International Journal of Web Applications Hathairat Ketmaneechairat 18 1 2026 https://doi.org/10.6025/ijwa/2026/18/1/13-24 https://www.dline.info/ijwa/fulltext/v18n1/ijwav18n1_2.pdf This study introduces a dual dataset framework for analyzing the co-evolution of macro level digital infrastructure and micro level developer ecosystems. We integrate 45 years (1980-2020) of country level telecommunications indicators spanning mobile penetration, internet adoption, and broadband diffusion across 217 economies with a contemporary snapshot of 1,247 trending GitHub repositories, capturing realtime developer attention across 40+ programming languages. Through fork to star ratio analysis, we identify distinct behavioral archetypes: research oriented languages (R, Jupyter Notebook) exhibit 4-13x higher forking rates (median ratios 0.29-0.38), reflecting academic reuse patterns for method adaptation and reproducibility, while infrastructure languages (Rust, TypeScript) demonstrate star-dominant engagement (ratios <0.04), indicating production tool consumption via stable APIs rather than source modification. Critically, we document the emergence of compositional language ecosystems where polyglot architectures deliberately stratify languages by computational layer Rust for systems safety, Python for AI orchestration, and TypeScript for interfaces moving beyond "language wars" toward purpose driven symbiosis. These patterns reveal programming languages function not merely as syntactic tools but as socio technical coordination mechanisms governing community behavior. The framework enables novel cross domain inquiries linking national infrastructure quality to global innovation patterns, with implications for digital policy design and open source ecosystem stewardship.