It's hard to write code for computers, but it's harder to write code for humans

Learning: Examples vs Core Concepts

• Big debate around the article’s claim that “humans learn from examples, not core concepts.”
• Many commenters say they need concepts first (theory → examples), otherwise tutorials feel like blind copy‑paste.
• Others strongly prefer example‑first (“here’s a working house; now let’s poke it”), then backfill theory.
• Several argue most people need both: quick examples to get moving, and clear conceptual models to reason, debug, and adapt.
• Math and car‑driving analogies are used on both sides to argue that either concept‑first or example‑first works; consensus is that learning styles differ.

Docs, Onboarding, and Developer Experience

• Strong agreement that good DX is mostly about documentation and onboarding, not just clever abstractions.
• Praise for “just run this one command” experiences vs docs that start with deep architecture and hyperlink mazes.
• References to models like “tutorials vs how‑tos vs reference vs discussions” (e.g., 4-doc) as a useful framing, but not a silver bullet.
• Complaint that big companies often ship complex, under-documented systems where “core concepts” leak everywhere (AWS, NVIDIA tooling mentioned).

Tools, IDEs, and Programming Experience

• Some feel IDEs haven’t fundamentally improved in decades; coding still feels the same, only libraries/docs/QA have improved.
• Others counter that editors like Vim/Emacs with LSP, panes, macros, and live feedback already solve many pain points (multi-file views, refactors, error navigation).
• Visual / graph-based programming is viewed skeptically; graphs quickly become unreadable at scale.
• Interest in live programming (Smalltalk, Lisp-style REPL workflows) as a missed opportunity, but concerns about reproducibility.

Frameworks, Scaffolding, and Generators

• Split views on scaffolding and project generators.
• Critics say they hide concepts, generate opaque boilerplate, and make code harder to understand and maintain.
• Supporters argue opinionated scaffolding is invaluable for non-experts and complex domains, enforcing structure and reducing user error.
• Some see heavy frameworks as primarily benefitting employers (commodity hiring) rather than developer craftsmanship.

Code for Humans vs Computers vs AI

• Broad agreement that code is primarily for humans; machines only need low-level instructions.
• Emphasis on clarity, naming, structure, and documentation as the real hard work.
• Some speculate LLMs might eventually bypass traditional languages, but today they are buggy, average-quality coders at best and require strong human guidance.