Small steps.
A considered direction.
Build a useful foundation, test it with people, and grow from evidence. This roadmap separates implemented behavior from future work.
Working today · Development implementation
A native foundation.
- C++ interpreter with static checking and inferred return types
- Generic classes, private fields, and managed object memory
- Seven modules for data science
- Dense neural networks, KNN, and K means on CPU Float64
- Offline Iris and Wine with attribution and reproducible partitions
- Typed, budgeted CSV processing and persistent VS Code notebooks
02
Next milestone · Public preview preparation
Ready to share and learn.
- Finalize the public repository and open source license
- Validate release packages and compatibility on each target OS
- Publish checksums, release notes, and contributor guidance
- Improve diagnostics and test real research workflows
- Develop tutorials and collect feedback from early users
03
Looking ahead · Design & research
Grow with the questions.
- Native compilation of Aner programs and execution backends
- More numerical types, precision controls, GPU and distributed execution
- Richer table operations and additional data sources
- Live variable inspection, training charts, and linked notebook debugging
- CNNs, RNNs, SVMs, decision trees, and model persistence
- Independent packages and a library ecosystem for outside contributors