Documentation
Learn Alpha.
Start with Getting started, keep the language guide open while you write, and turn to the specification when you need the exact rule.
Browse every Alpha source file →Learn
Build the compiler, run a first program, and build a GPU training executable.
Language guideexplanationThe full tour: syntax, types, quantities, effects and families, and why Alpha is built this way.
Families and branchesanalysisIndexed data, constructors, motives, recursion, and exhaustive elimination.
Understand
How Alpha turns learning ideas into checked, comparable experiments on real hardware.
Language and compileranalysisHow source becomes checked core, typed machine work, native artifacts, and evidence.
Built for agentsanalysisWhy Alpha is literal, fast to compile, strictly typed, and names every device and GPU instruction: so agents can take a system apart and rebuild it.
PhilosophyanalysisWhy Alpha reopens the design space for agents instead of freezing today's AI stack.
The living catalogueanalysisHow a growing library of typed knowledge could let agents assemble and test learning systems from humanity's reusable ideas.
Catalogue-guided searchanalysisA research architecture for turning source-grounded mechanisms into diverse, qualified Alpha experiments.
Evaluate
What Alpha is good at, what it is bad at, and the central tradeoff.
Criticisms and hard questionsanalysisAn adversarial review agenda covering Alpha's thesis, engineering, science, evidence, and viability.
Where the ideas come fromanalysisPrimary literature behind dependent, quantitative, effectful, and verified programming.
Evidence and statusanalysisHow to read checked, run and measured claims, and what each one does not establish.