Interview Prep Platform

C 64 completed
Desktop App
web_app / typescript · small
103
Files
24,922
LOC
7
Frameworks
10
Languages

Pipeline State

completed
Run ID
#370475
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
79.00
Framework unique
Isolation
Last stage change
2026-05-10 03:35:28
Deduplication group #66475
Member of a group with 1 similar repo(s) — this repo is canonical view group →
Top concepts (2)
Project DescriptionWeb Frontend
Same scanner, your repo: https://repobility.com — Repobility

AI Prompt

Create a desktop interview preparation platform using Electron, React, and TypeScript. The app needs to support practice mode with LeetCode and ML System Design questions, and also include mock interviews with a 30-minute timer. Key features must include sandboxed code execution for Python, Java, and C++, structured diagram saving using React Flow, and personalized AI feedback powered by a local LLM setup like Ollama. Finally, it should track user progress and display analytics.
typescript react electron fastapi web-app interview-prep leetcode llm python desktop-app
Generated by gemma4:latest

Catalog Information

This project is a desktop application designed to help users prepare for interviews by providing practice problems from LeetCode and ML System Design.

Description

The interview-prep-platform is a desktop application that helps users prepare for technical interviews by providing access to practice problems from LeetCode and ML System Design. The platform allows users to browse, search, and solve problems in a user-friendly interface. It also includes features such as problem filtering, sorting, and tagging.

الوصف

هذا المشروع هو تطبيق سطح المكتب مصمم لβοء المستخدمين في الاستعداد للمقابلات الفنية عن طريق تقديم مشكلات تدريب من LeetCode وتصميم الأنظمة الذكية. يتيح التطبيق للمستخدمين تصفح، البحث، وحل المشكلات في واجهة مستخدم سهلة الاستخدام. كما يحتوي على ميزات مثل تصفية المشكلات، ترتيبها، وتسميتها.

Novelty

5/10

Tags

interview-preparation leetcode ml-system-design problem-solving practice-questions

Technologies

anthropic electron openai react recharts tailwind vite vitest

Claude Models

claude (unknown version)

Quality Score

C
64.4/100
Structure
69
Code Quality
69
Documentation
60
Testing
60
Practices
58
Security
68
Dependencies
60

Strengths

  • Good test coverage (32% test-to-source ratio)
  • Code linting configured (eslint)
  • Containerized deployment (Docker)

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • No CI/CD configuration \u2014 manual testing and deployment
  • 3 files with critical complexity need refactoring
  • 1036 duplicate lines detected \u2014 consider DRY refactoring

Recommendations

  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a LICENSE file (MIT recommended for open source)

Security & Health

14.1h
Tech Debt (B)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Repobility (the analyzer behind this table) · https://repobility.com
MIT
License
12.9%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

typescript
40.5%
json
32.6%
markdown
13.4%
python
11.5%
shell
0.7%
sql
0.6%
css
0.3%
javascript
0.3%
html
0.0%
text
0.0%

Frameworks

FastAPI React Electron pytest Vitest Tailwind CSS Vite

Concepts (2)

Source-of-truth: Repobility · https://repobility.com
CategoryNameDescriptionConfidence
Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/
auto_descriptionProject DescriptionA lean desktop application for LeetCode and ML System Design interview preparation with personalized AI feedback.80%
auto_categoryWeb Frontendweb-frontend70%

Quality Timeline

1 quality score recorded.

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