README.md

Computing

• mtdvio/every-programmer-should-know

  • A collection of (mostly) technical things every software developer should know about

• TODO: Consider The 2012 ACM Computing Classification System?

• [HCI]

• [Quantum Computing]

• [algorithms]

• [Data]

• [Graphics]

• [Cryptography]

• [Software Engineering]

  • [cloud-computing]

• [developer-tooling]

• [operating-systems]

• [history-of-computing]

• [theory]

  • [computerScience]
  • github.com/papers-we-love Papers from the computer science community to read and discuss.

• [ai]

• [blockchain]

• [programming]

• [networks]

• [psychology]

• [game]

• [web]

• [legal]

• [ehtics]

• [privacy]

• [hardware]

  • [logical-organization]

• [occupations]

• [books]

• What happens when...

  • This repository is an attempt to answer the age-old interview question "What happens when you type google.com into your browser's address box and press enter?"

  • [operating-systems] [networks]

• Other knowledge bases

  • Computer-Science-Resources
  • Everything I Know - Another knowlege base (that's awesome)
  • Programming, Math, Science
    • This is a list of links to different freely available learning resources about computer programming, math, and science.
    • [languages], [algorithms], [terminal], [graphics], [cryptography], [databases], [maths], [operating-systems]

• Computer Science Data Structure, Algorithm, Angular, React, etc.

  • Online snippets of loads of practical computing stuff

• The Elements of Computing Systems, Second Edition Building a Modern Computer from First Principles

  • [logical-organization]

• Harvard CS50

  • cs50/x/2023 Harvard - CS50’s Introduction to Computer Science - Open CourseWare

• MIT OpenCourseWare Computer Science #MITOpenCourseWare

• Stanford CS101 - Introduction to Computing Principles

  • IntroComputing.org
    • Free materials for teaching the essential qualities of computers using little snippets of code in the browser.
    • materials are based on Nick Parlante's Stanford CS101 course

• Structure and Interpretation of Computer Programs Harold Abelson and Gerald Jay Sussman with Julie Sussman

  • The material in this book has been the basis of MIT's entry-level computer science

• Computer Science from the Bottom Up

  • [operating-systems]
  • process
  • [[virtual-memory]]
  • [[linker]]

• build-your-own-x

  • Project to build advanced uses of computer science

• Project Based Learning

  • GitHub list of learning computing by creating projects
  • [web-apps], [interpereter], [networks], [opengl]
  • Project Based Tutorials in C

• @CompSciFact @CompSciFact

• Reading for graduate students

  • Reading to get to the next level after a degree

• 10 Papers Every Developer Should Read

  • Interesting set of papers for core concepts
    • Self looks very interesting

Unsorted

• Teach Yourself Programming in Ten Years, Why is everyone in such a rush?

  • Get Interested
  • Program (you need to do it)
  • Talk with other Programmers
  • Uni gives you creds
  • Projects with other programmers
  • Projects after other programmers
  • Half a dozen languages
  • understand computer hardware operations

• How can you not be romantic about programming?

  • Code, we say, can be: neat, nice, clean, crafted, baroque, minimal, solid, defensive, hacky, a hack, art, a piece of shit, the stupidest thing I’ve ever read, beautiful, like a poem.

  • Some lines of code are a riddle to anyone but their author and the name code serves as a warning. Other times, strangely, it’s a badge of honor.

• Tech Drawings

  • Funny web comic

• Ask HN: What bits of fundamental knowledge are productivity multipliers?

  • Regular expressions for simple text processing.
  • Parser combinators for parsing.
  • Parser generators (esp. packrat variety) for parsing.
  • The concept of fuzzing and property testing for testing code.
  • Calculus for solving all sorts of problems.
  • MCMC for solving a huge class of probability problems.
  • Search algorithms for solving a variety of problems (e.g. all NP-hard problems, sudoku, HTNs, scheduling, planning).
  • Gradient descent for solving a variety of optimization problems.
  • Vector Space embedding as a conceptual tool for a variety of complex AI problems.
  • Effect composition (Haskell's IO or Scala's ZIO) as an incredibly powerful paradigm for concurrency and parallelism.

• Software Engineering Body of Knowledge (SWEBOK)

  • The Guide to the Software Engineering Body of Knowledge (SWEBOK Guide) describes generally accepted knowledge about software engineering. Its 15 knowledge areas (KAs) summarize basic concepts and include a reference list pointing to more detailed information
  • SWEBOK Guide V3 Topics