Muhammad Abdullah Awan

Software Engineer | Backend & AI/ML | Python • FastAPI • Laravel | Deep Learning • Computer Vision • Generative AI | BS Software Engineering

[Raj kr @raj_4_69]

All government websites be like:

[Muhammad Abdullah Awan @IAmAbdullahAwan]

HBD

[Ubuntu @ubuntu]

35 years strong. 🐧🧡

Happy birthday, Linux.

[NativePHP @nativephp]

C is the fastest common-use programming language we have available

It's the best at driving hardware

#PHP is essentially a C wrapper, the scripting language for C

So it can do anything C can do

[Mo Yahya @Momalindi]

On behalf of the United Nations in Pakistan, Happy 79th Independence Day, Pakistan! 🇵🇰

[Muhammad Abdullah Awan @IAmAbdullahAwan]

Proud moment for all of us. Happy Independence Day, Pakistan 🇵🇰

[Pakistan Cricket @TheRealPCB]

This Independence Day, let's celebrate our shared spirit, uphold the values that unite us and continue building a stronger Pakistan 💚🙌

Pakistan Zindabad! 🇵🇰

#IndependenceDay | #PakistanZindabad

[Muhammad Abdullah Awan @IAmAbdullahAwan]

Proud moment for all of us. Happy Independence Day, Pakistan 🇵🇰

[Muhammad Hanzala @Hanzalamalik97]

Happy Independence Day 🇵🇰❤️
#PakistanZindabad
#جشن_آزادی_مبارک

[Muhammad Abdullah Awan @IAmAbdullahAwan]

Proud moment for all of us. Happy Independence Day, Pakistan 🇵🇰

[Pakistan Zindabad 🇵🇰 @GHaZ_NAwE77]

Pakistan 79th Independence Day !

Long Live Pakistan, Pakistan Zindabad ❤

#Pakistan #PakistanZindabad
#LongLivePakistan #Peace #ISI 🇵🇰🙏

[Muhammad Abdullah Awan @IAmAbdullahAwan]

Happy 79th Independence Day, Pakistan 🇵🇰

#14August #PakistanIndependenceDay

[Jim Fan @DrJimFan]

My morning ritual: coffee, then watch our robot assemble stuff. The model isn't fast, but it measures every grasp, obsesses over every alignment, and handles every piece like an heirloom. Watching is therapeutic, even meditative. Simple pleasure from the execution of a task well done.

Uncut, no speedup, end-to-end policy in one go.

[Muhammad Abdullah Awan @IAmAbdullahAwan]

Do you remember when you joined X? I do! #MyXAnniversary

[Taylor Otwell @taylorotwell]

Man I dunno sometimes I’m pretty glad I don’t live in SF ���

[Brad Traversy @traversymedia]

Created an official site/docs for my AI Blueprint workflow - https://t.co/w0r2QRQwhA. I realize some things may not be 100% clear. This is a wip. I will be releasing some long form tutorials on the channel very soon demonstrating everything. Feel free to use, fork, make your own

[MIT CSAIL @MIT_CSAIL]

A free guide that answers key reinforcement learning questions: https://t.co/CY91nDnOkb

v/@arjunkocher & @sheriyuo

[Morty @0xMortyx]

Anthropic Engineer Andrej Karpathy:

"Optimization writes code - and sometimes it writes it better than you."

Now LLMs write 70–90% of production code at top AI labs.

90% of devs using AI tools today can't explain what's happening under the hood.

That applies 10× harder to LLM agents.

In this 15-min talk, Karpathy breaks down how AI actually works.

[Amit Sharma @AmitSha77280075]

Anthropic engineer:

"You're not supposed to prompt Claude. You're supposed to build a system that prompts itself."

In 45 minutes she breaks down how Anthropic builds agents that remember, learn from their mistakes, and get smarter with every run.

Worth more than any paid course you'll find on building agents.

Watch the session

[Chelsea Finn @chelseabfinn]

I'm giving a talk tomorrow at ICML on emergent physical generalization, including π0.7 🤖

3:15 pm @ SCALE workshop in Ballroom 201
https://t.co/xppCOkktOJ

[Jürgen Schmidhuber @SchmidhuberAI]

Who invented convolutional neural networks (CNNs)?

1969: Fukushima had CNN-relevant ReLUs [2].

1979: Fukushima had the basic CNN architecture with convolution layers and downsampling layers [1]. Compute was 100 x more costly than in 1989, and a billion x more costly than today.

1987: Waibel applied Linnainmaa's 1970 backpropagation [3] to weight-sharing TDNNs with 1-dimensional convolutions [4].

1988: Wei Zhang et al. applied "modern" backprop-trained 2-dimensional CNNs to character recognition [5].

All of the above was published in Japan 1979-1988.

1989: LeCun et al. applied CNNs again to character recognition (zip codes) [6,10].

1990-93: Fukushima’s downsampling based on spatial averaging [1] was replaced by max-pooling for 1-D TDNNs (Yamaguchi et al.) [7] and 2-D CNNs (Weng et al.) [8].

2011: Much later, my team with Dan Ciresan made max-pooling CNNs really fast on NVIDIA GPUs. In 2011, DanNet achieved the first superhuman pattern recognition result [9]. For a while, it enjoyed a monopoly: from May 2011 to Sept 2012, DanNet won every image recognition challenge it entered, 4 of them in a row. Admittedly, however, this was mostly about engineering & scaling up the basic insights from the previous millennium, profiting from much faster hardware.

Some "AI experts" claim that "making CNNs work" (e.g., [5,6,9]) was as important as inventing them. But "making them work" largely depended on whether your lab was rich enough to buy the latest computers required to scale up the original work. It's the same as today. Basic research vs engineering/development - the R vs the D in R&D.

REFERENCES

[1] K. Fukushima (1979). Neural network model for a mechanism of pattern recognition unaffected by shift in position — Neocognitron. Trans. IECE, vol. J62-A, no. 10, pp. 658-665, 1979.

[2] K. Fukushima (1969). Visual feature extraction by a multilayered network of analog threshold elements. IEEE Transactions on Systems Science and Cybernetics. 5 (4): 322-333. This work introduced rectified linear units (ReLUs), now used in many CNNs.

[3] S. Linnainmaa (1970). Master's Thesis, Univ. Helsinki, 1970. The first publication on "modern" backpropagation, also known as the reverse mode of automatic differentiation. (See Schmidhuber's well-known backpropagation overview: "Who Invented Backpropagation?")

[4] A. Waibel. Phoneme Recognition Using Time-Delay Neural Networks. Meeting of IEICE, Tokyo, Japan, 1987. Backpropagation for a weight-sharing TDNN with 1-dimensional convolutions.

[5] W. Zhang, J. Tanida, K. Itoh, Y. Ichioka. Shift-invariant pattern recognition neural network and its optical architecture. Proc. Annual Conference of the Japan Society of Applied Physics, 1988. First backpropagation-trained 2-dimensional CNN, with applications to English character recognition.

[6] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel: Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, 1(4):541-551, 1989. See also Sec. 3 of [10].

[7] K. Yamaguchi, K. Sakamoto, A. Kenji, T. Akabane, Y. Fujimoto. A Neural Network for Speaker-Independent Isolated Word Recognition. First International Conference on Spoken Language Processing (ICSLP 90), Kobe, Japan, Nov 1990. A 1-dimensional convolutional TDNN using Max-Pooling instead of Fukushima's Spatial Averaging [1].

[8] Weng, J., Ahuja, N., and Huang, T. S. (1993). Learning recognition and segmentation of 3-D objects from 2-D images. Proc. 4th Intl. Conf. Computer Vision, Berlin, pp. 121-128. A 2-dimensional CNN whose downsampling layers use Max-Pooling (which has become very popular) instead of Fukushima's Spatial Averaging [1].

[9] In 2011, the fast and deep GPU-based CNN called DanNet (7+ layers) achieved the first superhuman performance in a computer vision contest. See overview: "2011: DanNet triggers deep CNN revolution."

[10] How 3 Turing awardees republished key methods and ideas whose creators they failed to credit. Technical Report IDSIA-23-23, Swiss AI Lab IDSIA, 14 Dec 2023. See also the YouTube video for the Bower Award Ceremony 2021: J. Schmidhuber lauds Kunihiko Fukushima.

[Jürgen Schmidhuber @SchmidhuberAI]

Our Huxley-Gödel Machine learns to rewrite its own code, estimating its own long-term self-improvement potential. It generalizes on new tasks (SWE-Bench Lite), matching the best officially checked human-engineered agents. Arxiv 2510.21614  With @Wenyi_AI_Wang, @PiotrPiekosAI, @nbl_ai, Firas Laakom, @Beastlyprime, @MatOstasze, @MingchenZhuge

[Jürgen Schmidhuber @SchmidhuberAI]

Some still don't know: Google's 2017 normalized quadratic Transformer [TR1] is based on the principles of the 1991 unnormalized linear Transformer [ULTRA]. In 1991, KEY/VALUE was called FROM/TO. ULTRA’s computational costs scale linearly in input size, rather than quadratically! The 1993 paper on a recurrent ULTRA extension [FWP2] introduced the attention terminology: learning "internal spotlights of attention" by gradient descent. See the T in ChatGPT! Details and references: https://t.co/6NEFdgghAw