The 30-Day Learning Plan
Section 15 · Lesson 4 · Level: beginner · ~15 min · Prereq: none
Why this matters¶
A 135-lesson wiki is easy to start and easy to abandon in week two. This page is the schedule: four weeks at roughly 45 minutes a day, one deliverable every week, and a checkpoint that tells you whether you are actually learning or only reading. Do not follow it perfectly. Follow it unevenly for 30 days instead of perfectly for three.
The plan at a glance¶
timeline
title The 30-day plan
section Week 1 Foundations
Days 1 to 2 : Workbench, first prompts, real output
Days 3 to 4 : Git, branches, first pull request
Days 5 to 7 : First CI pipeline green
section Week 2 Ship something
Days 8 to 10 : API, database, first migration
Days 11 to 13 : Staging deploy and a smoke test
Day 14 : Checkpoint — public URL
section Week 3 Harden it
Days 15 to 17 : Tests, smoke tests, reviewing agent output
Days 18 to 20 : Secrets, rules file, skills
Day 21 : Checkpoint — agent-proof repo
section Week 4 Add the model
Days 22 to 24 : Context, MCP, retrieval
Days 25 to 27 : Evals, injection, budget
Days 28 to 30 : Ship it, measure it, plan the next 30 days
Week 1 — foundations (~45 min/day)¶
- Day 1: Set up your workbench, then How to use this wiki. Run one command from each.
- Day 2: How LLMs work for coders and Prompting fundamentals. Paste real output into your notes.
- Day 3: Structuring instructions and Prompt patterns cookbook.
- Day 4: Git essentials and Branching and pull requests. Branch, commit, open a PR.
- Day 5: GitHub CLI quickstart and Writing GitHub Actions.
- Day 6: Your first CI pipeline and Writing a great README.
- Day 7: Fix whatever is red. Add the badge to the README.
Deliverable: a public repo with a green pipeline and a README a stranger could follow.
Week 2 — ship something¶
- Day 8: The feature loop and Plan mode and spec-driven development.
- Day 9: Data modeling basics.
- Day 10: Deployment concepts and Deploy on Railway.
- Day 11: Postgres on Neon. One branch per environment.
- Day 12: Environments and promotion.
- Day 13: Local to cloud walkthrough.
- Day 14: Capstone 1, worked to the milestone list.
Deliverable: your API and database on a public URL, with a merge that auto-deploys to staging and one promotion you approved.
Week 3 — harden it¶
- Day 15: The test pyramid in practice and Testing with agents.
- Days 16–17: Integration and smoke tests, Reviewing agent output, Code review for AI code.
- Days 18–19: Secrets hygiene, Secure code generation, Rules of operation: the full stack, AGENTS.md that actually works.
- Day 20: What are agent skills? and Writing good skills.
- Day 21: Capstone 3, including the fresh-session test.
Deliverable: a repo with a lean AGENTS.md, three skills, a hook, required checks, and a logged test of all three.
Week 4 — add the model¶
- Day 22: Context engineering.
- Days 23–24: What is MCP?, Using MCP in practice, then MCP security risks — before you install another server.
- Days 25–26: Evaluating AI features, Token and cost discipline, OWASP LLM Top 10 in plain English, Prompt injection and exfiltration.
- Day 27: Observability and Rollbacks and incidents.
- Days 28–30: Capstone 2. Record the eval score and the cost per request in the repo.
Deliverable: a model feature behind a flag, an eval set in CI, real numbers in docs/ai-feature-metrics.md, and a written plan for the next 30 days.
Four tracks, if you are not here to ship a product¶
The four paths on How to use this wiki map onto the same 30 days. Just shipping: weeks 1–2 and Capstone 1; skip the evals week. Professional engineering: week 1 on git and CI, then branch protection, the test pyramid, ADRs on day 19 instead of skills, code review on day 17. AI-product builder: weeks 1 and 4, plus context engineering and MCP, and Capstone 2 instead of Capstone 3. Team lead: Rules of operation, Writing SOPs, Issue-driven development, CODEOWNERS, Human accountability, Managing a skill library, then Case study synthesis.
Checkpoints¶
- Day 7. You can clone your repo on a clean machine and run its tests using only the README, and explain what a pull request does.
- Day 14. Your app is on a public URL. A merge deploys to staging automatically, and you promoted one commit yourself, with a rollback you tested.
- Day 21. A fresh agent session, given only the repo, can make a small change and open a passing pull request.
- Day 30. A model feature behind a flag, an eval score in the repo, and one number you can defend: cost per request.
After day 30¶
The plan does not end, it changes gear: one shipped change per week (small is fine — the point is the loop), one lesson per week in your own docs/lessons.md with the command and its output, and one skill per month capturing a workflow you repeated three times.
Try it¶
- Copy the four weeks into your calendar as 30 blocks of 45 minutes, titled with the day's lesson.
- Start a notes file and paste one real command output into it every day. No summaries.
- At each checkpoint, write one sentence: what you can do now that you could not on day zero.
- At the end of day 30, write the next 30 days yourself.
Common mistakes¶
- Reading ahead instead of shipping — finishing week 3's reading in week 1 with nothing deployed.
- Skipping week 3 because week 2 felt good — you get a deployed app you are afraid to change.
- Treating the checkpoints as optional — they are the only evidence the month worked.
- Not recording outputs — "I read about migrations" and "I ran one" are different things a year later; the note with the error message is the difference.
- Stopping at day 30 — the compounding starts at week five.
Key takeaways¶
- 45 minutes a day, four weeks, one deliverable per week, four checkpoints.
- Every day ends with a run command and a recorded output.
- Pick a track on day 1 and let it reorder days 17–28; do not do all four.
- The checkpoints are tests of capability, not of reading: deploy, promote, get a stranger productive, record a number.
- After day 30: one shipped change a week, one lesson a week, one skill a month.
Further learning¶
- How to use this wiki — the four paths this plan is built from.
- Capstone 1, Capstone 2, Capstone 3 — the four deliverables, in more detail.
- Further learning — courses and docs for after the 30 days.
- Contribution exercises and Quality exercises — the wiki's own exercise pages, if a day's lesson was too easy.