Dont Compete is a purely local, containerized, and agent-driven platform for GATE (Graduate Aptitude Test in Engineering) preparation. It combines a modern React frontend with an autonomous backend pipeline that scrapes, classifies, and generates study materials from raw syllabus PDFs and local LLMs.
- 100% Local & Private: All data processing and AI generation happens on your machine using Ollama. No external APIs, no cloud dependencies.
- Container-First Architecture: The entire system runs via Docker Compose. No local Python or Node.js environment setup required.
- Functional Asset Generator:
- Sequential Pipeline: 8-stage functional sequence (Download -> OCR -> DB Sync -> Classification -> Theory -> Manifest).
- Deterministic: Heuristic Parsing ensures high-fidelity image extraction for questions and explanations.
- Idempotent: Re-runs extend existing datasets instead of reclaiming them.
- Modern Modular Interface:
- Modular Shell: Minimal root layout delegating logic to specialized, reusable components.
- Adaptive Assessment: Handles MCQ, MSQ, and Numeric inputs with real-time validation.
- Dynamic Navigation: URI-based breadcrumbs and stateful dashboard expansion.
The system is split into two autonomous components that communicate via shared file-system artifacts:
- Asset Generator (
/generator): A functional Python pipeline using DuckDB, PyMuPDF,tenacity(retries), and Ollama. - Frontend (
/frontend): A high-performance React application (Vite, TanStack Router) that dynamically discovers generated static assets via filesystem structure (Zero-Config discovery).
For a detailed deep-dive into the system design, components, and data flow, please refer to arch.md.
- Docker Desktop or Docker Engine + Compose.
- Git.
-
Clone the repository:
git clone https://github.com/imxade/dont-compete.git cd dont-compete -
Launch the System:
docker compose up --build
- Frontend: Accessible at
http://localhost:3000. - Generator: Autonomously populates content in the background.
- Idempotency: Existing data is skipped; re-launching only processes new or missing streams.
- Frontend: Accessible at
-
Monitor Pipeline:
docker compose logs -f generator
Central configuration is managed in generator/src/config.py. You can customize:
TARGET_STREAMS: Which exam streams to process (e.g., CS, DA).OLLAMA_MODEL: The local LLM to use (default:llama3.1).
We are building a free, high-quality platform for everyone, and we need your help to achieve that!
AI is a powerful accelerator, but it's not perfect. We rely on the community to ensure quality and depth.
- Improve Theories: AI-generated explanations can be generic or miss nuance. If you have a better explanation, analogy, or diagram for a concept, please submit a PR!
- Quality Assurance:
- Review: Help us verify the correctness of questions and answers in Pull Requests.
- Model Testing: Run the generator with different LLMs (Mistral, Gemma, Phi-3, or larger parameter models on your local machine) and report which yields the best results.
- Community Questions: Identify gaps in our question bank and add commonly asked questions or "gotchas" for specific topics.
- Expand Scope: PRs adding support for other competitive exams are highly welcome! Let's build a universal free platform together.
The project includes a comprehensive test suite that runs in Docker.
1. Generator Tests (Backend)
docker compose run --rm asset-generator pytest generator/tests2. Frontend Tests (Playwright) Run the end-to-end tests using the official Playwright container:
docker run --rm --network gatebuster_app_network \
-e BASE_URL=http://frontend:3000 \
-v $(pwd)/frontend:/app \
-w /app \
mcr.microsoft.com/playwright:v1.58.0-jammy \
/bin/sh -c "npm install && npx playwright test"Note: Ensure the frontend service is running (docker compose up) before starting Playwright tests.
Apache 2.0 License - see LICENSE for details.