CareerRadar

Real-time developer job fit copilot powered by Jev System One (@typesafe-ai/sdk).
Evaluates live job postings on LinkedIn against candidate verified systems scale in sub-800ms.

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⚡ Quick Setup

1. Install & Configure

git clone git@github.com:GauravAhuja7/CareerRadar.git
cd CareerRadar
npm install

Create a .env file in the root directory:

JEV_API_KEY=your_typesafe_jev_api_key_here
PORT=3001

2. Start Local Engine

npm run dev

• API Server: http://localhost:3001
• Web Hub: http://localhost:5173

3. Load Chrome Extension

1. Open Chrome and navigate to chrome://extensions.
2. Toggle Developer mode (top right) ON.
3. Click Load unpacked and select the extension/ directory from this repo.
4. Pin CareerRadar and open any job listing on LinkedIn, Indeed, Wellfound, Greenhouse, or Ashby.

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🛠️ What I Built & How It Works

1. Anti-Slop Architecture: Replaced slow generative LLMs with Jev System One (jev-latest): evaluating state against typed mathematical primitives (choice, score, noul) rather than streaming hallucinated text.
2. Context Subagent: An in-browser content observer detects active job postings, extracts clean qualification requirements, and pairs them against uploaded candidate resume vectors without DOM shift.
3. Speculative Fan-Out: Evaluates application verdicts (CAN_APPLY, REACH_APPLY, SKILL_MISMATCH), technical synergy (/4), and screening odds in a single parallel inference pass.
4. Scope-Over-Tenure Engine: Distinguishes calendar years from verified scale (e.g., proves how 1.5 yrs building high-throughput Kafka/AWS pipelines offsets a "3-5 yrs required" requirement).
5. Linear/Raycast UI: Built a restrained Chrome side panel exposing live fit gauges, skill alignment bars, and 1-click tailored interview pitches.
6. Telemetry & Calibration: A slide-up reasoning drawer surfaces raw Bayesian probability distributions and confidence scores in <800ms.