⚠️ ARCHIVED: This project has been archived as a technical showcase. For a practical ML tool solving real user problems, see DataLint - automated data validation for ML teams.
NeuralDSL was an ambitious domain-specific language for neural networks, featuring multi-backend code generation, shape propagation, and advanced debugging. While technically sophisticated, it didn't address real user pain points.
Building NeuralDSL taught me valuable lessons about product development:
- Technical elegance ≠ user value: Complex DSLs are fascinating to build but don't solve pressing problems
- Users want simplicity: ML practitioners prefer familiar tools over new abstractions
- Validation before building: Talk to users before investing months in features
- MVP mindset: Start with minimal, validated solutions rather than comprehensive platforms
This repository remains available as a demonstration of advanced Python patterns:
- Complex parser implementation (Lark-based DSL parsing)
- Multi-backend code generation (TensorFlow, PyTorch, ONNX)
- Advanced type system and shape propagation
- Plugin architecture and extensibility patterns
- Comprehensive testing and CI/CD setup
After realizing NeuralDSL solved theoretical problems rather than real ones, I'm building DataLint - automated data validation that prevents ML model failures by learning from clean datasets.
DataLint focuses on:
- ✅ Real user problems: Data quality issues cause 60% of ML failures
- ✅ Immediate value: Saves hours of manual validation work
- ✅ Simple adoption: Works with existing pandas workflows
- ✅ Measurable ROI: Prevents costly model redeployments
This project represents an important step in my journey from technical exploration to user-focused product development.
This project is licensed under the MIT License - see the LICENSE.md file for details.