Show HN: Vibe coding a bookshelf with Claude Code

Vibe coding and ideal project size

• Many see “vibe coding” as perfect for small, self-contained apps: one-off scripts, single-page tools, and “software for one” that would otherwise live in the “someday” pile.
• Commenters note a clear size boundary: once a project has too many files or interdependencies, LLMs start to over-generate, introduce odd abstractions, and miss subtle bugs.

Workflows, architecture, and context management

• Several describe a plan-first workflow: have the AI draft an implementation plan, refine it, save it as markdown, then have the model implement the plan.
• Good results on larger projects reportedly depend on: well-defined tickets, clear module architecture, example code, and “agent.md” guidance.
• Classic software design advice resurfaces: program to interfaces, keep modules decoupled, and limit how much code must be in context for any change.

Human intent, taste, and authorship

• A recurring theme: the model handles execution, the human provides intent and taste. That’s framed as the main leverage.
• Others argue “taste” itself can be modeled and tuned, as seen in image models; some see “AI taste” as already influencing UI and writing styles.
• The article’s rhetorical style triggers debate about “AI-smelling” prose and whether polishing with LLMs flattens individual voice.

Usefulness, productivity, and learning trade‑offs

• Supporters say LLMs collapse the cost of trying ideas: days of side-quest coding become minutes or hours, especially for non-full-time programmers.
• Multiple people share similar vibe-coded projects (bookshelves, note libraries, movie trackers, learning tools), often built “for fun” rather than hard utility.
• Critics worry that outsourcing implementation robs people of learning, craftsmanship, and the satisfaction of doing the “tedious” parts themselves.

Novelty, limitations, and skepticism

• Skeptics observe that successful vibe-coded apps are almost always variants of things in the training data; they want to see genuinely novel algorithms or breakthroughs.
• Others counter that most real-world coding is repetitive plumbing, not new compression algorithms, and automating that is already transformative.
• There’s frustration that marketing implies 10–100× productivity, while open-source maintainers aren’t reporting decade-worth jumps in progress—only more AI-generated “slop” to triage.

Personal software vs SaaS and safety concerns

• Some argue LLMs make it finally practical to “roll your own” personal tools instead of adapting to bloated, enshittified SaaS.
• Others stress that vibe coding is unsuitable for safety‑critical domains (planes, air traffic control), yet expect people will still try, raising reliability and oversight concerns.