Most important superconductor material Updated 2025-07-16
As of 2023 the most important ones economicaly were:The main application is magnetic resonance imaging. Both of these are have to be Liquid helium, i.e. they are not "high-temperature superconductor" which is a pain. One big strength they have is that they are metallic, and therefore can made into wires, which is crucial to be able to make electromagnetic coils out of them.
- Nb-Ti: the most widely used one. Used e.g. to create the magnetic fields of the Large Hadron Collider Up to 15 T.
- Nb-Sn: more expensive than Nb-Ti, but can reach up to 30 T.
Monero Updated 2025-07-16
Cryptocurrency with focus on anonymity. Was almost certainly the leading privacy coin since its inception until as of writing in the 2020s.
Ciro Santilli has received and held considerable quantities of Monero, notably 1000 Monero donation. so bias alert.
As mentioned at Section "Are cryptocurrencies useful?", Ciro Santilli believes that anonymity is the most valuable feature that really matters on crypto coins, and therefore if he were to invest in crypto, he would invest in Monero or some other privacy coin.
localmonero.co/knowledge/monero-stealth-addresses?language=en gives an overview of the privacy mechanisms:
- ring signatures, which hide the true output (sender)localmonero.co/knowledge/ring-signatures Gives an overview. Mentions that it is prone to heuristic attacks.Uses a system of decoys, that adds 10 fake possible previous outputs as inputs, in addition to the actual input.So the network only knows/verifies that one of those 11 previous outputs was used, but it does not know which one.TODO so how do you know which previous outputs were spent or not?
- RingCT which hides the amounts.
- stealth addresses, which hides who you send toThis forces receivers to scan try and unlock every single transaction in the chain to see if it is theirs or not.The sender therefore can know when the money is spent, but once again, not to whom it is being sent.
Based on en.wikipedia.org/wiki/CryptoNote and like Satoshi Nakamoto created by under the pseudonym "Nicolas van Saberhagen" www.reddit.com/r/Monero/comments/7v2obe/offering_a_bounty_for_a_video_of_the_speech_by/
Coinbase has actually stayed away from trading it even as of 2019 when Monero was the third largest market capitalization crypto because of fear of regulatory slashback: decrypt.co/36731/heres-why-coinbase-still-hasnt-listed-monero. Although it must be said, the value of privacy crypto is greatly reduced when everyone is trading it on exchanges, which require a passport upload to work.
Monaco (editor) Updated 2025-07-16
Mole (unit) Updated 2025-07-16
Mole (espionage) Updated 2025-07-16
Molecular machine Updated 2025-07-16
The most beautiful ones:see also Section "Animations of molecular biology processes"
- sodium channel
- basically anything that falls in the DNA replication and transcription
Molecular biology technique Updated 2025-07-16
Molecular biology feels like systems programming Updated 2025-07-16
Whenever Ciro Santilli learns about molecular biology, he can't help but to feel that it feels like programming, and notably systems programming and computer hardware design.
In some sense, the comparison is obvious: DNA is clearly a programmable medium like any assembly language, but still, systems programming did give Ciro some further feelings.
- The most important analogy perhaps is observability, or more precisely the lack of it. For the computer, this is described at: The lower level you go into a computer, the harder it is to observe things.And then, when Ciro started learning a bit about biology techniques, he started to feel the exact same thing.For example when he played with E. Coli Whole Cell Model by Covert Lab, the main thing Ciro felt was: it is going to be hard to verify any of this data, because it is hard/impossible to know the concentration of each element in a cell as a function of time.More generally of course, this is exactly why making any biology discovery is so hard: we can't easily see what's going on inside the cell, and have to resort to indirect ways of doing so..This exact idea was highlighted by I should have loved biology by James Somers:
For a computer scientist, a biologist's methods can seem insane; the trouble comes from the fact that cells are too small, too numerous, too complex to analyze the way a programmer would, say in a step-by-step debugger.
And then just like in software, some of the methods biologists use to overcome the lack of visibility have direct software analogues:- add instrumentation to cells, e.g. GFP tagging comes to mind
- emulation, e.g. E. Coli Whole Cell Model by Covert Lab
- The boot process is another one. E.g. in x86 the way that you start in 16-bit mode, largely compatible into the 70's, then move to 32-bit and finally 64, does feel a lot the way a earlier stages of embryo development looks more and more like more ancient animals.
Ciro likes to think that maybe that is why a hardcore systems programmer like Bert Hubert got into molecular biology.
Some other people who mention similar things:
- I should have loved biology by James Somers highlights the computer abstraction layer analogy between the two:
Modus ponens Updated 2025-07-16
Modulo operation Updated 2025-07-16
Modulation Updated 2025-07-16
But now people want to send voice. How to do it?
It would not be practical without modulation: Why can't you send voice without modulation?
Modular multiplicative inverse Updated 2025-07-16
Modular arithmetic Updated 2025-07-16
Modern work is evil Updated 2025-07-16
- Dilbert
- Severance 2022
- tvtropes.org/pmwiki/pmwiki.php/Main/SoulCrushingDeskJob
- en.wikipedia.org/wiki/Bullshit_Jobs
- Falling Down 1993
Modem router Updated 2025-07-16
Mobile app Updated 2025-07-16
MNIST database Updated 2025-07-16
70,000 28x28 grayscale (1 byte per pixel) images of hand-written digits 0-9, i.e. 10 categories. 60k are considered training data, 10k are considered for test data.
This is THE "OG" computer vision dataset.
Playing with it is the de-facto computer vision hello world.
It was on this dataset that Yann LeCun made great progress with the LeNet model. Running LeNet on MNIST has to be the most classic computer vision thing ever. See e.g. activatedgeek/LeNet-5 for a minimal and modern PyTorch educational implementation.
But it is important to note that as of the 2010's, the benchmark had become too easy for many applications. It is perhaps fair to say that the next big dataset revolution of the same importance was with ImageNet.
The dataset could be downloaded from yann.lecun.com/exdb/mnist/ but as of March 2025 it was down and seems to have broken from time to time randomly, so Wayback Machine to the rescue:but doing so is kind of pointless as both files use some crazy single-file custom binary format to store all images and labels. OMG!
wget \
https://web.archive.org/web/20120828222752/http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz \
https://web.archive.org/web/20120828182504/http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz \
https://web.archive.org/web/20240323235739/http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz \
https://web.archive.org/web/20240328174015/http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
OK-ish data explorer: knowyourdata-tfds.withgoogle.com/#tab=STATS&dataset=mnist
M. mycoides strain Updated 2025-07-16
MMORPG Updated 2025-07-16
MLperf Updated 2025-07-16
mlcommons.org/en/ Their homepage is not amazingly organized, but it does the job.
Benchmark focused on deep learning. It has two parts:Furthermore, a specific network model is specified for each benchmark in the closed category: so it goes beyond just specifying the dataset.
Results can be seen e.g. at:
Those URLs broke as of 2025 of course, now you have to click on their Tableau down to the 2.1 round and there's no fixed URL for it:
And there are also separate repositories for each:
There are unlisted articles, also show them or only show them.