Tech Blog

Master Bcrypt password hashing algorithm. Learn salt generation, cost factor tuning, hash structure breakdown. Compare Bcrypt vs Argon2 vs Scrypt. Complete code examples for Node.js, Python, Java included!

Deep dive into Attention Mechanism core principles including Self-Attention, Query-Key-Value computation, and Multi-Head Attention. Master the technical foundations of Transformer, GPT, and LLM with complete Python code examples.

Deep dive into Token and Context Window concepts in large language models, including BPE, WordPiece tokenization algorithms, model context window comparison, and practical methods for token counting and cost optimization.

Comprehensive guide to diffusion models covering core principles, forward diffusion and reverse denoising processes, DDPM/DDIM algorithms, and Stable Diffusion architecture. Compare with GAN and VAE, explore text-to-image, image-to-image, and inpainting applications with Diffusers code examples.

Deep dive into vector embedding technology: evolution from Word2Vec to Sentence-Transformers, OpenAI embedding models in practice, semantic search and recommendation system applications. Includes Python code examples and similarity calculation explained.

Comprehensive guide to Generative AI covering core principles, key technologies (LLM, Diffusion Models, GAN, VAE), and applications. Includes GPT, Claude, Midjourney comparisons with practical tips and tool recommendations.

Master Knowledge Graphs: triple structure, graph database applications, and construction workflow. Includes Neo4j code examples, GraphRAG technology deep dive to build smarter AI knowledge systems.

Master large language model fine-tuning techniques: Full fine-tuning, LoRA, QLoRA and other PEFT methods explained. Includes Hugging Face practical code, data preparation guide, and fine-tuning vs RAG selection strategies to help you customize your own AI model.

Deep dive into LLM Function Calling principles and practices. Covers JSON Schema definition, OpenAI API usage, parallel calling, and MCP protocol comparison. Includes complete Python code examples for building AI assistants with tools!

Deep dive into LLM hallucination: what is AI hallucination, why it happens, types of hallucinations. Includes detection methods, reduction strategies, RAG and prompt engineering practices to build more reliable AI applications.

Deep dive into LoRA (Low-Rank Adaptation) fine-tuning technology: mathematical principles of low-rank decomposition, key parameter configuration, QLoRA quantization optimization, and PEFT library practical code. Master the core skills of efficient LLM fine-tuning with 90% memory reduction while achieving professional-grade results.

Deep dive into LLM quantization techniques: INT8, INT4, FP16, BF16 quantization types explained, PTQ vs QAT comparison, GPTQ, AWQ, GGUF quantization methods in practice. Includes llama.cpp and bitsandbytes code examples to help you deploy lightweight LLMs efficiently.

Deep dive into Multi-Agent Systems (MAS): architecture patterns, communication coordination mechanisms, and framework comparison. Includes CrewAI, AutoGen, LangGraph practical code examples for mastering multi-agent collaboration!

Comprehensive guide to neural network fundamentals including artificial neurons, activation functions, forward and backpropagation, loss functions and optimizers. Deep dive into CNN, RNN, Transformer architectures with PyTorch/TensorFlow code examples.

A comprehensive guide to NLP natural language processing, covering tokenization, named entity recognition, sentiment analysis, machine translation, and mainstream models like BERT and GPT.