How These Things Work – A book about CS from first principles (2016) (opens in new tab)

In a similar vein, I highly recommend the MOOC "nand2tetris" which progresses smoothly from simple logic circuits right up to a high level Java-like programming language.

I did a EE CS double major a LONG time ago. Senior year included a real summonavitch VLSI course, where in Q1 we had to design a 16- or 32- bit CPU. Over Thanksgiving break, TI manufactured them for us. Q2, we built a computer. Q3, we made it bootable (OS) and C compiler. I was interviewing for jobs in Japan (school was in Terre Haute, so travel overhead!) and I thought I was going to die. But it was the best thing that ever happened to me...

By "fabricate" do you mean that your design was produced in silicon? If so, that's super cool!

As I posted the last time this book came up https://news.ycombinator.com/item?id=22294655 that book would be more accurately called The Misinformation. If you follow the instructions in it you will die. Better alternatives are listed in my linked comment.

Oh, puh-leeze! THE book on rebuilding civilization is How to Invent Everything: A Survival Guide for the Stranded Time-Traveler[1] by Ryan North.

[1] https://www.amazon.com/How-Invent-Everything-Survival-Strand...

I've been looking for a book like that lol.

Agreed. I read through this on a recent vacation and even though i have a computer engineering degree, it was nice to get through the basics again and they have some fun exercises.

Would you say that this book is still relevant, now that it is 15 years old?

The amazing thing about Code is how it traces the connection of formal logic (in the Aristotelian sense) to the, as you say, pre-computer code of braille and even flag signals to form the foundations of modern computing.

I am a self-taught developer and probably had 10 years experience in web development when I first read Code. I would have these little moments of revelation where my mind would get ahead of the narrative of the text because I was working backwards from my higher level understanding to Petzolds lower level descriptions. I think of this book fairly often when reading technical documentation or articles.

I recently listened to Jim Keller relate engineering and design to following recipes in cooking [1]. Most people just execute stacks of recipes in their day-to-day life and they can be very good at that and the results of what they make can be very good. But to be an expert at cooking you need to achieve a deeper understanding of what is food and how food works (say, on a physics or thermodynamic level). I am very much a programming recipe executor but reading Code I got to touch some elements of expertise, which was rewarding.

https://youtu.be/Nb2tebYAaOA?t=1351

Code's good but it doesn't cover Kleisli categories and Kleisli composition, Peano arithmetic, parametric polymorphism, sum types, pattern matching, or any of numerous other things covered in Maguire's How These Things Work. So it's not accurate to say Code is "far more comprehensive"; Code mentions FORTRAN, ALGOL, COBOL, PL/1, and BASIC, but the example programs it contains are written in assembly, ALGOL, and BASIC. It doesn't contain any programs you can actually run except for an three-line BASIC program and some even simpler assembly programs.

I'm reading Code right now and it's fantastic. I'm a bit more than 1/2 the way through and so far it's only been about how computers work and not really about computer science.

I heard an expression this weekend that I think is apt - a computer is to computer science as a telescope is to astronomy.

Code is what got me as a teenager interested in tech. It is an awesome book.

This is part of what allowed me to get into programming. The "no prior" knowledge part is absolutely true.

I did start getting lost around the second half of the book.

Somewhat of a tangent but related, there is an anime called Dr.Stone where a brilliant genius scientist kid gets ported 3700 years in the future and people have reverted back to the stone ages. He teaches them how to build everything from scratch and makes some crazy stuff i.e. antibiotics, etc. Highly recommend

The questions usually lack a starting point. It's one thing to be stuck I medieval time, but stone age is another thing entirely.

I share your skepticism. Seems to me the engineer is falling prey to the Dunning-Kruger effect[1]. Rather than knowing enough to be able to puzzle together all the pieces they don’t know, I’d wager they don’t know enough to be able to discern what they won’t be able to figure out.

[1]: https://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect

The author states that computer science has little to do with computers on the first page so I would not expect circuitry.

First-principles is a physics/math way of thinking, and is common parlance in the mathematical modeling world.

When we say a model is a first-principles model, it means it is derived through fundamental equations like conservation of mass/energy, and other known relationships. This is in contrast to a data-driven model, where the underlying phenomena are not explicitly modeled -- instead the model is created by fitting to data.

Elon Musk became associated with it because he applied this form of thinking to business problems, i.e. by establishing the "fundamental equations" (as it were), questioning some basic assumptions and coming up with conclusions that are necessarily true but that no one else has arrived at.

Data-driven models (or the human equivalent: reasoning by analogy) are convenient to build and work well in the space the data has been collected in (~interpolation). However, they do not extrapolate well -- you cannot be sure they will work outside of the space of training data that the model has seen.

First-principles models (or the human equivalent: reasoning by principles) are generally more difficult to build and test (I worked on first-principles physics models for a decade -- they are a pain), but because they are built on a structure of chained principles/truths, they often extrapolate well even to areas where data has not been collected.

This is why if you want to improve efficiency and operations in known spaces, you use data-driven models (fast to build and deploy, accurately captures known behavior).

But for doing design and discovery (doing new things that have never been done before), first-principles models/thinking will carry you much farther.

That's the Baader-Meinhof phenomenon talking [1] :)

[1] https://science.howstuffworks.com/life/inside-the-mind/human...

It's a relatively common idiom here in Australia. It gets used quite a bit in STEM education, i.e. "prove such-and-such from first principles", but is also pretty common more colloquially.

According to your link, it is also used a bit in South Africa (where Elon grew up), but less common in the US. Rather than being a new and overhyped term, perhaps it is a case of Elon using a term that is quite everyday to him, without realizing it is less familiar to the audience.

FWIW, the latin form "ab initio" is not uncommon either (in Europe at least).

https://en.wikipedia.org/wiki/Ab_initio

It often occurs that technical jargon is swiped to be used as business speak by business leaders with technical backgrounds. For example I often hear “orthogonal” when “independent” is usually more appropriate.

Aristotle used the term, and it’s probably older than him.

It's all over the place in physics and chemistry, and as a consequence on the engineering areas that are based on those (AKA, nearly all of them).

It is rarer to see it in CS, but it's more because CS used to deal with very simple theories up to recently than because of some fashion. As CS theories start to construct up from the earlier ones, it's appearing more.

UK here - I remember it back in college thirty+ years ago as in "build X from first principles", which could mean (e.g.) implement an algorithm without out using library calls.

Possibly the Baader-Meinhof Phenomenon

It's a favorite term used by people that identify as 'thought leaders'