Knox

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Knox

Knox

@KnoxCodex

21 • Developer • ML/DL In pursuit of knowledge

Gotham Beigetreten Mayıs 2025
492 Folgt914 Follower
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Knox
Knox@KnoxCodex·
Just launched my first full-stack MERN project: AniMash 🎉 Inspired by The Social Network, it's a Facemash-style voting app for anime characters. 🗳️ Vote between pairs 🏆 See who's on top in the leaderboard ❤️ Show your Love Link: …sh-knoxs-projects-e7251485.vercel.app 🎥 Here’s a quick demo ⬇️
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Knox@KnoxCodex·
Just watched this........ Absolute Cinema 🗿
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Knox
Knox@KnoxCodex·
@ThedatagGuy Congratulations bro 🎉🎉🎉
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data guy
data guy@ThedatagGuy·
Career update Joined nit Warangal as junior research fellow Will be working on core machine learning and deep learning Stepping into the research field, excited for the new learnings
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Knox@KnoxCodex·
ML intern expectations at a YC startup
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Knox@KnoxCodex·
studying autoencoders.....
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Knox@KnoxCodex·
@akankshaa1268 No need to give in to fomo Only do the things you are interested in doing
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akanksha
akanksha@akankshaa1268·
never watched stranger things, breaking bad, squid game, family man, money heist, aspirants, kota factory, peaky blinders, mismatched, 500 days of summer etc and now not even dhurandhar my fomo is broken fr😭
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Yatharth
Yatharth@yatharthpnwr·
Starting a series where I journal my learnings from the @solana India Fellowship over the next 8 weeks (if I don’t get purged). CLIMBING THE LADDER : EP 1 LAUNCHES SOON @kirat_tw @100xSchool
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Knox
Knox@KnoxCodex·
@ThedatagGuy Primarily using Andrej Karpathy's micrograd project, pytorch documentation and a few other medium articles.
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Knox
Knox@KnoxCodex·
I'm currently trying to develop a deep understanding of pytorch's autograd engine. See you on the other side🫡
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Hero Of Justice
Hero Of Justice@herooffjustice·
So I qualified for GATE DA, barely. I did so many mistakes AAAAhhh I didn't cover the syllabus, and had other health issues, but it's on me We're getting a perfect score this time 👊
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Knox@KnoxCodex·
Qualified GATE DA Need to make a lot of improvements for next time!!!
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Ashanvi
Ashanvi@ashanviii·
@FireGoku_ dropping a comment so this masterpiece reaches my mutuals
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𝐓𝐨𝐣𝐢.
𝐓𝐨𝐣𝐢.@FireGoku_·
🗣️: “Bro, anime is peak” The anime:
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Niraj
Niraj@nirajxdev·
builders are building yappers are yapping fighters are fighting b/w DSA vs Web influencers are farming impressions course bros are selling ₹999 dreams and beginners are buying them meanwhile real builders are too busy to tweet
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Knox
Knox@KnoxCodex·
Setup review....
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Knox@KnoxCodex·
I'd rather help hianime than buy a crunchyroll subscription
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Knox
Knox@KnoxCodex·
@ThedatagGuy CampusX notes are goated 🔥 I am also studying about CNN's these days
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data guy
data guy@ThedatagGuy·
from perceptron to ResNet I have covered most of the things in deep learning AlexNet implementation in progress, and thinking of doing one small project on transfer learning with both techniques, feature extraction, and fine-tuning What topics should I definitely know in deep learning? Please let me know in the comments
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Knox
Knox@KnoxCodex·
@CorticalLabs please give me an intern as a brain cell
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CG@cgtwts

Let me explain what just happened, because I don’t think people realize how INSANE this is. > Cortical Labs put 200,000 real human brain cells onto a silicon chip and trained them to play Doom in just one week. > Each CL1 system costs $35,000. > A rack of 30 units consumes only 850–1,000 watts combined. > The human brain operates on 20 watts. > Large AI training clusters burn through megawatts. >Backed by In-Q-Tel. 115 units began shipping in 2025. > Cortical Labs is selling “Wetware as a Service” through Cortical Cloud, letting developers deploy code remotely to living human neurons with no lab required, > priced like a software subscription but powered by real brain cells grown from adult skin and blood samples. > it isn’t about gaming, it’s about biological computing that could eventually outperform traditional silicon in energy efficiency and adaptability. This is getting really scary and we’re still at the very beginning.

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Knox
Knox@KnoxCodex·
Building your own ML framework is one of the best ways to truly understand deep learning. Next improvements I’m exploring: • MNIST training example • Conv layers • Autograd engine
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Knox
Knox@KnoxCodex·
After building the framework, I packaged it as a real Python library and published it on PyPI. Now it can be installed with: pip install knoxtinytorch
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Knox
Knox@KnoxCodex·
While learning how neural networks actually work, I wondered: What if I built my own mini PyTorch from scratch? So I did. Introducing TinyTorch A tiny neural network library written entirely with Python + NumPy. And published on PyPI. Here’s how it works 🧵
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Entdecken

@ThedatagGuy @akankshaa1268 @yatharthpnwr @solana @kirat_tw @100xSchool @herooffjustice @ashanviii