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Welcome to Backend Engineering Journey.

This curriculum is engineered for a specific transition: moving from rusty, fragmented coding knowledge (~10% retention) to Mid-to-Senior Backend Software Engineer capable of designing, implementing, reviewing, and operating production systems in the AI era.

1. Philosophy: Engineering in the AI Era

AI coding assistants can write boilerplate CRUD endpoints in seconds. They also routinely introduce:

Therefore, this curriculum does not test whether you memorized function signatures. Every lesson trains you on four things:

  1. First-Principles Mechanics: How Linux, TCP, Python's memory model, PostgreSQL's B-Tree/MVCC, and Redis/RabbitMQ actually work under the hood.
  2. Reading Companion: Exact chapter pointers to the highest-signal software engineering books so you build deep theoretical backing alongside code.
  3. AI Trap & Code Review: Every lesson includes a realistic snippet of plausible-looking "AI-generated" code containing a subtle production bug that you must diagnose and fix.
  4. Incremental Portfolio Architecture (pulseforge/): Instead of throwaway toys, you evolve a single flagship backend platform across all six phases (v0.1 $\rightarrow$ v1.0).

2. How to Work Through a Lesson

Every lesson directory follows a strict layout:

NN-lesson-slug/
├── README.md                 # The complete lesson text & reading companion
├── examples/
│   ├── 01_working_demo.py    # Runnable demonstration of the concept
│   └── 02_broken_trap.py     # Deliberately broken code demonstrating a failure mode
├── exercises.py              # The 5-rung exercise ladder (you edit this!)
├── test_exercises.py         # Automated verification suite
└── solution/
    ├── exercises.py          # Reference implementation
    └── SOLUTION.md           # Architectural walkthrough & interview answers

Your step-by-step routine for each lesson:

  1. Launch the Local Browser UI (optional but recommended): ``bash python3 -m web_ui.server --no-open ` Open http://127.0.0.1:5050`.
  2. Read ## At a glance and ## Reading Companion: Skim or read the recommended book pages if you want deeper context.
  3. Read ## Deep dive and run examples/: ``bash python3 phase0-foundations/01-linux-and-shell/01-processes-and-streams/examples/01_streams_and_signals.py `` Never just read code—run it and observe the output.
  4. Study ## AI Trap & Code Review: Try to spot the production bug before reading the explanation.
  5. Solve the 5-Rung Exercise Ladder in exercises.py: - Rung 1 (Warm-up): Basic mechanics verification. - Rung 2 (Fix the AI Bug): Repair a broken/leaky implementation. - Rung 3 (Implement): Build a core component from specification. - Rung 4 (Refactor): Clean up structure, typing, or algorithmic complexity. - Rung 5 (Production Edge-Case): Handle concurrency, partial failure, or malicious input.
  6. Run the tests until green: ``bash python3 -m unittest phase0-foundations/01-linux-and-shell/01-processes-and-streams/test_exercises.py -v ``
  7. Answer the ### Can you explain? questions out loud: These are real Mid-Level backend interview questions. Only after answering them, expand the Reference Solution (SOLUTION.md) in the web UI.
  8. Mark complete & commit: Use the self-check panel in the web UI. It ticks the lesson in PROGRESS.md and saves your rungs and notes to progress/rungs.json. Commit both with the lesson: ``bash git add -A git commit -m "feat(p0-01-01): complete processes and streams exercises" ``

3. Recommended Weekly Schedule (~15–18 Hours/Week)