*This is a submission for the Agent.ai Challenge:
Full-Stack Agent (See Details)*
Assembly of Agents (See Details)
Productivity-Pro Agent (See Details)*
What I Built
I built GLaDOS, an AI-powered game recommendation system that recommends games and links to buy them based on user's favorite games.
Why I Built This
Gamers are always looking to play more titles that are familiar enough to them genre wise but are entirely new adventures, yet they have no idea what to play. GLaDOS (inspired by the iconic character from Portal) is here to solve that issue by providing tailored recommendations to its users.
Demo
Check out GLaDOS in action:
Demo Link
Features
- Dynamic User Input: Users simply provide their steam profile and GLaDOS does the rest.
- Custom Game Recommendations: GLaDOS creates recommendations tailored to what you have already played and enjoyed.
- Personalized Suggestions: Recommend games based on user taste, past played games, and current favorite games (most hours spent).
How I Built It
1. Web Scraping: Used agent.ai builder's to scrape a user's steam profile when provided with the link.
2. Information extraction: Using GPT-4o to extract necessary and relevant information only from the web scraping.
3. AI-Driven Recommendations: GLaDOS takes into account past played games, games in showcase and favorite games to generate recommendations.
Agent.ai Experience
Building GLaDOS with Agent.ai was both rewarding and challenging:
Delightful Moments
- The no-code builder made this whole process seamless and easy.
- Watching GLaDOS craft amazing and personalized recommendations felt like magic!
- The intuitive design of the agent AI builder made it very easy to use
Challenges
- Extracting the proper relevant data from the steam profile.
- Fine-tuning the AI responses for properly generating the required results.
- Fine-tuning the prompts and engineering a solution to get the best possible results.
Future Enhancements
- Bigger Library: Gaining access to the user's other accounts across platforms and recommending games via those.
- Mobile App Development: Enable users to access recommendations on the go and also implement this for mobile games.
- Real-Time Feedback: Incorporate real-time user feedback to improve game recommendations.
- Multi-Language Support: Make the app accessible to a global audience.
- Recommendation History: Track past recs and suggest newer ones based on what the user liked before.
Thank you for exploring GLaDOS, your AI-powered game recommender! Do share your love and support!
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