Rethinking Research Notes: Adopting Zettelkasten for My Paper Explanations
Announcing the adoption of the Zettelkasten method for structuring paper explanations on abhik.ai/papers to improve connections and reduce redundancy
Deep dive into machine learning, computer vision, and software engineering. Expert insights on AI, local LLMs, quantization, and practical implementation details from real-world projects.
Announcing the adoption of the Zettelkasten method for structuring paper explanations on abhik.ai/papers to improve connections and reduce redundancy
Explore how torch.compile accelerates PyTorch models through kernel optimization. This article visualizes PyTorch kernel structures and their file mappings.
Learn why PyTorch throws the "view size is not compatible" error, understand tensor memory layout, and discover optimal solutions with performance benchmarks.
Fix Linux GPU boot errors: nouveau vs NVIDIA driver conflicts, initramfs solutions, and the early driver loading chicken-and-egg problem.
Interactive H.264 video compression guide with visualizations. Explore motion estimation, DCT transforms, quantization, and rate-distortion optimization.
Understand GGML file structure and quantization formats used by local LLMs. Visual guide to how llama.cpp stores and loads model weights efficiently.