ANIRudH-lab-life/job-radar

🎯 AI-Powered Job Search Agent — searches multiple job boards and rates them against your profile using a local LLM - ANIRudH-lab-life/job-radar

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README

🎯 JobRadar A CLI job search agent that hunts across 8 free sources at once and helps you figure out which ones are actually worth your time. It uses a local LLM to score jobs against your profile — but the AI is just a guide, not a decision-maker. You're the one who decides what fits. That's the whole point. Why JobRadar? Most job boards show you hundreds of listings and leave you drowning in tabs. JobRadar pulls from multiple sources at once, filters out the noise, and gives you a ranked list with AI-generated notes on why each job might (or might not) be a fit. But here's the thing: we believe the best job search tool puts a human in the loop. The AI can score and summarize, but it can't understand your gut feeling about a company culture, or that you'd rather work somewhere with a smaller team even if the salary is lower. That part is yours. So use the scores as a starting point, not a verdict. What it does

Searches 8 sources at once — Remotive, Arbeitnow, RemoteOK, Jobicy, Himalayas, Greenhouse (direct ATS), Ashby (direct ATS), and optionally LinkedIn Rates jobs with a local LLM — auto-detects Ollama or llama.cpp, scores each job 0-100 on skills match, experience fit, salary fit, and remote fit Remembers what you've seen — persistent cache so you don't re-review the same jobs every run Works completely offline — all AI runs locally on your machine, no cloud APIs, no subscriptions Looks good in the terminal — color-coded scores, progress bars, clean tables Web dashboard — dark-mode SPA at localhost:3000 with Kanban pipeline, filters, and config editor

Quick Install Option 1: One-liner (Linux / macOS) curl -fsSL https://raw.githubusercontent.com/ANIRudH-lab-life/job-radar/main/setup.sh | bash Option 2: npm (all platforms) npx jobradar-setup

or

Step Checks for Downloads if missing

1. Python 3.9+ in PATH — (shows install link)

2. pip packages Each import individually Only missing packages

3. LLM Backend You choose: Ollama or llama.cpp Ollama install + model pull, or llama.cpp binary

4. LLM Model Only if llama.cpp chosen qwen3-1.7b Q4_K_M (~1.1GB) GGUF

5. Config profile.yaml Creates defaults

Safe to re-run — second run is instant, nothing re-downloaded. The installer asks which LLM backend you prefer:

Ollama (recommended) — auto-installs the model, easy to manage, works out of the box llama.cpp (faster) — raw performance, downloads GGUF model manually

Platform notes

Platform Installer llama-server source

Linux (x64) bash setup.sh Ollama (recommended) or GitHub release zip

macOS (ARM) bash setup.sh Ollama (recommended) or brew install llama.cpp

macOS (Intel) bash setup.sh Ollama (recommended) or brew install llama.cpp

Windows (x64) .\setup.ps1 Ollama (recommended) or GitHub release zip

Quick Start cd job-radar source .venv/bin/activate # Windows: .venv\Scripts\activate

Quick search (no AI — fast)

Search with AI rating (needs a local LLM running)

Interactive mode — just run it and type queries

Web dashboard

Open http://localhost:3000

Start the LLM server (for AI scoring)

Option A: Ollama (recommended)

Option B: llama.cpp (faster)

Source Type Notes

Remotive Job board API Remote jobs

Arbeitnow Job board API Worldwide, paginated

RemoteOK Job board API Remote jobs, good volume

Jobicy Job board API Remote jobs with salary data

Himalayas Job board API Remote jobs with seniority levels

Greenhouse Direct ATS 15 curated tech companies (Gitlab, Figma, Stripe, etc.)

Ashby Direct ATS 15 curated tech companies (OpenAI, Anthropic, Linear, etc.)

LinkedIn Web scraping ⚠️ Off by default — may violate ToS

The Greenhouse and Ashby sources pull directly from company career pages via their public APIs. No login, no API keys. You can edit the company list in companies.yaml. Profile Setup Create a profile.yaml with your details so the AI can score jobs against your actual background: name: "Your Name" title: "Software Engineer" experience_years: 5 skills: [Python, Docker, AWS, React] desired_roles: [Backend Engineer, SRE] salary_min: 100000 salary_max: 160000 location_preference: "Remote" remote_ok: true industries: [Fintech, SaaS] The more detail you put in, the better the scoring. But remember — the AI's score is a suggestion, not a ranking you have to follow. Companies (ATS Sources) Edit companies.yaml to add or remove companies for the Greenhouse and Ashby sources. Just use the company slug (the part of the URL on their careers page): greenhouse: - gitlab - figma - discord - shopify - stripe

ashby: - openai - anthropic - linear - resend - clerk Caching JobRadar remembers jobs you've already seen so you don't re-review them on every run. By default, it keeps a 7-day cache in ~/.jobradar/seen_jobs.db.

Change cache duration to 30 days

Skip the cache entirely

Clear the cache

Header stats — total discovered, high match, pending review, applied Job cards with color-coded match scores (green/yellow/red) Inspector panel — full description, matched keywords, LLM reasoning Kanban pipeline — drag jobs from Discovered → Reviewing → Applied → Interviewing Config editor — edit profile.yaml and companies.yaml in the UI Live activity log — terminal-style console showing LLM scoring progress Keyboard shortcuts — J/K to scroll, E to edit, A to mark applied

Start it with: cd dashboard bash run.sh # Linux/macOS

or

Flag Default What it does

-q, --query — Your search query

-l, --location — Filter by location

-p, --profile profile.yaml Path to your profile

--no-ai off Skip AI rating (faster)

--export — Save results to .json or .csv

--limit 50 Max jobs per source

--max-pages 3 Max pages per source

--max-concurrency 3 Concurrent AI rating calls

--companies companies.yaml Company list for ATS sources

--cache-days 7 Days to remember seen jobs

--no-cache off Disable the cache

--clear-cache — Clear cache and exit

--list-ats-companies — Show configured ATS companies

--enable-linkedin off ⚠️ Enable LinkedIn scraping

--llm-url (auto-detect) LLM server URL (auto-scans 11434, 8080, 1234)

--llm-model qwen3:1.7b LLM model name (override auto-detection)

Changing the Model JobRadar defaults to qwen3:1.7b (1.1 GB, fast on CPU). If you want a different model:

Pull a different model

Use it with JobRadar

A Note on AI in Job Search We built JobRadar because job searching is exhausting and the tools out there either dump too many listings on you or try to automate the whole thing. We think the sweet spot is: let the machine do the grunt work (searching, filtering, summarizing) and keep the human making the actual decisions. The AI scoring is there to save you time reading through listings, not to tell you what to apply for. A 95/100 score doesn't mean "apply immediately" — it means "this one looks relevant, worth a closer look." A 40/100 doesn't mean "skip it" — it might be a role you'd love that the AI just doesn't have enough context for. You are the loop. The AI is just the filter. ⚠️ LinkedIn Warning LinkedIn scraping is off by default. It depends on undocumented HTML that breaks constantly and might violate their ToS. We keep it around because sometimes it's useful, but we'd rather you know the tradeoff: python -m jobradar -q "python dev" --enable-linkedin Running Tests pip install pytest pytest tests/ -v License MIT — see LICENSE

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