mrv_asura

A concise, visual retelling of [Jay Kreps’ classic essay](https://www.linkedin.com/blog/engineering/distributed-system...) All diagrams are from Kreps’ own Stanford EE380 lecture (slides).

1. [Lecture video](https://www.youtube.com/watch?v=SU8LaHLh6Ng)

2. [Slides](https://web.stanford.edu/class/ee380/Abstracts/141112-slides...)

I read the original blog, but i thought it would be better if it was more concise and more visual. Apparently, the author had already given an= lecture (that too with slides). So, put the LLMs to good use.

I loved the original blog so much! Fun read it was!

Awesome! Curious how you guys manage to make it "75% cheaper"?

Personal favorites:

1. Merkle Trees: https://ronkathon.pluto.xyz/src/tree/index.html

2. Digital Signatures: https://ronkathon.pluto.xyz/src/dsa/index.html

Also checkout the github repo: https://github.com/pluto/ronkathon

See github repo: https://github.com/mrdaybird/artspeak

I learnt about KL Divergence recently and it was pretty cool to know that cross-entropy loss originated from KL Divergence. But could someone give me the cases where it is preferred to use Mean-squared Error loss vs Cross-entropy loss? Is there any merits or demerits of using either?

Great explanation. Now it makes sense! I briefly remember napping during my physics lectures looking over the transformation equations, which did not make sense to me. ( I know it's partly my fault but...)

I have never used Postman, but I am curious, how useful or good, if any, their product is? given the recent bashing it received for being over-valued and being just a wrapper around curl. If people like it and are willing to pay for it, isn't its valuation justified, which I think is true for any startup in general.

Read: Another nail in Netflix's coffin.

Creator of mlpack and armadillo announced Bandicoot.

Question: Does fp16 provide more accuracy than mixed-precision? If so, any reason for this to be happening?

Looking at the discussion, everybody is agreeing to the fact that it is already well-known that fp32 is overkill and fp16(or bf16) is already industry standard(for most cases at least). But any opinions on mixed precision floating point is seems to be missing. Has anybody seen benchmarks that seem to indicate that mixed-precision fp performs worse than fp16 and fp32,(other than the paper)?

> The reason for having more bits is having large numbers of incoming or outgoing connections.

I am having trouble getting my head around this statement, could you please explain this more? This idea is not intuitive to me. Any example will be much appreciated.

My current thought process is this: how having more dynamic range of a single weight/parameter will help in more incoming and outgoing connections? Maybe I am approaching this statement the wrong way.

Thank you. :)

Well, for me in the beginning getting results matter more than clean code. There are certain things you learn along the way, like what 'not' to do, which help you in the long run, so you don't run into hiccups along the way while doing things quickly. It's also important to write simple code because simple code is more scalable and refactor-able in general.

I wish I had put more benchmarks in the articles to show its application and how it performs. I had some interesting findings on the performance differences between CPU(unrolled, transposition before matmul etc), GPU and also simd code using WebAssembly. I hope to put in another article as this blog was already too long.

"Financial freedom was never the destination. There is no destination.

There’s only a road.

And if life is a journey, then financial freedom is more like a horse.

Being on a horse makes your journey easier, faster, and more enjoyable."

- a quote from the article.

It gets more interesting the further you read! Superb work by the author.

The timing of this is great! I just watched Casey Muratori's lecture, "Simple Code, High Performance"[1] and its follow-up videos[2, 3]. I highly recommend watching it. It is about Software Optimization, and gives practical and high-level understanding of topics like CPU Architecture, SIMD etc. [1]https://www.youtube.com/watch?v=Ge3aKEmZcqY [2]https://www.youtube.com/watch?v=8VakkEFOiJc [3]https://www.youtube.com/watch?v=1tEqsQ55-8I