AI Science Guides
Accessible explainers of AI principles and concepts — from how large language models work to neural network basics, demystifying the science behind AI in plain language.
3 articles
NEWThe Real Moat for Developers in the AI Coding Era
As Coding Agents and Vibe Coding mature, programming language choice is no longer a constraint. The two edges that AI cannot easily replace are system integration and deep business understanding.
NEWWhat is RAG and What Role Does Embedding Play?
Retrieval-Augmented Generation (RAG) has become the standard architecture for enterprise LLM applications, solving hallucination, knowledge cutoff, and private-data issues by letting models 'look up' facts before answering. Embedding is the soul of RAG—converting text into semantic vectors so similar meanings can be matched in vector space. This article explains the RAG pipeline, how Embeddings work, and how they empower production-grade intelligent systems.
How to Use AI for Stock Trading? A Complete Roadmap from Concept to Live Trading
AI-driven stock trading is no longer science fiction—it is already happening. This article breaks down the practical applications of AI in stock trading, from data collection, feature engineering, and model training to live deployment, helping you avoid the most common pitfalls.