Beej's Guide to Learning Computer Science

Original Article ↗ Hacker News Discussion ↗

Reactions to Beej’s Guides

  • Strong praise for the networking and C guides; many say they were instrumental for courses, careers, and teaching.
  • Appreciated for being concise, practical, and free, in contrast to paywalled/SEO’d content.
  • Some nostalgia for “small web” / 90s hacker culture and admiration for the author’s commitment to high‑signal, non-commercial teaching material.

What This New Guide Actually Covers

  • Several readers note it’s more “how to learn and think like a programmer” than a traditional computer science text.
  • Critiques of the title: people expected topics like complexity, automata, computability, etc.; see it more as software engineering / programming advice.
  • The author clarifies it’s aimed at undergrad CS students and focused on learning strategies, mindset, and habits, not a full CS curriculum.

Math for CS and Adult Learners

  • Consensus: re-learn high-school algebra first; it underpins everything else. Geometry/trig helpful; discrete math is “different but intuitive.”
  • Emphasis on building “mathematical maturity”: seeing through notation to underlying ideas, getting comfortable with proofs, quantifiers, sets, and symbol manipulation.
  • Recommended resources: Khan Academy, BetterExplained, MathAcademy, GeoGebra, discrete math textbooks (e.g., Rosen), and graph theory intros.
  • Mixed views on MathAcademy: praised for structure and repetition, but some learners feel concepts stay too fragmented.

AI, Copy-Paste Coding, and Fundamentals

  • The guide warns beginners against relying on AI or copy-paste; argues early effort must go into problem-solving, not shortcutting.
  • Some compare avoiding AI to avoiding chess engines; others say the real issue is letting AI “play for you,” not using it as a tool.
  • Counterpoint: time is limited; knowing names of algorithms and using tools may be more realistic than hand-implementing basics forever.
  • Strong pushback from others that fundamental understanding (e.g., how computers work, data structures) is still essential.

Passion, Work, and the Job Market

  • The text’s “you gotta want it” message resonates with some; others call it naive, noting many people grind in jobs they don’t love for money.
  • Discussion on how software uniquely blurs hobby and profession, fueling both excellence and toxic overwork expectations.
  • Debate over how many programmers actually code outside work, and comparisons to other professions (law, consulting, medicine, trades) where unpaid or “hobby-like” effort also exists.
  • Concerns about a tough job market attributed more to oversupply and prior over-hiring than to AI directly.