Ciro Santilli @cirosantilli 37

A slow development test cycle will kill your software.

New developers won't want to learn your project, because they would rather shoot themselves.

This means that build time, and the time to run tests, must be short.

5 seconds to rebuild is the maximum upper limit.

Of course, at some point software gets large enough that things won't fit anymore in 5 seconds. But then you must have either some kind of build caching, or options to do partial builds/tests that will bring things down to that 5 second mark.

You also have to spend some time profiling execution and build from scratch times.

A slow build from scratch will mean that your continuous integration costs a lot, money that could be invested in a new developer!

It also means that people won't bother to reproduce bugs on given commits, or bisect stuff.

One anecdote comes to mind. Ciro Santilli was trying to debug something, and more experience colleague came over.

To reproduce a problem, ciro was running one command, wait 5 seconds, run a second command, wait 5 seconds, run a third command:

The first thing the colleague said: join those three commands into one:And so, Ciro was enlightened.

This is a general principle of software/hardware design that Ciro feels holds wide applicability.

The most extreme case of this is of course the integrated circuit itself, in which it is essentially impossible (?) to observe the specific value of some indidual wire at some point.

Somewhat on the other extreme, we have high level programming languages running on top of an operating system: at this point, you can just GDB step debug your program, print the value of any variable/memory location, and fully understand anything that you want. Provided that you manage to easily reach that point of interest.

And for anything in between we have various intermediate levels of complication. The most notable perhaps being developing the operating system itself. At this level, you can't so easily step debug (although techniques do exist). For early boot or bootloaders for example, you might want to use JTAG for example on real hardware.

In parallel to this, there is also another very important pair of closely linked tradeoffs:

Emulation also has another potential downside: unless you are very careful at implementing things correctly, your model might not be representative of the real thing. Also, there may be important tradeoffs between how much the model looks like the real thing, and how fast it runs. For example, QEMU's use of binary translation allows it to run orders of magnitude faster than gem5. However, you are unable to make any predictions about system performance with QEMU, since you are not modelling key elements like the cache or CPU pipeline.

Instrumentation is another technique that has can be considered to achieve greater observability.

This is the actual main function of university for many people as of the 2020s. And it fulfills it quite well. A breeding ground.

In a closely related sense, university is simply a symbol of personal status. Not a place where you go to learn. And especially in the Anglophone world of fancy colleges, university also doubles down as a form of long term luxury hotel. Even if it ends up meaning debt.

There's nothing wrong with sexual selection. This type of natural eugenics is an important part of humankind. It is however just sad that any type of learning falls so much behind. A close second would be fine. But as it stands, it is just too far off.

Related:

Ciro Santilli has felt that perhaps what is missing in 2020's AGI research is:

Forcing these boundaries to be tested was one of the main design goals of Ciro's 2D reinforcement learning games.

In those games, for example:Therefore, those continuous objects would also have "magic" effects that could not be explained by "simple" "what is touching what" ideas.

Bibliography:

Notable mentions:

Other notable people that are likely also awesome but Ciro has less familiarity with their contributions:

Another notable reference is in Lost Horse LLC, MacKenzie Bezos's charity instrument.

Breakdown:

Related:

Opens a virtual MIDI piano GUI. It just works on Ubuntu 20.04: askubuntu.com/questions/34391/virtual-midi-piano-keyboard-setup/1298026#1298026

VMPK is a virtual device that replicates what you would get by connecting a physical MIDI keyboard to your computer. It is not a software synthesizer on its own. But it does connect to a working synthesizer by default (Sonivox EAS) which makes it produce sounds out-of-the box.

TODO: then I messed with my sound settings, and then it stopped working by default on the default "MIDI Connection" > "MIDI Out Driver" > "Network". But it still works on "SonivoxEAS".

A hello world of actually connecting it to a specific software synthesizer manually on Advanced Linux Sound Architecture with aconnect can be found at: askubuntu.com/questions/34391/virtual-midi-piano-keyboard-setup/1298026#1298026

Save to a MIDI file: askubuntu.com/questions/709673/save-as-midi-when-playing-from-vmpk-qsynth/1298231#1298231

Reasonable default key mappings to keyboard covering 2 octaves.

3 multiple simultaneous keys did not work (tested "ZQI"). This might just be a limitation of my keyboard however.

TODO how to save to a .mid file? askubuntu.com/questions/709673/save-as-midi-when-playing-from-vmpk-qsynth

SourceForge.

Besides of course sexual selection, considering in this section only "formal learning" activities.

Consider e.g. the 2020 University of Oxford, where many many people are taking courses without any laboratory work (and also without much use at all) like literature and history, and they are paying about 9k pounds/year for it: how much it costs to study at the University of Oxford?.

Basically all of this could be done online from books.

Laboratories are impossible however, because expendables of every experiment you do cost from hundreds to thousands of dollars, not to mention crazy upfront equipment costs.

For this reason, the brick and mortar aspect universities should focus exclusively on laboratories, and ensuring that the students with the most relevant knowledge (which can be readily obtained online) get access to those laboratories. Students should of course fully master every aspect of theory pertinent to their experiments. principal investigators should hand pick whichever criteria they want to select their students, possibly based partly on exam as a service if they find it a useful metric.

Furthermore, the use of laboratories should put great focus on novel research. A lot of laboratory instruction could be done from video of an experiments. As much as possible, we should use laboratories for novel research. Related: Section "Videos of all key physics experiments".

The only one on GitHub. In RST and renders to HTML with image formulas.

Too "direct formula overload" at first look.

By the creator of SymPy, who works at Los Alamos National Laboratory and has a PhD in chemical physics: swww.linkedin.com/in/ondřej-čertík-064b355b/ Man, big kudos to this dude.

Haven't found the one yet:

Optional but really ideal:

The state of messaging is ridiculous as of 2020.

Many/most companies are unable to give any beauty to its employees.

Hiring is simply a process of "let's get this money making project working ASAP", bring people in, without considering Brooks's law.

And then when that happens, companies put people in extremely narrow knowledge areas, making them unable to see or participate in the bigger picture of things, unless they spend 10 years there and reach architect status.

This is perhaps particularly painful for high flying birds like Ciro Santilli.

Companies need a higher top to down force that attempts to actually teach the business and tech to every employee to counter the low level manager get things done now pressure.

Companies that are able to do that, will have many more employees with a sense of purpose, and with the ability to innovate. Those companies will win.

Great reports to how 2022-Ciro Santilli views how OurBigBook.com could go. Ciro can't help to feel that he is a mixture of both of the vision, tech and people guy. Not as extreme as any of them, but more like a well rounded (and less good individually) version of each. High flying bird vs gophers.

Reviews:

Ciro Santilli's joke version of the Chinese Four Treasures of the Study!In the past, Ciro used to use file managers, which would be the fourth tresure. But he stopped doing so for years due to his cd alias... so it became three. He actually had exactly three windows open when he was checking if there was anything else he could not open hand of.

Of course, this only made sense when Apple was more of an underdog to IBM, and Ciro Santilli greatly admires their defiance of the norm.

As of 2020 however, Apple is kind of on the top of the mobile world, and Think different simply makes no sense anymore, notably because it relies on closed source offline software used by millions.

it's Popular Now It Sucks comes to mind.

This is a trap every company that prides itself on it's "alternative culture" sets for itself. If they succeed, they could become the norm.

The type of laser described at: Video "How Lasers Work by Scientized (2017)", notably youtu.be/_JOchLyNO_w?t=581. Mentioned at: youtu.be/_JOchLyNO_w?t=759 That point also mentions that 4-level lasers also exist and are more efficient. TODO dominance? Alternatives?

Bibliography:

The time-independent Schrödinger equation is a variant of the Schrödinger equation defined as:

So we see that for any Schrödinger equation, which is fully defined by the Hamiltonian $\hat{H}$, there is a corresponding time-independent Schrödinger equation, which is also uniquely defined by the same Hamiltonian.

The cool thing about the Time-independent Schrödinger equation is that we can always reduce solving the full Schrödinger equation to solving this slightly simpler time-independent version, as described at: Section "Solving the Schrodinger equation with the time-independent Schrödinger equation".

Because this method is fully general, and it simplifies the initial time-dependent problem to a time independent one, it is the approach that we will always take when solving the Schrodinger equation, see e.g. quantum harmonic oscillator.

Some key points that are a bit hard to grasp, at least in some versions:

The premise that "we can't make AGI, but we know enough about the human brain to upload on to a computer" is flawed. Edit: after reading Superintelligence by Nick Bostrom (2014), Ciro Santilli was convinced otherwise. What is flawed is of course just the "extracting connectome with macroscopic probes part". A post mortem connectome extraction with microtome is much more believable. But of course they weren't going to show fake slices of Jonny Depp's brain, are they? Famous actor bodies are sacred! What a huge lost opportunity. On the other hand however, the scale of the first connectome extraction would be arguably too huge to be undertaken by a random pair of rogue researchers. The same would also likely apply to any first time human brain connectome. It would much more likely be a huge public effort, much like the Human Genome Project.

But this film does have the merit of exploring how an AGI might act to take over the AGI might act to take over the world once created, notably by creating its own physical research laboratory. Though it doesn't feel likely that it could go under the radar for 2 years given the energetic requirements of the research. Even the terrorists find it before the FBI!

I also wish they had shown the dildo (or more likely, direct stimulation!) computerized Jonny Depp used to use with his wife before he managed to re-synthesized his body. But you know, 18+ would cut too much profits. Ah, what a shame.