photon_lines

It's a giant tulip bubble. Bitcoin was never supposed to be a store of value. It's a decentralized currency that many scam artists and criminals use to transfer wealth -- alongside with consuming massive amounts of energy, bitcoin does nothing but bring a huge net negative value to society: https://digiconomist.net/bitcoin-energy-consumption

But hey, let's keep this up and see how long it lasts :)

Knowing how easily even the smallest things torture me, I deliberately avoid contact with them. A cloud passing in front of the sun is enough to make me suffer. How then should I not suffer in the darkness of the endlessly overcast sky of my own life? With Balls running down that sandy beach and humping any hope for my irreverent and everlasting search for peace? My everlasting search for isolation?

I love this book!!! I also just finished writing a visual/geometric intro to Calculus and the derivative which you can find here: https://photonlines.substack.com/p/visualizing-the-derivativ...

For anyone interested in reading a bit more about Huh's story and some key lessons from it - I made a bit of a write up about him and his teacher which you can find here: https://photonlines.substack.com/p/lessons-from-june-huh-fro...

Thanks for the correction. I fixed the issue in the main article -- I think my brain automatically translated Terry -> Terrance. I should have paid more attention to the spelling that's for sure so apologies for any frustration!

If anyone doubts whether math is 'real' or not - maybe watching this clip of these 4 men might clarify it (pay attention to the language they use to discuss the architecture of the universe):

https://youtu.be/AmUI2qf9uyo?si=6B6qA249jZqCVV5j

Yes

Nice - thanks for the info! I had no idea about the Toy Story 2 fiasco as well so this was a great read :)

Cool. I wrote a subset of Prolog using only a few lines of Python and if anyone is interested, you can find it here: https://github.com/photonlines/Python-Prolog-Interpreter

There isn't much of a difference between injecting insulin (being diabetic) or getting your body to produce it. The main difference lies in having no automated mechanism for regulating blood sugar levels. Due to this - you have to calculate the correct amount of insulin you need for your meals and measure your blood sugar regularly to make sure it stays within balance. If your blood sugar levels tend to be higher than normal (i.e. you aren't injecting enough insulin), that isn't good. It is OK in the near term - but can be devastating in the long term due to health complications (i.e. blindness / nerve damage / etc..). If your blood sugars are fall too low - you can have severe consequences in the near term (i.e. die or have a seizure within 1 hour of injecting insulin). In other words - you need to always keep your blood sugar in check in order to survive and to be in good health. Some people find this extremely hard to do - some people like me embrace it.

Ohhh I see and my apology for the misunderstanding!! Either way - she's still young and she has plenty of time to figure it out. If not - one other recommendation I have is getting lab tests done every 3 to 6 months and going for regular check-ups with a doctor. If you find a good doctor - they can make a big difference in getting patients to notice bad HB1C measures and can talk with her about keeping them in check and why it's so important to keep blood sugar levels within normal range. Either way - I wish her a lot of luck and my offer still stands if she (or you yourself) need any help or advice :)

Have you tried putting your daughter on a different diet (low glycemic-index diet with more natural and keto-friendly foods)? This will make managing her diabetes a lot simpler and it'll make her feel better too.

I'm a type one diabetic and have been one for over 15 years now. Getting diagnosed is one of the best things that's ever happened to me. It got me to notice the huge impact diet and exercise have on the mind and body. I initially had issues like your daughter too but changing my eating habits and altering my life-style definitely had a profound effect and made it much easier to manage. If you need tips / help let me know and I'll do what I can. My number one tip is to stick to a more keto-based diet. It will make her blood sugar much easier to manage and has a lot of health benefits.

I totally agree. The UI and the usability on this website is down-right terrible.

Great idea! Thanks for the feedback!

I'm sorry but I'm not sure what you mean by mathematics not being 'natural.' Einstein, Feynman, and many others used mathematics to give us an amazing exposition into the universe. Feynman in fact has stated that a 'particle' takes all possible 'path' computations into account for every possible 'particle trajectory' that exists and that the path of least energy (i.e. least information exchange) takes precedence. If you're telling me this isn't mathematical then you honestly don't know much about mathematics.

Sorry but I disagree. Dirac disagrees here as well. We discovered the positron from mathematics not the other way around. Many other discoveries have been made due to the application of mathematics (check out AlphaGo and ChatGPT).

Well...sort of. The last part is my hypothesis. The first part isn't. Particles don't have a trajectory (location and momentum) within spacetime in the normal way we think of them as having. This has been known for a very long time :)

Not really. Everything in the human brain is actually an abstraction. The image or your 'vision' of what's 'out' there is just a computation done by the human brain. It's a 'holographic' 3-dimensional projection which only gives you enough information to survive evolved through many years of evolution. If you want to play that game, we shouldn't be having this discussion at all but we are having a discussion so it's not really coincidental. There is no mountain it's information all the way up and down.

Yes, and that's precisely my hypothesis :)

Here - I'll explain it to you (and what atoms and particles and molecules etc... are). They're human abstractions. Particles don't behave like particles because the programmer sat down and thought extremely hard how to create a universe with the best amount of variation and dynamics and complexity using the least amount of resources (i.e. compute power). Instead of modeling individual point particles and their trajectories at each time instance at each point in space - it programmed the universe instead in terms of particle trajectories and their dynamics (i.e. the universe only computes probabilities and probabilistic amplitudes for quantized events). Humans think of this behavior as being modelled as some sort of united 'field' but just like anything else this is also an abstraction. The dynamics and edges are there to make it possible to perform further information compression which plays a vital component in terms of the universe's evolution (i.e. the universe is an infinitely recursive function and the life forms within it are vital in enabling it to perform recursive compression state space transitions and to optimize its own information exchange dynamics).

Nice - thank you! Very informative post as well -- I love topology and this looks like a god-send!

Great video - thank you for this!!

I find that reading books rather than code tends to be more helpful in terms of finding good takes on what clean code is -- more specifically books on refactoring or specific language-related features (like 'Effective Java' or 'Fluent Python'). The issue with just reading code is that many times - you'll miss out on why the author chose to use the expression or abstractions which they chose to use. Reading a book at least takes you through author's thought process. For an alternative - you could always browse repositories which contain notes on refactoring as well like this one (which does a good job summarizing some of the key principles from Fowler's book on refactoring):

https://github.com/HugoMatilla/Refactoring-Summary

Google acquired the early android team and they were vital in making it succesful: https://www.androidauthority.com/google-android-acquisition-...

Chrome: hmm - was Chrome developed after Brin & Page left? I never realized this but OK you got me on that one if that indeed is the case.

Go: I liked it initially and I love the performance, but i don't necessarily love the language syntax and design.

The rest: mostly tools which make scaling Google's infrastructure easier. I don't count these as outstanding accomplishments but I suppose you may have had me here too (albeit the scope I was referring to mostly referred to regular ppl not infra teams but meh I suppose I'll give you this one as well).

I don't disagree with you and I agree with your take on understanding data structures and algorithms ... it is important, but making 80 to 90 percent of your recruitment process based on this and on playing games with quizzes and LeetCode is idiotic. There are lots of other factors that play a vitally important role in creating great software and most big companies do NOTHING to pay attention to these factors albeit I fully admit to this being hard to test for. That's why I offer every company I'm recruited into to do a paid (or free albeit open source) work sample test if they want to do so. Most don't take me up on this offer and I think its idiotic but it is what it is.

LeetCode has no correlation with great software development. Great development is more about paying attention to the end users, having an eye for usability and design and great communication & the ability to execute. The current LeetCode churning explains why Google hasn't produced anything worth using since Brin & Page checked out and why so much of software today is absolute garbage - but it is what it is I suppose. I suppose its better to screen for IQ and desparation than you know...people who love developing great software. Let them keep doing what they're doing while treating their own people like disposable garbage during hard times (like Google & Meta just did) and we'll see what they'll churn out over the next decade or so (I'm not expecting anything from either of these companies).

Sorry for the criticism here, but your linked paper / article does nothing to explain the math behind transformers. You should re-name it something like 'Scaling Transformer Mathematics' or 'The Math Behind Scaling GPUs / Transformers' or whatever more descriptive name there is to describe what you're outlining in your article. 'Transformer Math 101' should at least explain 1) input embeddings 2) (keys, queries, values) and the idea behind the matrix multiplication of these linear / numeric sets of values 3) the Softmax computation in the original paper as well as the relevant matrix transformations which take place 4) dot products ( and how they relate to similarity scoring) 5) feed-forward neural networks and the gradient descent / backpropagation and how they work. There are many many concepts you didn't even touch upon. This is not 'Transformer Math 101' by any means.

Sorry, but I completely disagree with your general take. An AGI is a generalized agent which is able to perform extreme information compression on any available set of inputs. Think Force = m*a and coming up with the laws of the universe. You can store each possible action in memory of all available results OR -- a generalized agent will find the most succinct and least complex form of modelling the state of a system. An AGI to me in other words is a form of intelligence which is able to extract information and compress it such a way to not allow the lossy compression or storage to hinder its ability to predict real outputs from a set of training data fed into it. By the way, I have some ideas on how to create one and I'll be sharing some of the key algorithms and data structures that I'll be using here for anyone interested:

https://github.com/photonlines/AGI-Algorithms-and-Prototypes...

You would only rotate a node when it becomes un-balanced (i.e. so the only case you would deal with is the instance where a new child-node is added to another child within the tree). There are no cases where you would need to re-balance when you add a 2nd child - the balance factor would be larger than 1 in the instance of adding the 1st child element to the node and thus you could never find cases like this (unless I'm misunderstanding what you're asking).

It could be good for in memory stores / log-structured merged trees / other data store applications although it isn't used much now-days. I find them simpler to implement and understand than red-black trees -- although that's a matter of taste I suppose. They beat red-black trees in read-heavy loads (i.e. writes / updates are more costly for AVL trees than for Red-Black trees although they beat R-B trees for read-heavy loads). You can find another implementation in Illumos (an open source Unix operating system) available here: https://github.com/illumos/illumos-gate/blob/master/usr/src/...