Understand what software work actually produced

Learning paths

Ordered sequences that reuse lessons across tracks. Paths are guides—not duplicate pages.

• Code Review Fundamentals

  From reading a diff to evidence-based approval, including AI-generated changes.

  1. Reading a Diff
  2. →Semantic vs Stylistic Changes
  3. →PR Size
  4. →Reviewing Large Changes
  5. →Identifying High-Risk Changes
  6. →Reviewing Test Changes
  7. →Reviewing AI-Generated Code
  8. →Evidence vs Assertion
  9. →Evidence-Based Approval

• Understanding a Repository

  Commits, churn, hotspots, releases, and the evidence Git can supply about activity.

  1. Commit Frequency
  2. →Additions and Deletions
  3. →Code Churn
  4. →Identifying Hotspots
  5. →Branch Comparison
  6. →Release Delta
  7. →Commits Since a Release
  8. →Evidence vs Assertion

• AI Coding Economics

  Tokens through caching, context growth, task cost, accepted-change cost, and ROI.

  1. Tokens
  2. →Input vs Output Tokens
  3. →Context Windows
  4. →Context Growth
  5. →Tool Calls
  6. →Prompt Caching
  7. →Retries and Agent Loops
  8. →Cost Per Task
  9. →Cost of Retries
  10. →Cost of Human Review
  11. →Cost Per Accepted Change
  12. →Model Selection Economics
  13. →Coding Agent ROI

• Measuring Software Delivery

  DORA-style metrics and flow measures with interpretation discipline.

  1. Deployment Frequency
  2. →Lead Time for Changes
  3. →Cycle Time
  4. →Change Failure Rate
  5. →Recovery Time
  6. →Developer Productivity
  7. →Code Churn
  8. →Evidence vs Assertion

• Evaluating AI-Generated Software

  From agent claims through Git evidence, review, cost, and verification.

  1. Evidence vs Assertion
  2. →Evidence Chains
  3. →Evidence for AI-Generated Changes
  4. →Reviewing AI-Generated Code
  5. →Reviewing Test Changes
  6. →Release Delta
  7. →Verification
  8. →Cost Per Accepted Change
  9. →Evidence-Based Approval