Understanding Tool Calling
Master how LLMs invoke external tools ā from JSON Schema definitions and model decision-making to agent loops, security, and production monitoring.
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Master how LLMs invoke external tools ā from JSON Schema definitions and model decision-making to agent loops, security, and production monitoring.
Build a code-editing agent from scratch in Python ā an LLM, a loop, and three tools in a compact baseline implementation.
Master the art of managing what goes into a language model's context window ā from tokens and system prompts to tool calls and memory strategies.
Go from zero knowledge to a working Chrome extension ā learn the fundamentals, security model, and build a real extension that modifies web pages.
Walk through Karpathy's 200-line GPT implementation ā from autograd and tokenization to multi-head attention, training, and text generation.
Build a neural network from scratch ā Karpathy's autograd engine, neurons, layers, and training ā in pure Python with zero dependencies.
Build a GPT language model from scratch in PyTorch ā from a bigram baseline through self-attention, multi-head attention, and full transformer blocks to generating Shakespeare.
Build character-level language models from scratch ā from bigram counting through MLPs, batch normalization, and WaveNet ā generating new names with PyTorch.
Master the fundamental data structures and algorithms that underpin all software ā arrays, linked lists, trees, graphs, sorting, and more ā with typed Python implementations and complexity analysis.
Understand the layers beneath your code ā how CPUs execute programs, how memory hierarchies work, how processes and threads enable concurrency, and how networks connect everything together.
Learn the design patterns, testing practices, and system design principles that separate working code from well-engineered software ā from GoF patterns to scalable distributed architectures.