AI learning hub · sharpbyte.dev

Learn with a map, not a pile of tabs

Two engineer tracks: Path A and Path B—pick up bridges, interview prep, and system design from the cards below.

Path A

Foundations — ML → LLM depth

Python data stack through Transformers, pretraining and fine-tuning, evaluation, and multimodal intuition. For engineers who want to train, debug, and adapt models.

Open syllabus →

Path B

Applied — LLM products & agents

APIs, tokenization, structured tools, hybrid RAG, LangGraph, MCP agents, evaluation, deployment patterns, and when to fine-tune or serve open models locally. For engineers shipping reliable copilots.

Open syllabus →

Interview prep

Interview ready — theory & design

Theory Q&A (core + applied LLM) plus the system design guide (§§1–8: architecture through security—diagrams and recap tables; §§9–17 next).

Open interview hub →

How to use this hub: each topic page follows the same rhythm (outcome → prerequisites → pitfalls → reading). See sample topic layout with a code block. Interview ready includes theory Q&A and the design guide (sections 1–8 live; 9–17 next). Cross-track context lives in bridge topics; end-to-end briefs are in capstones. Acronyms and short definitions are collected in the glossary.