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A developer-friendly walkthrough of the Transformer architecture that powers GPT, BERT, and modern LLMs — covering self-attention, positional encoding, and the encoder-decoder structure with code examples.
A practical introduction to reinforcement learning concepts — agents, environments, rewards, and policies — with TypeScript examples that map RL ideas to problems developers already understand.
A beginner-friendly guide to computer vision fundamentals using Python and OpenCV — covering image processing, edge detection, object detection, and building a practical image classifier.
How to integrate AI tools into your code review workflow — covering automated suggestions, security scanning, review prioritization, and maintaining human judgment.
A practical introduction to natural language processing concepts web developers need — tokenization, sentiment analysis, named entity recognition, and integrating NLP APIs into applications.
Practical prompt engineering techniques that turn LLMs into reliable coding assistants — covering context management, chain-of-thought, and structured outputs.
A practical introduction to machine learning concepts that backend engineers need when integrating ML models into production applications.