Indie at Scale: How Large Language Models Make Community-Centered Game Development Economically Viable

Large language models are now widely used in software engineering, yet most evidence of their practical effect comes from corporate productivity studies or synthetic bench-marks. This study offers a complementary view from the solo Indie developer’s perspective: a reflective case study of two iOS games developed in continuous pair programming with a large-language-model coding assistant and subsequently released on the App Store. Both games target communities that mainstream consumer software has long underserved. The Listening Maze is a spatial-audio echolocation maze for blind and low-vision players. Trace Memory Game is a memory-recall game built around brief-flash visual encoding and freehand redraw, positioned for daily cognitive engagement rather than cognitive training or clinical intervention. We describe the development methodology, the four-step human-LLM loop used throughout, the resulting codebases, an indicative break-even analysis, and what the experience implies for socially oriented software development by very small teams. We propose the crossover thesis as a design heuristic and label every claim in the discussion as observation, interpretation, or hypothesis. The evidence is an existence proof from a single developer on a single platform, not a population estimate.

@article{Qarkaxhija2026,
  title     = {Indie at Scale: How Large Language Models Make Community-Centered Game Development Economically Viable},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {8},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Jusuf Qarkaxhija},
  doi       = {10.14569/IJACSA.2026.0170880},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170880}
}