Designed around discovery.
What would a language look like if the whole research workflow mattered? These are the questions guiding Aner.
OUR STARTING POINT
Make the assumptions behind an experiment as understandable as its code.
01
Active design work
02Reproducibility as part of the workflow
Can explicit partitions, fitted preprocessing, and source identity reduce avoidable research mistakes?
Active design work
03Data contracts before model code
Make schema, missingness, provenance, and resource decisions explicit without a long cleaning script.
Active design work
04Learning that can be inspected
Connect source, data, network structure, and training history in a coherent research workspace.
Research direction
05One language, deliberate execution choices
Establish clear CPU semantics before extending to native compilation, GPUs, and larger machines.
Research direction
An ecosystem others can build on
Design stable library boundaries, package identity, and documentation that grows with the language.
Have a question worth exploring?
Help turn a real research challenge into a better language design.