Engineering and product writing from the team building Praxys — spec-driven, verified, no shortcuts.https://praxys.co/https://praxys.co/blogs/the-scaffolding-tax/https://praxys.co/blogs/the-scaffolding-tax/In an exploratory API study, 34–40% of billed model tokens went to platform work — database setup, auth, access control, rate limiting, tracing, error handling — rather than task-specific endpoint logic. Task-specific logic was still the largest share, but the plumbing was a substantial part of the bill, and it's the part a good engineering platform can make reusable instead of re-deriving on every service.Mon, 27 Jul 2026 00:00:00 GMTsubstrateengineeringhttps://praxys.co/blogs/llm-lingo/https://praxys.co/blogs/llm-lingo/Teams controlling AI-assisted engineering spend usually reach for the model dial first — smaller model for routine work, frontier model for hard changes. Our benchmark points at a second lever hiding in the architecture: for the same bounded coding task, the programming language an LLM is asked to write changed the token footprint of a working solution by roughly 2x between the most compact and most verbose cases. The careful conclusion isn't a language ranking — it's that token economics belongs in the decision, honestly, alongside fluency, runtime economics, and governance.Mon, 27 Jul 2026 00:00:00 GMTengineering