FizzBuzzEnterpris
tinygrad
FizzBuzzEnterpris | tinygrad | |
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17 | 58 | |
- | 17,800 | |
- | - | |
- | 9.7 | |
- | 10 months ago | |
Python | ||
- | MIT License |
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FizzBuzzEnterpris
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Java 21 makes me like Java again
> I'll answer your question with a question: Have you seen https://github.com/EnterpriseQualityCoding/FizzBuzzEnterpris... ? :)
You can write that kind of crap in any language, including C++.
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No One Wants Simplicity
There’s a difference between complexity that’s inherent to the problem, and complexity that’s added by developers who have drunk architectural cool aid.
This is an example where all of the complexity is caused by rigid adherence to the most popular architectural patterns of about 10 years ago.
https://github.com/EnterpriseQualityCoding/FizzBuzzEnterpris...
It looks completely ridiculous to modern eyes, but during peak OOP it was just how you should do it.
If you like simplicity then your fizz buzz implementation would be a few lines.
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Virtual Threads Arrive in JDK 21, Ushering a New Era of Concurrency
https://github.com/EnterpriseQualityCoding/FizzBuzzEnterpris... isn't too far removed from some of what I've seen in big tech, especially architecture-wise. Certainly less costly absurdity.
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Subverting the Software Interview
What you need is Fizzbuzz, Enterprise Edition
https://github.com/EnterpriseQualityCoding/FizzBuzzEnterpris...
- Every day, I commit a new and more complicated version of some simple code
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Ask HN: Why do you make class members private?
It's been a decade since I used C# but the corporate design pattern culture of that language back then turned me off of it forever.
Everything looked like this: https://github.com/EnterpriseQualityCoding/FizzBuzzEnterpris...
Maybe it's better now but the Java/C# practice of shoveling largely empty classes around with an IDE isn't something I'd point to as a good example.
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Why DRY is the most over-rated programming principle
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With your example I had to think for about 1-2 min before it made sense. If the codebase is full of clever stuff then I have to spend hours understanding all of the clever things before I can make changes. If everything is simple then it's easy to change.
If you want to see where overengineering leads you then take a look at this project. https://github.com/EnterpriseQualityCoding/FizzBuzzEnterpris...
It is satire but I have absolutely worked in places that write code like that.
Good programmers know that it's 10x times harder to read code than write it, so they deliberately keep it simple so that they can read it later.
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Why programmers are not paid in proportion to their productivity
I did something similar a 4 or so years back. I wrote something in a month (+ a couple of working with stakeholders to make sure it did what it should). I did it in a legacy tech stack that the architects didn't like, on the side of the main activity, as the deadline was coming close and some hireing processes were slow.
A team of around devs 5 (some coming and going) having been trying to solve the same problem since, but they're still not being close to finished.
In other words, the productivity is in the order 50x to 100x slower than when I did it. Rather, the main reason was that I knew how to write code like that, while they were set up to fail.
Basically, some architect was making all sorts of unnecessary demands for how to wite the code, and the programers were not familiar with much of the tech stack that was introduced.
Also, coding standards were really verbose, easily 10x-30x what I wrote, in lines of code. The current state of what they have look suspiciously like FizzBuzzEnterpriseEdition:
https://github.com/EnterpriseQualityCoding/FizzBuzzEnterpris...
TLDR; Incompetent tech leadership prone to cargo-culting, can slow down productivity to virtually zero. In some cases, productivity can go up by ~100x if ignoring their demands.
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The use of `class` for things that should be simple free functions (2020)
I swear I've worked with people who if they were shown FizzBuzzEnterpriseEdition wouldn't be able to see the joke as that's how they naturally write all code.
https://github.com/EnterpriseQualityCoding/FizzBuzzEnterpris...
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The mindless tyranny of “what if it changes?” as a software design principle
Reminds me of FizzBuzzEnterpriseEdition . https://github.com/EnterpriseQualityCoding/FizzBuzzEnterpris...
You never know when you might need to change the implementation of how the "Fuzz" string is returned, so you need a FuzzStringReturner.
And you never know when you might need multiple different ways of returning "Fuzz", so you need a FuzzStringReturnerFactory.
And that barely scratches the surface of what you need.
tinygrad
- tinygrad: extreme simplicity, easiest framework to add new accelerators to
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GGML – AI at the Edge
Might be a silly question but is GGML a similar/competing library to George Hotz's tinygrad [0]?
[0] https://github.com/geohot/tinygrad
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Render neural network into CUDA/HIP code
at first glance i thought may its like tinygrad. but looks has many ops than that tiny grad but most maps to underlying hardware provided ops?
i wonder how well tinygrad's apporach will work out, ops fusion sounds easy, just a walk a graph, pattern match it and lower to hardware provided ops?
Anyway if anyone wants to understand the philosophy behind tinygrad, this file is great start https://github.com/geohot/tinygrad/blob/master/docs/abstract...
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llama.cpp now officially supports GPU acceleration.
There are currently at least 3 ways to run llama on m1 with GPU acceleration. - mlc-llm (pre-built, only 1 model has been ported) - tinygrad (very memory efficient, not that easy to integrate into other projects) - llama-mps (original llama codebase + llama adapter support)
- George Hotz building an AMD competitor to Nvidia.
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George Hotz ROCm adventures
Hopefully we will see now full support with AMD hardware on https://github.com/geohot/tinygrad. You can read more about it on https://tinygrad.org/
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The Coming of Local LLMs
tinygrad
https://github.com/geohot/tinygrad/tree/master/accel/ane
But I have not tested it on Linux since Asahi has not yet added support.
llama.cpp runs at 18ms per token (7B) and 200ms per token (65B) without quantization.
- Everything we know about Apple's Neural Engine
- Everything we know about the Apple Neural Engine (ANE)
- How 'Open' Is OpenAI, Really?
What are some alternatives?
FizzBuzz Enterprise Edition - FizzBuzz Enterprise Edition is a no-nonsense implementation of FizzBuzz made by serious businessmen for serious business purposes.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
holochain - The current, performant & industrial strength version of Holochain on Rust.
llama.cpp - LLM inference in C/C++
lwjgl3ify - A mod to run Minecraft 1.7.10 using LWJGL3 and Java 17, 19, 20
openpilot - openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for 250+ supported car makes and models.
proposals - ✍️ Tracking the status of Babel's implementation of TC39 proposals (may be out of date)
llama - Inference code for Llama models
fibers - Concurrent ML-like concurrency for Guile
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
music-explorer - A music scraper, navigator, archiver, and cataloger for people looking for new sounds.
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ