Falsehoods Vibe Coders Believe About LLMs (wilsonhobbs.com)

Patrick McKenzie’s classic “Falsehoods Programmers Believe About Names” inspired a short, sharp take on what newcomers to “vibe coding” wrongly assume about LLM-generated code. The author catalogs dozens of myths — that LLMs always (or even reliably) produce working, compiling, secure, or useful code; that they can deterministically check correctness, reason about intent, or decide if code will halt; that they won’t invent libraries or APIs; or that they remember and act in anyone’s best interests. The post’s point: while LLMs empower faster prototyping and let non‑engineers build useful tools, blind trust is dangerous and many people overestimate the models’ capabilities. For the AI/ML community this is a practical reality check: LLMs often produce syntactically plausible but semantically incorrect code, hallucinate nonexistent packages or “standard” patterns, and cannot solve undecidable problems (e.g., general halting/termination) or perform formal verification. They have limited context/recall and no intrinsic understanding of intent or security. Implications are straightforward — treat LLM output as draft artifacts: validate with tests, static analysis, fuzzing, human code review, provenance checks for dependencies, and staged deployments. Vibe coding can be safe and productive, but only when combined with engineering rigor and explicit verification, not as a substitute for it.