Eric S. Raymond (ESR) — author of The Cathedral and the Bazaar, co-founder of the Open Source Initiative, ~50 years of coding — posts on X a counter-testimony to the narrative that "LLMs produce crap code, hallucinate, are useless for programming." A narrative he finds "increasingly puzzling," since this almost never happens to him.
The experience. For two generations of models ("ChatGPT 5.4 and 5.5" under codex), he no longer observes any derailment. The former symptom — a model "going off the rails" near its context limit — has given way to a red warning prompting him to clear the session. His scope is broad: AI applied to feature changes, refactoring and debugging across 63 projects in C, Go, Rust, Python and shell, writing documentation, and even decompiling a DOS binary into readable source. His routine: every time he reopens a project, run the regression tests, then ask codex to audit the code (bugs + improvements).
The verdict. LLMs are "excellent and tremendously empowering." Their worst limitation is "architectural tunnel vision": excellent at coding to specification, but sometimes blind to higher-level patterns — which, he says, remains the job of his "meatbrain." The strongest, counter-intuitive point: LLMs do not get details and edge cases wrong. He declares himself worse than them on this front: if a change must touch five places in the code, the model finds all five, whereas the human fixes four and spends hours debugging before spotting the fifth.
The question. ESR questions the "downshouters": do they live in a different universe? Are they using old, weak models? Do they have a skill issue he doesn't perceive, because his mental habits and communication fit well with these tools' "handles"? He deems the question important, since "billions of dollars would be wasted on misdirected token spend." His recipe, "very simple": "Be clear in your thinking, tell the model what you want with precision, and good things happen" — before the closing line: "what…what am I missing here?"
To be read as a credible pro-LLM counterpoint, signed by a historic figure of open source, to the recurring debate on the value of coding agents — resonating with the specification discipline defended elsewhere (cf. Token Manifesto).