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Hand-picked articles showcasing the best of LLM practice
Deep dive into 8 modern LLM architectures (2025): DeepSeek-V3 MLA & MoE, OLMo 2 normalization, Gemma 3 sliding window attention, Llama 4 innovations. Compare design choices, engineering priorities, and performance trade-offs.
Complete guide to Transformer architecture: self-attention mechanisms, encoder-decoder design, and how Transformers power GPT, BERT, and modern LLMs. With code examples and visual diagrams.
# Decoding Strategies for Large Language Models (LLMs) At the core of every large language model (LLM) is a sophisticated process for generating text. Instead of selecting words at random, the model...
Fresh insights and practical techniques
AI agent challenges explained: solve latency issues, fix brittle planning, and avoid reflection loops. Advanced engineering patterns and RL techniques for production-ready agentic AI systems.
Learn how TensorRT-LLM transitioned from C++ to PyTorch-first architecture, achieving 3x faster development velocity and production-ready LLM inference optimization on NVIDIA GPUs.
Complete vector database comparison guide: Milvus, LanceDB, Chroma, and Pinecone. Compare performance, scalability, and costs for RAG, semantic search, and AI applications with real-world use cases.
Comprehensive RAG chunking guide: compare 9 strategies (Fixed-size, Recursive, Semantic, etc.) with Python code examples. Optimize chunk size and overlap for better retrieval. Includes LangChain implementations.
Complete LangChain ecosystem comparison: LangChain for building chains, LangGraph for complex agents, LangSmith for monitoring. Decision matrix, code examples, and use cases for each tool.
Learn how to add special tokens to LLMs during fine-tuning without causing catastrophic forgetting. Our guide covers smart initialization and PEFT/LoRA.
Practical wisdom from the intersection of research and production
Every technique shared comes from real production systems handling millions of requests. No theoretical fluff, just what works.
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