening). Then you reach tailoring of a CV to a job posting, which is the first hard need of LLM, but you could stop by that point, or even fork to skip to the tracking step, which again has no LLM.

The general issue is that without LLMs, this project is a somewhat basic pipeline/scraping/knowledge-base approach to job hunting, and you can likely do it yourself with a bit of python scripts and vim. My idea was to use LLMs to explore how far this space can be automated more holistically, in a reasonable way.

Honestly I was a bit shocked with how vibecoded other projects on this space are, so I wanted to see how a non-vibecoded use of LLMs for job search would work.