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As is tradition, I'll plug the latest episode of What's Going On With Shipping:

https://youtu.be/QCyB-Ym0ryk?t=947

(the timestamp links to the "May 2025 Estimate" chapter)


As someone who knows four languages[1] (picked every single one up during childhood) and is currently learning Sanskrit, I have to say that Krashen's input hypothesis and Orberg's Lingva Latina is probably the way to go if you are learning languages as an adult.

The direct teaching method works but is time-consuming and generally used for languages that lead to an occupation, viz. English. The grammar translation method is a waste of time. It might satisfy your intellectual curiosity about the structure of the language but you won't be able to make yourself understood after a lifetime of study. I wonder at the sheer lunacy of dumping thousands of random sentences into your lap and translating it from one language to another.

After a year and a half of false starts, I started reading a couple of Sanskrit stories every day. Because the context is maintained across the story, your brain starts recognizing patterns in sentences. You keep reading sentences like

sarvē janāḥ kāryaṁ kurvanti

sarvē janāḥ gacchanti

sarvē janāḥ namanti

and you automatically associate sarvē (all) with janāḥ (people) without needing to know the declension of those words. This applies to the cases as well.

To be able to converse about or understand a wide variety of topics, you will eventually have to move beyond stories due to restrictions on the tense/aspect/moods you encounter as a result of the nature of the material. But that is doable.

[1] Much of India is bilingual. A substantial minority might know four or more languages due to the many mother and father tongues and heavy internal migration across the states (whose boundaries were drawn on linguistic lines post-independence)


Warning, always convert your colors to from sRGB to Linear RGB before doing any math on them, then convert them back to sRGB afterwards for displaying them.

sRGB is the familiar color space you all know and love, it's what your display uses, and it's what has those RGB numbers between 0 and 255. But it's not a linear color space.

First think of values as being between 0 and 1 instead of 0 and 255. To change sRGB to Linear, do X^2.2 (close to squaring the value). To change Linear back to sRGB, do X^(1/2.2) (close to a square root).

In Linear RGB, a value of 0.5 is halfway between black and white. You can put a stripe or checkerboard pattern next to the color value, and it will appear to be the same brightness. But in sRGB, a value of 0.5 is much darker. Linear RGB of 0.5 is roughly equivalent to sRGB of 0.73.

The actual method of conversion involves a complicated curve that isn't continuous, using X^2.2 is still an approximation.


Key to Dreamer’s success, says Hafner, is that it builds a model of its surroundings and uses this ‘world model’ to ‘imagine’ future scenarios and guide decision-making.

Can you look at the world model, like you can look at Waymo's world model? Or is it hidden inside weights?

Machine learning with world models is very interesting, and the people doing it don't seem to say much about what the models look like. The Google manipulation work talks endlessly about the natural language user interface, but when they get to motion planning, they don't say much.


It would be great if there were more hobby OS resources targetting something other than X86. It is common and everything, but all the bootstrapping and device cruft involved isn't something you should need to get in your head, and 386 assembly by itself is terribly frustrating and obtuse to write by hand. I think most people might be better off starting out with a RISC microcontroller. There isn't nearly as much bullshit involved between getting a running kernel and talking some simple serial, and you can get an instruction set reference, assembler manual, and processor datasheet that will tell you everything you need to know. And it's easy to have fun hardware hacking that way too, making your own peripherals and such.

This is reminding me of an ascii art game called Candy Box. It begins very minimal like this, but unfolds into a wonderfully imaginative RPG game.

https://candybox2.github.io/candybox/


Will add a personal anecdote on my mental-health journey and the impact psylocibin has had on my life.

Bio:

- Late 30s.

- Long history of depression my entire life. "Melancholic" child. Bad drunk teenager. Suicidal in college (failed attempt).

- No drugs except alcohol until I was in my mid 20s.

I've been prone to major bouts of depression my entire life. I went to therapy multiple times a week for years and got on SSRI's towards the end of university as a response to a failed (but serious) suicide attempt.

SSRI's never did anything for me except make me feel like shit (and not be able to take one). Eventually I went off them and sort of got by, and I managed to stay safe by drinking no alcohol. Therapy twice a week was an utter waste of time and money.

Then, sort of on a whim, I grew some mushrooms at home with my then fiancée and we took them together. I was mid 20s and had no prior experience with any drug but alcohol. Not knowing what I was doing, we took a BIG dose. I had a trip that was fun at first and then became quite unenjoyable.

For the next twelve months I felt like myself again. The change was subtle but, over a long term, quite obvious.

After about 18 to 24 months, my depression came back. We took mushrooms again and the same thing happened. A year of well being in exhcange for 2 fun hours and 6 tough ones.

So about every two years I'll take a big (2-4g dry) dose of mushrooms and...it's like magic. I feel like myself again. I'm "back." Life is not happy, none of my problems go away, but I feel like I'm an agent in my own life as opposed to a spectator.

The well being lasts about a year or 18 months (less if I've been drinking alcohol). It's almost never as bad as when I was suicidal, but it still sucks. For me depression is like being a professional chef and one day your taste buds make everything taste like ash. Or a painter and one day you see colors less and less.

Last year I went into a VERY deep depression, so deep that I refused to take mushrooms until my wife basically forced me to. Same thing. The very next day I felt like "I was back."

Those things changed my life.

I've since had fun with other drugs maybe 5 times. Acid a couple times, molly a couple times. Cheap (wtf) fun for a half a day, but nothing like the impact mushrooms have on my mind.

I've had one bad trip while taking mushrooms recreationally. I don't understand who would take those things for fun, at night.

Strictly during the day, well-rested, with loved ones, and in nature!

I'm also convinced that the impact they have on me is purely chemical. It has nothing to do with "facing my demons" or "connecting with a higher spirit" or anything like that. I just get off my stupid rut.

"Neurons that fire together wire together" as they say, and when I'm depressed it's the stupid neurons that fire together. Mushrooms makes a whole different set fire, and fire hard, and that seems to be enough.

The deepeest lesson I've gotten while on shrooms is:

"I'm trying my best. Everything is actually fine."

Pretty good lesson.


Currently, what's the best way to make gaussian splat scenes from video?

FYI

> Standard Ebooks is a volunteer-driven effort to produce a collection of high quality, carefully formatted, accessible, open source, and free public domain ebooks that meet or exceed the quality of commercially produced ebooks.

I'm looking at doing that for calculus made easy by sylvanus Thompson but I need to overcome my lazyness first


During the last years, I have switched to baking the bread in a microwave oven.

It is much faster and more reproducible than in a traditional oven and this allows me to bake a bread every morning, for my breakfast.

The baking time should be determined experimentally. In my microwave oven, the dough made of 500 g wheat flour is baked well in a glass vessel covered with a glass lid in a time of 14 minutes at 1000 W. One minute less or one minute more make little difference. The baking vessel must be large, to allow for expansion.

A basic bread can be made by putting 500 g wheat flour in a hemispherical glass bowl of suitable size (e.g. about 20 cm/8 inches in diameter), adding instant dry yeast (here sold in 7 g bags suited for 500 g flour) to the flour, mixing the yeast with the flour, adding water about 75% of the flour weight, and then kneading the dough.

For this amount, kneading needs about 6 to 7 minutes. I knead with one hand, keeping the bowl in the other hand. In this way, one hand remains clean, free of sticky dough. You need to keep around a pie spatula (or a spoon, if you do not have a pie server), to be able to remove the sticky dough from your kneading hand, when you finish. The kneading should stop when the dough becomes homogeneous, elastic and cohesive. After you do it a few times, it becomes easy to recognize when it is ready.

Then you can transfer the dough from the bowl into the baking vessel and leave it to rest and grow. For maximum growing, you may let the dough rest for an hour, but if in a hurry you can bake it earlier without much difference in the final product.

For the best growing, the dough should stay in a warm place. While I knead the dough, I boil water for tea in the microwave oven. Then, when the dough is ready, I take out the glass teapot and I put the baking vessel on the now warm plate of the microwave oven. I close the oven and one hour later I press the button to start the baking.

It is also possible to make unleavened bread (by not adding yeast), which still grows in a microwave oven much more than traditional unleavened bread. Its main advantage is that it can be ready faster. To make the bread fast, one can also use baking powder instead of yeast, which allows skipping over the resting time. The bread made with baking powder grows well, but it has a more compact and much finer structure than the bread made with yeast.

To the basic bread, there are a myriad possible additions. Most people want salted bread, so salt should be added to the flour, together with the yeast. You can add various spices, seeds or ground seeds. Some spices are best combined with sugar, but this should not be done everyday. With the exception of salt, I prefer to not add anything else uniformly but to lay flat and thin the dough, deposit on it layers of spices, seeds, sugar etc., then roll the dough to incorporate the additions and form it in the baking vessel. In this way, the bread will have alternate layers of plain bread with spices or seeds, so that a smaller quantity of those will produce a taste as intense as a greater quantity that would have been dispersed uniformly in the bread.


Another recent cool work in this field is this paper : https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/

They manage to get the same quality, with <1hr of training, and running at 60fps 1080p, it uses point cloud instead of volumetric representation


Chess belongs to a class of games that are particularly interesting to study: it is a finite extensive form game which basically means the game can be modeled as a tree and requires only shared knowledge among participants, all the participants have complete shared knowledge, it’s sequential, has perfect recall (nothing prevents looking backwards at the game state), and a simple payoff function (you win if you checkmate the opponent, in which case the opponent also loses) that is zero-sum.

This makes it so chess has a one-shot sub game perfect equilibrium: https://en.m.wikipedia.org/wiki/Subgame_perfect_equilibrium. But, the dimensionality of the possible end states is very high, because there are many different configurations leading to a checkmate, so you can’t easily perform backward induction. But you can perform minimax (which is how Stockfish works): https://en.m.wikipedia.org/wiki/Minimax

From a game theory perspective chess is really not at all like an actual war, because the properties of the game are different in almost every conceivable way - not just because of perfect information. But, you can model more complex “games” like war in a way that makes them more similar to chess if you wish. Just be careful because ignoring very fundamental differences like the payoff model could greatly affect decision making - for war, you’d probably rather surrender or make minor concessions in most cases than win at the cost of 99% of your people and infrastructure getting destroyed.


Well, maybe if you wrote it like this:

mandatory_leading_letters = "^\w+"

optional_suffix = "([-+.']\w+)*"

domain = "\w+"

domain_suffix = "([-.]\w+)*"

tld = "\.\w+([-.]\w+)*$"

regex = "{mandatory_leading_letters}{optional_suffix}@{domain}{domain_suffix}{tld}"

Then you could understand it? Seems to me the trouble isn't with regex but with the decision to write a regex without trying to make it understandable. It is also possible to minify your onto one line in some languages or otherwise obfuscate it; should we then condemn it?

If the author had to fix a multi-line regex, I hope that in the process, they understood it enough to break it up into pieces so that the next time it would be more possible to debug.

Regex are pretty neat. They are often not the solution. I would not want to use lookaround for example. But also, they are really useful in a lot of cases, and I would hate to not use them because they're capable of being obfuscated.


>"start a turn-key Amazon business"

From the very limited research I've done, it is something relatively similar to this. There's a market in China of selling e-books which teach you various ways to make money on the English-speaking web without having to know much English yourself.

I mean it makes sense, if you go to BlackHatWorld or HackForums there are loads of people selling guides teaching you to do similar stuff, they're just in English. I imagine that given China's position in the marketplace, it's probably fairly lucrative for an individual or small company to make nonsense brands and sell stamped tech-junk for 10x markups to Americans.


To me the other algorithms described in the list are more novel and interesting:

https://madebyevan.com/algos/crdt-tree-based-indexing/ - for when precise order is critical, like paragraphs in a document. This algorithm is almost like storing adjacency information like a linked list, but is more convergent. Very interesting for [my use-case](https://www.notion.so/blog/data-model-behind-notion).

https://madebyevan.com/algos/crdt-mutable-tree-hierarchy/ - for tree-shaped data, like blocks in a Notion page that should have exactly one parent, but allow concurrent re-parenting operations

https://madebyevan.com/algos/log-spaced-snapshots/ - log space snapshots, for choosing what fidelity of historical information to store. For context, many CRDTs for rich text or sequences store unbounded history so that any edit made at any time can be merged into the sequence. For long-lived documents, this could be impractical to sync to all clients or keep in "hot" memory. Instead, we can decide to compact historical data and move it to cold storage, imposing a time boundary on what writes the system can accept on the hot path. The log-spaced snapshots algorithm here could be used to decide what should be kept "hot", and how to tune the cold storage.


The Z Library is still alive, as a TOR Hidden Service: http://zlibrary24tuxziyiyfr7zd46ytefdqbqd2axkmxm4o5374ptpc52...

> Rust's enums and exhaustive match statement are just amazing

ADTs + pattern matching are the killer feature set that makes the more popular functional languages so damn pleasant to use, and they're starting to spread to more and more languages. I suspect that, 50 years from now, we'll look back and see them as the key paradigm shift of this era.


Note that, to get an idea of the contents, you can see the previously published fascicles, and earlier drafts online (pre-fascicles):

• Mathematical Preliminaries Redux: https://cs.stanford.edu/~knuth/fasc5a.ps.gz

• 7.2.2 Introduction to Backtracking: https://cs.stanford.edu/~knuth/fasc5b.ps.gz

• 7.2.2.1 Dancing Links: https://cs.stanford.edu/~knuth/fasc5c.ps.gz

• 7.2.2.2 Satisfiability: https://cs.stanford.edu/~knuth/fasc6a.ps.gz

The first three were published together as "Volume 4, Fascicle 5" in 2019, and the last one—Satisfiability—was published as "Volume 4, Fascicle 6" in 2015. Of course the actual publication as Volume 4B has hundreds of fixes and additions beyond what was published earlier, and a lovely preface that you can read here: https://www.informit.com/articles/article.aspx?p=3143614

> You might think that a 700-page book has probably been padded with peripheral material. But I constantly had to "cut, cut, cut" while writing it, because […] new and potentially interesting-yet-unexplored topics kept popping up, more than enough to fill a lifetime; yet I knew that I must move on. […] I wrote nearly a thousand computer programs while preparing this material, because I find that I don't understand things unless I try to program them.


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