"Vibe code hell" has replaced "tutorial hell" in coding education

AI as Learning Aid vs. Crutch

• Many experienced developers find tools like Copilot great for learning new languages (e.g., Rust), treating them as “super-powered autocomplete” or man pages that lower syntactic overhead while preserving conceptual focus.
• Several commenters stress this only works if you already “know how to code” and can recognize bad code smells; beginners often lack that filter and may solidify confusion.
• Some report that turning off AI revealed they’d learned far less than they thought, and that long-term use caused noticeable skill and syntax atrophy.

Sycophancy, Bias, and “Vibe Code Hell”

• The article’s “sycophant” critique resonated: LLMs tend to agree and optimize for engagement, not truth, which can subtly reinforce users’ misconceptions.
• People try mitigations (e.g., prompting from opposing biases, asking for harsh critique) but note they can’t see their own hidden biases, making this unreliable.
• “Vibe coding” is distinguished from “tutorial hell”: tutorials give some conceptual knowledge but no independent skill; vibe coding gives operational skill with AI but little underlying understanding of the code produced.

Auto-complete, Documentation, and Tutorials

• Strong debate over AI autocomplete in learning:
  • Pro: great for exploring APIs, new language features, and small snippets you can still review; similar to but more powerful than classic IntelliSense.
  • Con: encourages blind trust in opaque “black boxes,” erodes manual ability, and hides important alternatives that traditional completion lists would surface.
• Many dislike the trend of “I don’t understand the docs, send video,” preferring searchable text; videos are seen as slow and hard to reference, though short, focused ones have a place.
• Good tutorials should force struggle: explanations, debugging, wrong turns, and “why” — not just copy/paste steps.

Education Models and Discomfort

• There’s broad agreement that deep learning is uncomfortable and requires repeated failure, but some argue it should be “challenging” rather than gratuitously painful.
• Long discussion contrasts apprenticeship, self-teaching, and university:
  • Apprenticeship is praised as the historic model for crafts, but hard to scale in modern tech.
  • Others argue access to knowledge (docs, books, online material) plus self-directed projects can substitute, though not for everyone.
  • Universities are seen by some as excellent foundations and by others as largely orthogonal to real-world programming skill.

Code Volume, Testing, and Workplace Impact

• Organizations now churn out far more code via non-expert “vibe coders,” but reviewer capacity is unchanged. This leads to large volumes of low-quality code that seniors must audit or rewrite.
• Some propose using LLMs to generate tests and move toward definition-and-test–driven workflows, asserting big productivity gains.
• Others counter that LLM-generated tests are often shallow or tautological, and that serious correctness still requires precise human specifications and careful verification.
• There’s worry about a looming “drought of educated workers”: if early-stage learning is offloaded to AI, future senior engineers with deep understanding may be scarce, affecting everyone who depends on complex systems.