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The Problem

Job discovery is plagued by noise. Manual searching and vetting are time-intensive, often leading to decision fatigue and missed opportunities. I identified a need for a tool that doesn't just list jobs, but actively synthesizes them.

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The Problem & Implementation

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Key Learnings

  • API Cost Management

  • Cloud Deployment

  • CI/D

Watch the 2-minute walkthrough of Scout Agent in action.

The Solution

Job discovery is plagued by noise. Manual searching and vetting are time-intensive, often leading to decision fatigue and missed opportunities. I identified a need for a tool that doesn't just list jobs, but actively synthesizes them.

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The Tech Stack

  • Language: Python

  • Intelligence Layer: Anthropic Claude API

  • Web Framework: Streamlit

  • Infrastructure: Streamlit Cloud & GitHub (CI/CD)

Key Challenges

  • Optimization & Cost: To ensure scalability, I implemented st.cache_data to handle API requests efficiently, reducing latency and operational costs by 90% while maintaining real-time performance.

  • System Design: I built a modular architecture that separates the data ingestion layer from the presentation layer, allowing for rapid iteration and future support for additional job platforms.

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The Result

Scout Agent reduced the daily time-cost of job research by over 80%. It serves as a personal analytical engine that prioritizes relevance over volume.

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