MAYAN Alfa

MAYAN ALFA is an independent computational research framework developed by David Hess for deterministic prime-distribution analysis and large-scale numerical observation. Its work centers on segmented filtration, structured interval mapping, and reproducible validation across arithmetic ranges. The framework takes a correctness-first approach, emphasizing traceable workflows and carefully bounded evidence before performance optimization.

MAYAN ALFA provides measurement methodologies, benchmark and evidence materials, research guides, and archives that document its processes and results. Measurements are reported with their limits, execution modes, steps, and validation contexts; full-workflow runtime is distinguished from clean-binary timing to support responsible interpretation. Public evidence is presented within a defined 10-million-to-10-billion range, while BRONZE, SILVER, and GOLD archive layers extend controlled research continuity to larger ranges. GitHub releases and Zenodo DOI records support public access, verification, and preservation. The framework also maintains a deliberate boundary between public materials and controlled archive content, with its documentation covering release rules, validation, publication practices, and long-term research continuity.