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A self-paced, source-linked path from Git internals to AI coding agents, ending with an optional read-only repository assistant challenge. Reading time is an estimate; one linked article is subscriber content.
Prerequisites: No branch-specific or prior AI requirement. Basic familiarity with files is useful; the optional capstone can be done collaboratively across research, design, documentation, implementation and testing.
Understand the data model behind everyday version control.
Read about blobs, trees and commits; connect file contents to recorded history.
Use a visual walkthrough to connect references and objects to common Git operations.
Learn the pieces and workflows that make an AI coding agent useful and controllable.
Map the model, context, tools and control loop in a coding agent.
Compare a simple workflow with an agent pattern and note how tool boundaries and evaluation affect reliability.
Compare a coding product case study with infrastructure built for a very different workload.
Read the product case study; note that the Pragmatic Engineer article is subscriber content.
Read the Scion case study and distinguish Anthropic-specific scale numbers from general lessons.
Apply the ideas in a small, read-only project with evidence and safety boundaries.
Define the question set, evidence format, read-only boundary and abstention behavior before building.
Check line citations, path handling and uncertainty; write down limitations and next steps.