RepoSense

Understand any GitHub repository in minutes using Gemini-powered reasoning.

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RepoSense — About the Project

Inspiration

Understanding an unfamiliar codebase is one of the most time-consuming and frustrating tasks for developers. Whether onboarding to a new team, reviewing an open-source project, or evaluating a hackathon submission, developers often spend hours navigating folders, tracing data flow, and inferring architectural decisions.

Most existing tools focus on syntax-level or static analysis. They explain what the code does, but rarely explain why a system is structured the way it is.

The release of Gemini 3, with its enhanced reasoning and long-context capabilities, inspired me to explore a different approach: treating a codebase as a system to be reasoned about, not just a collection of files. This idea led to the creation of RepoSense.

What I Built

RepoSense is a Gemini-powered code intelligence tool that analyzes public GitHub repositories and produces system-level insights rather than conversational responses.

Given a repository URL, RepoSense infers:

  • The project’s purpose and intended users
  • High-level system architecture and design patterns
  • Responsibilities of key folders and components
  • Data and control flow across the application
  • Code quality issues, risks, and anti-patterns
  • Actionable improvement recommendations
  • A simple ASCII architecture diagram

The output is structured, deterministic, and optimized for rapid understanding.

How I Built It

RepoSense was built entirely using Google AI Studio (Antigravity) with Gemini 3 as the core reasoning engine.

Key design decisions include:

  • Reasoning-first design
    The application avoids chat-style interactions and instead produces structured system analysis.

  • Prompt engineering
    Carefully designed prompts guide Gemini 3 to reason holistically about repositories, infer architecture, and avoid generic advice.

  • Interactive frontend
    Users can switch between full analysis, architecture-only, and risk-focused views, with an optional deep reasoning mode.

  • Graceful latency handling
    Since deep analysis can take time, the UI communicates progress clearly instead of failing or blocking.

No traditional backend is required. Gemini 3 API access and execution are managed securely by Google AI Studio.

Challenges Faced

One major challenge was handling real-world constraints such as GitHub API rate limits when analyzing popular repositories. Instead of exposing raw errors, RepoSense switches to a graceful fallback mode that clearly communicates temporary limitations while still delivering meaningful, inference-based insights.

Another challenge was ensuring that Gemini 3’s output remained precise and non-generic. Achieving this required multiple iterations of prompt refinement to balance depth, clarity, and determinism.

Designing a frontend that felt like a real developer tool—not a demo—was also a key focus, especially around alignment, typography, theming, and dark/light mode support.

What I Learned

Through this project, I learned:

  • How to design prompts for long-context, system-level reasoning
  • How to build AI-powered tools beyond chat interfaces
  • How thoughtful UX can build trust in AI systems
  • How to handle API limitations gracefully
  • How to translate raw AI capability into a usable product

Most importantly, I learned how powerful reasoning-focused models like Gemini 3 can be when treated as analytical engines rather than conversational agents.

Conclusion

RepoSense demonstrates how Gemini 3 can power next-generation developer tools that reason about complex systems, not just individual code snippets. By focusing on architecture, intent, and system behavior, the project highlights a practical and impactful use of Gemini 3’s advanced reasoning capabilities.

Built With

  • 3
  • ai
  • ai-powered
  • analysis
  • antigravity
  • api
  • application
  • applications
  • architecture
  • artificial
  • cloud
  • code
  • css
  • dark
  • deployment
  • design
  • developer
  • development
  • engineering
  • frontend
  • gemini
  • gemini-3
  • generative
  • github
  • github-api
  • google
  • google-ai-studio
  • html
  • intelligence
  • interactive
  • javascript
  • language
  • large
  • light
  • long-context
  • mode
  • models
  • prompt
  • prompt-engineering
  • reasoning
  • repositories
  • software
  • static
  • studio
  • system-level
  • tools
  • ui
  • ui/ux
  • vercel
  • web
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