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Local Python automation
Job Hunt Automation
A daily local assistant that gathers approved job feeds, applies deterministic fit and deduplication rules, and prepares a Google Sheets queue for manual review.
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Evidence gallery
01 / Controlled local capture
Dashboard
October 7, 2026 dashboard capture from a local run; counts are a point-in-time queue, not hiring outcomes.
Optional explainer
See the workflow at a glance
This animation explains the workflow; it is not a recording of a live run. The dashboard captures are real, and applications remain manual.
Animated overview
This animation explains the workflow; it is not a recording of a live run. The dashboard captures are real, and applications remain manual.
02 / Challenge
Finding relevant openings should not consume the time needed to evaluate them.
Repeatedly checking job boards creates duplicates and context switching. I wanted a local assistant that prepares a consistent review queue without deciding where to apply or contacting employers on my behalf.
03 / Approach
A scheduled Python run applies explicit rules before writing to Sheets.
A daily Windows run collects listings from the approved Himalayas and Jobicy feeds, normalizes them, applies deterministic fit rules and deduplication, and updates a Google Sheets workbook. The dashboard, technical queue, and pipeline make the next human action visible.
- No automatic application, message, or outreach is sent.
- Optional Gemini and Ollama paths are disabled, not active production AI.
- The workbook supports manual review, status tracking, and follow-up.
04 / Result
The local workflow makes the search reviewable while keeping the final decision manual.
The October 7 screenshots show a working local dashboard and queue. They are not evidence of interviews or offers. The architecture diagram explains how data reaches the workbook and where automation stops.
What I would improve next
- Continue reviewing feed quality and rule drift as sources change.
- Keep manual decisions explicit if new integrations are added.