Inspiration
Inspiration
Finding relevant hackathons, internships, and developer opportunities can be time-consuming. Developers often have to search across multiple websites and manually decide whether an opportunity matches their skills. I built AI Opportunity Scout to make this process faster and more personalized.
What it does
AI Opportunity Scout helps developers discover opportunities based on their skills, preferred opportunity type, and location. Users can enter skills such as Python, AI Agents, and Machine Learning, and the application searches for relevant opportunities.
Each result provides a match score, matched skills, deadline status, a short explanation of why the opportunity matches the user, and a direct link to learn more.
How I built it
The application is built with Python and Streamlit. It uses SerpApi to retrieve web search results and a custom matching system to compare opportunities with the skills entered by the user.
The application is deployed online using Streamlit Community Cloud, while the source code and project documentation are maintained on GitHub.
Challenges I ran into
One challenge was converting general web search results into useful structured opportunities. I also had to handle API authentication, skill matching, deadline information, and deployment configuration.
Accomplishments that I'm proud of
I built and deployed a working application that turns a user's skills into personalized opportunity recommendations. The project includes match scoring, skill detection, opportunity explanations, deadline indicators, and direct links to opportunity sources.
What I learned
I learned more about API integration, Streamlit application development, search-result processing, environment secrets, Git/GitHub workflows, and cloud deployment.
What's next
Next, I plan to improve AI Opportunity Scout with smarter AI-based ranking, better deadline verification, filters, saved opportunities, notifications, and more reliable opportunity-source validation.