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Introduction "This may be the story of how it all began." —— Andrej Karpathy This is the No.48 article in the "One Open Source Project a Day" series. …
The moment that made this problem real Scroll through your social media feed for ten minutes and you'll encounter it dozens of times: a clip of a humm…
A traditional Snake game grid has only 4 states each grid point can be in: empty, head, body, or apple. And for some reason every published Snake AI p…
Seeing Fast and Slow: Learning the Flow of Time in Videos Time is everywhere in video — yet most computer vision models treat it as an afterthought. W…
I just finished building a deep learning system that identifies pneumonia from chest X-rays with 96% accuracy and an AUC-ROC of 0.99. I ran the entire…
When one learning rate isn't enough — per-parameter scaling and the decay problem Momentum gave the optimizer a memory across time. Adaptive methods g…
These are my notes on the first part of Andrej Karpathy's Makemore series. I intend to add notes on the remaining videos soon. If you spot any errors …
Anthropic's prompt cache has a 5-minute TTL. Orchestrator loops running faster than 270 seconds pay ~10% of full input token costs. Key Takeaways Anth…
AI systems are no longer just software. Modern large‑scale models depend on a full vertical stack that spans: numerical formats, quantization strategi…
Claude Code's underlying model excels at understanding existing codebases and maintaining instruction fidelity in long sessions, making it the better …
A spiking neural network allegedly reached 1.088 billion parameters and trained from random initialization to a reported loss of 4.4 before the builde…
Fig 1. YOLOv12 performing real-time vehicle detection on the Cars Detection Dataset. 1. Introduction: The Need for Speed and Precision Object Detectio…
Image classification sounds easy until you remember that a computer never sees “objects.” It only sees pixel arrays. This post explains why that makes…
Understanding CNNs means understanding how models turn raw pixels into structured representations. This guide explains convolution, pooling, and archi…
Multilayer Perceptrons (MLPs) are the foundation of deep learning. This guide explains MLP intuition, real-world usage, and when you should (and shoul…
What is regularization in machine learning, and how do you actually prevent overfitting in practice? This guide explains L1 vs L2, dropout, and early …
Deep learning and neural networks work because of entropy, KL divergence, probability distributions, and optimization. This guide explains the theoret…
A controlled study finds that data distribution, not just volume, dictates LLM capability. Benchmark-aligned training inflates scores but creates narr…
AI News Update: April 10, 2026 - A Week of Breakthroughs and Concerns Published: April 10, 2026 | Reading time: ~5 min This week has been a whirlwind …