Added: AI Systems Design From First Principles

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Added: AI Systems Design From First Principles describes an architectural curriculum or codebase focused on building machine learning systems by breaking them down into their most fundamental computational and logical building blocks, rather than relying on high-level, black-box frameworks (like LangChain or corporate APIs).

What it Means in Practice:

• No Hidden Abstractions: Instead of just calling a pre-built library, you implement core components—such as tokenizers, vector databases, embedding distance calculators, stateful agent loops, and neural backpropagation matrices—using standard data structures.
• Focus on Core Trade-offs: It teaches you to evaluate the system at the foundational level, forcing you to reason about constraints like memory overhead (VRAM/RAM), latency (token-per-second processing), network boundaries, and deterministic execution paths.
• Engineering Over Magic: It demystifies artificial intelligence by treating models and agents as predictable, distributed software systems governed by standard engineering laws rather than abstract cognitive capability.