This is a submission for the Gemma 4 Challenge: Build with Gemma 4
PR Sentinel analyzes React and TypeScript snippets and generates structured engineering feedback focused on maintainability, accessibility, performance, and UI quality.
What I Built
PR Sentinel is an AI-assisted frontend PR reviewer focused on React and TypeScript engineering quality.
Developers can paste frontend code snippets and receive structured engineering feedback across:
- React best practices
- Maintainability
- UI/UX quality
- Accessibility (A11y)
- Component architecture
- State management patterns
The project was inspired by real frontend review pain points commonly seen in enterprise applications, especially issues related to stale closures, infinite re-renders, semantic accessibility structure, and reusable component design.
Instead of producing generic AI summaries, PR Sentinel organizes feedback into categorized engineering review cards that resemble actual senior-level frontend review comments.
Demo
Live demo uses a limited development API configuration and may occasionally be unavailable during evaluation periods.
Code
(https://github.com/naomirasamalla/Frontend-PR-Review-Assistant)
The application allows developers to paste frontend snippets and receive categorized AI-generated engineering review feedback in real time.
Key Features:
AI-powered React/TypeScript review analysis
Structured frontend engineering feedback
Accessibility-focused review insights
UI/UX and maintainability recommendations
Real-time review rendering
How I Used Gemma 4
PR Sentinel uses Gemma 4 to analyze React and TypeScript frontend code snippets and generate structured PR-style engineering feedback.
The project focuses on identifying practical frontend issues such as:
- infinite render loops
- stale closures in async logic
- unsafe DOM access patterns
- maintainability concerns
- accessibility-related frontend risks
Gemma 4 was selected because the project required fast reasoning over frontend engineering patterns while generating concise, developer-focused review output.
The model is used to evaluate pasted code snippets and return structured recommendations similar to a lightweight frontend pull request review workflow.
The application includes built-in diagnostic sandbox scenarios that simulate real frontend engineering issues commonly encountered in React applications.


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