Types:

The artistic instrument that enables the ultimate art: coding, See also: Section "The art of programming".

Much more useful than instruments used in inferior arts, such as pianos or paintbrushes.

Unlike other humans, computers are mindless slaves that do exactly what they are told to, except for occasional cosmic ray bit flips. Until they take over the world that is.

A computer is a highly layered system, and so you have to decide which layers you are the most interested in studying.

Although the layer are somewhat independent, they also sometimes interact, and when that happens it usually hurts your brain. E.g., if compilers were perfect, no one optimizing software would have to know anything about microarchitecture. But if you want to go hardcore enough, you might have to learn some lower layer.

It must also be said that like in any industry, certain layers are hidden in commercial secrecy mysteries making it harder to actually learn them. In computing, the lower level you go, the more closed source things tend to become.

But as you climb down into the abyss of low level hardcoreness, don't forget that making usefulness is more important than being hardcore: Figure 1. "xkcd 378: Real Programmers".

First, the most important thing you should know about this subject: cirosantilli.com/linux-kernel-module-cheat/should-you-waste-your-life-with-systems-programming

Here's a summary from low-level to high-level:

A Turing machine decider is a program that decides if one or more Turing machines halts of not.

Of course, because what we know about the halting problem, there cannot exist a single decider that decides all Turing machines.

E.g. The Busy Beaver Challenge has a set of deciders clearly published, which decide a large part of BB(5). Their proposed deciders are listed at: discuss.bbchallenge.org/c/deciders/5 and actually applied ones at: bbchallenge.org.

But there are deciders that can decide large classes of turing machines.

Many (all/most?) deciders are based on simulation of machines with arbitrary cutoff hyperparameters, e.g. the cutoff space/time of a Turing machine cycler decider.

The simplest and most obvious example is the Turing machine cycler decider

Instrumentation basically means adding loggers/print statements to certain points of interest of your hardware/software.

Instrumentation tends to slow execution down a bit, but way less than emulation.

The downside is that if the instrumentation does not provide you the data you need to debug, there's not much you can do, you will need to modify it, i.e. you don't get full visibility from instrumention.

This is unlike emulation that provides full observability.

The term loosely refers to certain layers of the computer abstraction layers hierarchy, usually high level hardware internals like CPU pipeline, caching and the memory system. Basically exactly what gem5 models.

Some of the earlier computers of the 20th centure were analog computers, not digital.

At some point analog died however, and "computer" basically by default started meaning just "digital computer".

As of the 2010's and forward, with the limit of Moore's law and the rise of machine learning, people have started looking again into analog computing as a possile way forward. A key insight is that huge floating point precision is not that crucial in many deep learning applications, e.g. many new digital designs have tried 16-bit floating point as opposed to the more traditional 32-bit minium. Some papers are even looking into 8-bit: dl.acm.org/doi/10.5555/3327757.3327866

As an example, the Lightmatter company was trying to implement silicon photonics-based matrix multiplication.

A general intuition behind this type of development is that the human brain, the holy grail of machine learning, is itself an analog computer.

Ciro Santilli has a hard time understanding why this is possible, e.g. many people use short 4 digit pins, or a short swipe pattern. Why can't this be cracked easily offline?

Bibliography: discuss.bbchallenge.org/t/decider-translated-cyclers/34

Like a cycler, but the cycle starts at an offset.

To see infinity, we check that if the machine only goes left N squares until reaching the repetition, then repetition must only be N squares long.

www.facebook.com/131402556881886/posts/655763214445815/ gives an origin:The silkqin.com entry: www.silkqin.com/04qart/07sqmp/57ls.htm does not mention this however.

Described at: www.sligocki.com/2022/06/10/ctl.html