I Built a Job Search Agent That Scores 200 Jobs With Local AI -- Zero Cloud, Zero Cost

A developer built JobRadar, a CLI tool that searches 8 job sources concurrently and scores each listing against a user's profile using a local LLM (qwen3-1.7b) running on the user's machine, with no cloud APIs or data leaving the computer. The tool provides a score from 0 to 100, a rating, and a reasoning paragraph for each job, and includes a FastAPI dashboard with Kanban-style application tracking.

Job searching is tab hell. You open LinkedIn, Indeed, RemoteOK, Glassdoor. You re-read the same listings. You copy-paste the same cover letter. You pay $30/month for tools that just aggregate feeds and call it "AI-powered." I wanted something better. So I built JobRadar. JobRadar is a CLI tool that searches 8 job sources concurrently and scores every listing against your profile using a local LLM running on your machine. python -m jobradar -q "python developer" -p profile.yaml That's it. Eight boards searched in parallel. Every job scored 0-100. Results saved to CSV. Total time: under a minute. Every other job search tool I found uses cloud APIs for scoring -- OpenAI, Claude, whatever. Which means your resume, your search history, your career preferences all go to someone else's server. And you pay per token. JobRadar runs qwen3-1.7b 1.1 GB on your CPU via Ollama. No API keys. No subscriptions. No data leaving your machine. The AI scores each job on four dimensions: Each job gets a score, a rating Excellent/Good/Fair/Poor , and a reasoning paragraph explaining why. Most job search tools scrape 1-2 boards. JobRadar hits 8 simultaneously: The Greenhouse and Ashby sources pull directly from company career page APIs. No scraping, no auth, no fragility. You define a profile YAML: name: Anirudh title: Backend Engineer skills: - Python - FastAPI - PostgreSQL - Docker experience years: 5 salary min: 120000 remote ok: true JobRadar sends each job description plus your profile to the local LLM and gets back structured JSON with scores and reasoning. The model runs on your CPU -- no GPU required. On my machine 15GB RAM, no GPU , it processes about 9 seconds per job. | Feature | JobRadar | Cloud-based tools | |---|---|---| | AI scoring | Local LLM free | OpenAI API $$ | | Data privacy | Stays on your machine | Sent to cloud | | Job sources | 8 concurrent | 1-3 | | Web dashboard | Yes Kanban | Depends | | License | MIT | Varies | | Setup time | bash setup.sh | Account + API key | Beyond the CLI, there's a FastAPI dashboard with a Kanban-style pipeline to track your applications. Filters, search, config editor -- all running locally on port 3000. git clone github.com/ANIRudH-lab-life/job-radar cd job-radar bash setup.sh or setup.ps1 on Windows Pick Ollama recommended python -m jobradar -q "python developer" -p profile.yaml MIT licensed. No vendor lock-in. Your data stays yours. GitHub: github.com/ANIRudH-lab-life/job-radar If you find it useful, a star would mean a lot. If you have ideas for improvement, open an issue -- I read every one.