generative coding model trained to ONLY generate 3B1B videos using https://github.com/DannyMang/3b1b library!
update: we will use community edition found at https://github.com/ManimCommunity/manim/ since community version looks more polished
CURRENT GOALS:
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clean up dataset
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prevent usage of llm with other irrelevant queries (perhaps refinetune)
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look into other training techniques that may improve performance
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how can we measure performance metrics of the model?
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ensure output provides synthatically correct code
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ALL OF GRANT"S VIDEO CODE CAN BE FOUND : https://github.com/3b1b/videos
- lets try finetuning a model to see how good it performs
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came across https://github.com/unslothai/unsloth, we're gna use this to finetune efficiently:
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relevant resources https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune#fine-tuning-qwen3-with-unsloth
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current goal build dataset to finetune on
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we will be using qwen 3, 14b just because alibaba >
overview of plans:
- Modern Serverless Architecture? [User Interface] → [API Gateway] → [Authentication Service] → [LLM Service] → [Rendering Service] ↓ [Database] ← [Payment Processing] → [User Management] Key Components:
Frontend: React/Next.js application hosted on Vercel or Netlify API Gateway: AWS API Gateway or Cloudflare Workers Authentication: Auth0 or Supabase for secure user authentication LLM Service: Containerized Qwen3 model on AWS ECS/EKS or GCP Cloud Run Rendering Service: Service that takes generated Manim code and renders videos Database: MongoDB or PostgreSQL for storing user data and generated content Payment Processing: Stripe for handling subscriptions and one-time purchases
- Security Measures Input Validation: Implement strict validation of user inputs to prevent prompt injection attacks Rate Limiting: Limit API calls per user to prevent abuse Content Filtering: Filter both input prompts and model outputs for inappropriate content Output Sanitization: Validate and sanitize generated code before execution Encryption: Use TLS for data in transit and encryption for data at rest Authentication: Implement robust OAuth or JWT-based authentication Monitoring: Set up logging and real-time monitoring to detect unusual patterns Regular Audits: Conduct security audits of your infrastructure
UX/UI Design for Your Target Audiences Product will serve two main audiences:
- Developers
Key UX Elements:
Clean, minimalist interface with code view options Detailed API documentation with examples Option to customize parameters (temperature, etc.) Code export functionality with different format options Git integration for version control of animations
- Educators/Teachers Educators want simplicity, visual feedback, and educational value. Key UX Elements:
Guided wizard interface with templates Visual preview of animations in real-time where possible Curriculum integration examples Ability to save and organize projects by subject/lesson Collaboration features for team teaching Export formats compatible with classroom presentation software
Technical Implementation Roadmap Phase 1: MVP (2-3 months)
Deploy Qwen3 model with basic prompting Build simple web UI for text-to-animation generation Implement basic user authentication Set up simple payment processing with Stripe Develop basic animation rendering pipeline
Phase 2: Enhancement (2-3 months)
Improve model fine-tuning with user feedback Build API for developer access Add more animation templates and examples Implement more robust security measures Develop education-specific features
Phase 3: Scaling (2-3 months)
Optimize infrastructure for cost and performance Implement advanced analytics Build collaboration features Develop integration plugins for common education platforms Expand marketing and partnership efforts
Technology Stack Recommendations Frontend
Framework: Next.js with TypeScript UI Library: Tailwind CSS + Shadcn UI State Management: Zustand or Redux Toolkit Animation Preview: Three.js or custom WebGL renderer
Backend
API: Node.js with Express or FastAPI with Python LLM Deployment: Docker + Kubernetes or serverless options like AWS Lambda Database: MongoDB for flexibility or PostgreSQL for relational data Caching: Redis for performance optimization Video Processing: FFmpeg for video generation
Infrastructure
Cloud Provider: AWS, GCP, or Azure CI/CD: GitHub Actions or GitLab CI Monitoring: Datadog or Prometheus + Grafana Security: AWS WAF, CloudFlare, or Akamai