gauge_field

I am not sure if we see the same thread. There is one reaction from "Rust" dev (who seems have a very new account on github) on why not rust. Most of the others seem to be from C# side. The pattern also seems to be the same on reddit thread. There is one post about why not rust, equally (or more depending how you weigh) is how other people react to this news.

What is weird is how much people talk about how other people react. Modern social media is weird

You also need some foundation in Linear Algebra if you want to have better formal understanding of its underlying framework( which is Hilbert Space).

Sometimes, one case where I made use of this is enumeration of uarch for different hardware to read from the host machine. The update for for new uarch type is closed ended until there is new cpu with new uarch, which is long time. So, for a very long time it is open-ended with very low velocity in change. It is ideal for enums (for a very long time), but you still need to support the change in list of enum variants to not break semver.

Yeah I feel that, not the entire language but, many of its choices, like error handling, sum types (with exhaustive enum matching) especially when writing in python.

Rust's design is designed to be more in the mentality of if it compiles that it is good enough, leaving less for runtime issues to occur unexpected, dictated by type and memory safety. So, it requires more type info (unless you use unidiomatic unsafe code) and talking with borrow checker. But, once you internalize its type system and borrow checker, it pays off if you care about compiler driven development (instead of dealing with errors in runtime).

The stable ABI is really tricky. Another pointer from another talk was about how stabilizing ABI might reduce the performance hash functions (which is used by the compiler) by 0.5%. So, it is really a tricky decision to make that involves doing the right tradeoff at the right point in time.

For me, it was introductory quantum mechanics (QM) books, you can go with MIT online course from Barton Zwiebach and online course from BLIS (This is for Rust/C implementation of BLAS). If you fall in love with QM and go for more rigorous formulation of its mathematical structure, you can follow it up with An Introduction to Hilbert Space by N. Young, which was the book used in my next semester for Hilbert Space Course.

Hilbert Space is the mathematical framework to describe QM systems.

On what topic? Linear Algebra, Quantum Mechanics?

For me, the main solution was to apply it to another problem that uses Linear Algebra as Application, which in my case was Introductory Quantum Course and implementing BLAS using Rust and C. That way you keep thinking and using this info. Otherwise, information in vacuum seems to abstract to care about.

If you are shocked by this, I suggest not reading his other recent topics.

A few main reasons:

- Quantum Hardware is tough scale up (not impossible) due to decoherence of quantum systems, you need quantum error correction.

- Finding an application that justifies it economically (against SOTA methods in classical part) is hard except for a few areas, (Cryptography, simulation of quantum systems that are not already solved by classical simulation methods)

- Hype seems to be working (see the stock prices of several quantum companies after the announcement of Willow chip, one of which had only the word quantum in its name despite not being QC hardware company) (which I dont approve of)

A contributing factor is how complex/smart the current hardware is. It can have cache line size, different forms of hardware/software prefetching, different ports for different ops, memory latency, simd extensions. These leave many opportunities for algorithms to be optimized over. There is also the issue of real life scenarios not matching with asymptotic ones (due to e.g., size of input), which when coupled with previous factors, leads to even more potential optimization schemes. Obligatory reference: https://agner.org/optimize/

Another point to appreciate is how universal this principle of symmetry is. It is used in every branch of physics going from Classical Physics (Lagrangian Formulation) to quantum physics (with Feynman's Path Integral Formulation), from conservation of momentum to conservation of electric charge in (U(1) Symmetry) of fundamental particles. The fact that she was able to do this as a woman 100 years agos is also amazing.

Agree with that. His statements are available on youtube. I was suprised how positive and eager to the change he was.

I am also curios about at least some benchmark even if it is not one-to-one perfect comparison. There should be at least one case-study where they have shorter feedback loop with Zig than with Rust. It would be interesting to see that.

Yeap, type provides a really good baseline for a documentation. A good documentation is really needed when debugging dynamically typed language python.

+ Deepseekai recently being in the headlines + Lack of knowledge around simd extension. Modern social media is interesting...

That is what I am struggling to understand about the hype. I regularly use them to generate new simd. Other than a few edge cases (issues around handling of nan values, order of argument for corresponding ops, availability of new avx512f intrinsics), they are pretty good at converting. The names of very intrinsics are very similar from simd to another. The very self-explanatory nature of the intrinsics names and having similar apis from simd to another makes this somewhat expected result given what they can already accomplish.

Generally speaking, Scott Aaronson has been really fun to listen to on variety of topics. I would suggest listening a few recent podcast he appeared on youtube. He has this really nice combination of no-bullshit and rigorous attitude.

In terms of books, I would say Linear Algebra Done Right. The book requires some background to understand efficient. But, once you have some background, it is very good for having a systematic and rigorous understanding of Linear Algebra theory

I had encountered several spamming bots. In all instances, I reported the issue to github support. They were very quick (beyond my expectation) to respond. I suggest others do the same. It has been really easy and affective as far as my experience goes.

Just to give a concrete example, it helped me with my projects involving langchain and several langchain extensions. I have a clear and consistent source of version record for each dependency.

Here is another paper about its applicability on a range of problems: https://cacm.acm.org/research/disentangling-hype-from-practi...

Here is a perspective from another MS researcher: https://www.youtube.com/watch?v=WY3htdKUGsA&t=1564s&ab_chann...

Essentially, they argue that unless strong algorithmic breakthrough happens (e.g. having cubic speedup, instead of quadratic), the only practical problem for which quantum computer will be useful, are those where you get exponential speed up:simulation of quantum systems (and breaking of RSA encyrption if you count that). Even those are challenged by other (approximate) simulation by Classical Deep Learning. There will be some quantum models for which quantum supremacy will be useful and Deep Learning wont. The question what classes of systems.

The first (and important enough) practical application (apart from breaking RSA) seems to be simulation of quantum systems. But, this has some problems since alot of quantum models can be simulated with Deep Learning methods (getting better) to a good approximation. Probably, some quantum systems wont be good for (classical) Deep Learning, those might be the first candidates for application of quantum computers.

Especially if you consider how they choose the words and how they can be interpreted

" Second, Willow performed a standard benchmark computation in under five minutes that would take one of today’s "

Standard benchmark in what sense. Well, it was chosen for task where quantum computer would have better performance.

I am not saying this is nothing. Maybe, use more reserved words, e.g. "special quantum oriented benchmark" or something.

When I think of standard benchmark, I am thinking more common scenarios, e.g. searching, sorting, matrix multiplication.

If you want to watch a talk from a person with good presentation skills on this point, I would recommend the following video: https://youtu.be/cWSh4ZxAr7E?t=3314

Regarding big business, I kind of have the opposite reaction that it has been more community maintained and been the one of the reasons I kind of admired it as a software project. They also tried to make this choice clear by making the tools, documents as accessible as possible.

If you look at the history of Rust, the crates, cargo-team, etc, it is mostly community maintained.

An example of such community-oriented large project is Bevy.

It was only when libraries, rust itself was mature enough, the big tech companies started get involved (apart from Mozilla of course).

Whenever the project gets big and important enough, it will get attraction from large companies. The same is true for C++, as well.

From my observation, their youtube views have been really high. Whenever I see a video review on youtube, it was on average ~100k views with lots of engagement.

Some people see it as not worth and too boring to understand and actually solve it. I had a few friends of mine that I used to teach in our course work. For some of the fundamental classes for stem (e.g. Probability and Stats), even though I tried to show him a way to arrive at the solutions, he tried to ask directly for the solutions instead of arriving at them by himself.

The same thing was true for probably me when I was studying geography and history in my high school years since they were taught largely by a collection of trivia knowledge that I did not find interesting. I would have used chatgpt and be done rather than studying them. But, when I took the courses that covered the same topics in history in my university, it was more enjoyable because the main instructor was covering the topic to tell a story in a more engaging manner (e.g. he was imitating some of the historical figures, it was very funny :))