Can Language Models Go Beyond Coding? Assessing the Capability of Language Models to Build Real-World Systems

Build-bench addresses a highly specific and technically challenging niche: software migration across instruction set architectures. While standard benchmarks like SWE-bench focus on general software engineering, this project targets the nuances of build logs, heterogeneous toolchains, and environment-specific dependencies. Its defensibility is currently low (score 4) because it is primarily an academic artifact with zero star-based community traction, though the 11 forks suggest active research interest. The 'moat' here is the curation of complex, real-world build failure scenarios which are difficult to replicate without deep DevOps expertise. Frontier labs are unlikely to compete directly by building an 'ISA migration benchmark,' but their general-purpose reasoning agents will naturally improve on these tasks. The project's value lies in being a specialized evaluation tool for companies building AI agents for cloud infrastructure migration (e.g., moving workloads from Intel to AWS Graviton/ARM). Its primary risk is falling into obscurity if it is not adopted by the wider AI software engineering community as a standard alongside SWE-bench.