A job-matching agent that splits work between a local model and a cloud API

This person built a Hermes agent that searches active job listings and offers them roles they match. Because much of the work is summarising and keyword searching, they split it: a local Qwen 35B model handles the easy part, and a cloud API, currently GLM 5.3 Flash, does the final analysis.

The split reduced token usage by about 50 percent, with Hermes dividing the work between the two models. They present it as a good pattern for local LLM use: run the easy or routine tasks locally and let a frontier model handle only the hard part.

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