Dan Luu's large-scale evaluations show that the token-efficiency advantage of terse programming languages for AI coding agents largely disappears on real tasks, contradicting a widely cited claim. In …
A widely cited analysis by Martin Alderson comparing token efficiency across programming languages found a 2.6x gap between C (least efficient) and Clojure (most efficient), with J averaging 70 tokens…
A widely cited blog post by Martin Alderson claims dynamically typed languages are more token-efficient for LLMs, with Clojure at 109 tokens and J at 70 tokens versus C's 2.6x higher cost, but a new a…
Ruby on Rails is a strong fit for AI agents because of its token efficiency, predictability, and ecosystem ergonomics, according to an analysis by Martin Alderson, co-founder of CatchMetrics. Alderson…
Z.ai's open-weight GLM-5.2 model, released in June 2026, approaches proprietary system performance on coding and agentic tasks while compressing inference costs, according to benchmarks and developer …