Vibe Code Warning – A personal casestudy

Emotional and Cognitive Effects

• Many describe LLM-heavy “vibe coding” as mentally dulling: similar to doomscrolling or gambling (“just one more prompt”), leaving them empty, detached, and needing rest to reset.
• Key loss is the internal mental model: after a few thousand lines, they no longer understand the code or feel it’s “theirs,” so there’s little sense of growth or accomplishment.
• Others report the opposite: they enjoy staying in a high-level “flow” of ideas while the machine handles implementation, finding traditional coding more frustrating than satisfying.

What “Vibe Coding” Means

• Original definition: describe a feature, have the LLM generate large chunks of code, avoid reading it, judge only by whether it runs and tests pass, then iterate via more prompts.
• Several commenters note the term is now blurred and often used for any AI-assisted coding, even when there is heavy planning, review, and structure.

Productivity: Where It Helps and Where It Fails

• Clear wins cited for:
  • Boilerplate, CRUD, simple tools, data transformations, test case generation.
  • Reading large docs and code and producing summaries, scripts, or prototypes.
• Mixed or negative experiences for:
  • Large, evolving codebases and low-level or high-correctness systems.
  • Feature work where architecture and invariants really matter; subtle bugs, duplication, and incoherent structures appear.
• Some say with good judgment about scope, it’s “significantly faster”; others say speed gains are illusory once you factor in debugging, refactoring, and later changes.

Planning, Discipline, and Workflows

• Strong emphasis from AI-positive users on:
  • Detailed upfront planning and architecture, often stored in Markdown/spec files.
  • Breaking work into very small, well-defined tasks; extensive tests; aggressive refactoring.
  • “Context engineering” (curating files, docs, conventions, AGENTS.md) rather than prompt wordsmithing.
• Others push back that this level of process is far from the marketed “just talk to it” vision, and that many still get bizarre failures despite careful planning.

Craft, Meaning, and Ownership

• Big divide between:
  • Those who value programming as a craft (like woodworking or hand-carving) and feel AI removes the meditative, learning-rich part of creation.
  • Those who care mainly about outcomes (shipping apps, side projects) and see AI as analogous to power tools or industrialization.
• Several note that joy often comes from gradually building a deep model of the system; vibe coding short-circuits that learning.

Reliability, Responsibility, and Risk

• Consensus that developers remain responsible: “AI slop in your codebase is only there because you put it there.”
• Concerns about:
  • Non-determinism and hallucinations, especially in complex or safety-sensitive domains.
  • Long-term maintainability of AI-written “spaghetti” and “balls of mud.”
  • Model/data poisoning as AI-generated code floods open source and training corpora.
  • Copyright ambiguity for heavily AI-generated projects and the mismatch with human-centric licenses.

Long‑Term Concerns and Adaptation

• Comparisons to self-driving cars: as long as humans must remain vigilant over an untrustworthy system, the cognitive load may be higher than doing it yourself.
• Analogies to artisans displaced by assembly lines: some see AI as inevitable and advise embracing it; others worry about deskilling, loss of meaningful work, and a world optimized for “getting things done” over human fulfillment.
• Many settle on a hybrid: use LLMs as powerful assistants for search, planning, and boilerplate, but keep humans in charge of core design, critical code, and understanding.