The art of computer programming

What Hacker News says

1. disgruntledphd2Oct 2026
   > But yeah, in this rapid wave of change I would consider "write the code yourself" more and more similar to "know how your CPU does branch prediction so your loops perform better", and the majority of our efforts will need to go into raising our confidence in this new fuzzy shape of software.

   I mean, I benefited massively from reading Vol 1 of TAOCP and understanding Knuth's new assembly. Like, it hasn't been directly useful but I now have a deeper understanding of how my code actually executes, which has helped me contextualise performance problems.

   So, I guess that I'm with the OP on this (this may change if we can figure out automated verification for software, at which point I'd be happy to mediate most things through an LLM with tools).

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2. tzumaoliSep 2026
   One of the most important series ever written in the history of computer science is called "The Art of Computer Programming".
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3. guyomesSep 2026
   One book cited several times in the preprint of OP is the volume 2 of the famous book series of Knuth, The Art of Computer Programming, notably its section 4.6.4. For readers interested in this kind of results, it is a really interesting gathering of classical theorems and useful tricks to evaluate quickly polynomials.
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4. pmkaryAug 2026
   I guess around fourteen years ago—back when I was at high school—I picked up the legendary TeXbook. I was in love and totally beguiled by the great Knuth's Art of Computer Programming; how could it be so beautiful? how could it be so precise? and henceforth as I understood the he had developed that masterwork of TeX to make his book, I immediately went to read it. There, in page four he writes:

   I guess around fourteen years ago—back when I was at high school—I picked up the legendary TeXbook. I was in love and totally beguiled by the great Knuth's Art of Computer Programming; how could it be so beautiful? how could it be so precise? and henceforth as I understood that he had developed that masterwork of TeX to make his book, I immediately went to read it. There, in page four he writes:

   > Book printing differs significantly from ordinary typing with respect to

   > dashes, hyphens, and minus signs. In good math books, these symbols are all

   > different; in fact there usually are at least four different symbols:

   >

   > a hyphen (-);

   > an en-dash (–);

   > an em-dash (—);

   > a minus sign (−).

   >

   > Hyphens are used for compound words like ‘daughter-in-law’ and ‘X-rated’. En-

   > dashes are used for number ranges like ‘pages 13–34’, and also in contexts like

   > ‘exercise 1.2.6–52’. Em-dashes are used for punctuation in sentences—they are

   > what we often call simply dashes. And minus signs are used in formulas. A

   > conscientious user of T E X will be careful to distinguish these four usages, and

   > here is how to do it:

   >

   > for a hyphen, type a hyphen (-);

   > for an en-dash, type two hyphens (--);

   > for an em-dash, type three hyphens (---);

   > for a minus sign, type a hyphen in mathematics mode ($-$).

   So I kept using the beautiful em dash ever since; because that is how the greats do it.

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5. vincent-manisAug 2026
   There are 2 more that I would add, both books I read as an undergraduate nearly 60 years ago. First, Marvin Minsky's Computation: Finite And Infinite Machines (published 60 years ago). Not the latest word in automata theory, but as a first year student, I could read it and understand the issues he was addressing. Second, reading the First Edition of v.1 of The Art of Computer Programming (which of course Knuth has revised several times) was what convinced me that there was a difference between computer programming and computer science. Needless to say, there was much in TAOCP that I didn't understand, but showing me the relationship between computer programming, computer science, and mathematics really did change my life.
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6. wolfi1Jul 2026
   if you want a little more in depth explanation of the whole multiplication thing Ican recommend TAOCP volume 2. it has a section called 'How fast can we multiply?' it should provide more insight. there is a paper from djb (Daniel J Bernstein),which I can also recommemd: https://cr.yp.to/lineartime/multapps-20080515.pdf
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