FranQuant/the_ai_engineer_capstones
End-to-end capstone implementations for The AI Engineer (Nov 2025).
Implements four progressive capstones progressing from foundational optimization (gradient descent, backpropagation) through transformer architecture (tokenizer, multi-head attention, decoder-only models) to agentic systems with MCP tool integration and OPAL loop state management. Each project includes reproducible training loops, diagnostic notebooks, and persisted artifacts (checkpoints, telemetry logs). Targets Python 3.11 with Colab compatibility and emphasizes clean software engineering patterns: modular components, tracing/telemetry instrumentation, and deterministic control flow for agent planning and memory updates.
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Last pushed
Feb 04, 2026
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