I tuned Hindsight for long conversations

Last night I wondered if an agent could learn what not to recommend in a job-matching pipeline; by morning, ours was blacklisting patterns it had only seen fail once—and getting better every run. job sense ai Last night I wondered if an agent could learn what not to recommend in a job-matching pipeline; by morning, ours was blacklisting patterns it had only seen fail once—and getting better every run. What I built here isn’t another resume–job similarity tool. It’s a loop: ingest resumes and job descriptions, generate matches, evaluate those matches, and then feed the failures back into the system so it stops making the same mistake twice. At a high level, the repo is pretty simple: main.py wires the pipeline together matcher/ handles embedding + similarity scoring agent/ wraps the LLM logic (ranking, reasoning, critique) memory/ is where Hindsight comes in evaluation/ defines what “bad recommendation” actually means The interesting part isn’t matching. It’s what happens after a bad ma