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Explore Jason Wei's three laws of AI: the Verifier's Law, Commoditization of Intelligence, and the Jagged Edge. A framework for understanding AI's future progress and automation timeline.

Boost RAG accuracy by 70%: We tested 9 chunking strategies (fixed-size, recursive, semantic). Semantic chunking won. Optimal: 256-512 tokens, 10-20% overlap. Includes Python/LangChain code and production tips.

Tested 5 RAG frameworks with real benchmarks: LangChain won for prototyping (3x faster dev time), Haystack for production (99.9% uptime). Includes speed tests, cost analysis ($500 vs $5,000/mo), and Python code examples.

DeepSeek V3 Multi-head Latent Attention (MLA) cuts KV cache 4-8x vs standard MHA. Learn low-rank compression, matrix absorption, prefill vs decode phases. Complete PyTorch implementation with tensor shapes.

Comprehensive guide to Large Language Models: how LLMs work, Transformer architecture, training process, prompt engineering, and real-world applications. Learn about GPT, Claude, Gemini, and more.

Monitoring **PyTorch GPU memory usage** during model training can be perplexing. To demystify this, we'll dive into the **PyTorch memory snapshot** tool, a powerful utility for detailed **GPU memory ...

Unlock the full potential of your CUDA kernels by mastering memory coalescing with TiledCopy. This article dives deep into optimizing data transfers from Global to Shared Memory on NVIDIA GPUs, covering cp.async, row-major vs. column-major layouts, and cache line alignment to maximize memory bandwidth and accelerate your deep learning workloads.

Discover how linear layers enable multi-head attention in Transformers, powering advanced NLP models with parallel processing and rich representations.