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Case Study: How Rebecca Built a Useful Micro‑App in a Week (Checklist & Templates)

ccodeacademy

2026-01-28

9 min read

Copy Rebecca Yu’s 7‑day micro‑app sprint: daily checklists, templates, and timeboxed tasks to ship a student project in one week.

Beat decision fatigue: how one student shipped a real micro‑app in seven days — and how you can copy her sprint

Students, teachers, and self‑taught learners struggle with one shared pain: projects feel too big, scattered, and uncertain. You want a guided, timeboxed path that yields a working product and a portfolio piece. This case study deconstructs how Rebecca Yu built Where2Eat — a dining micro‑app — in seven days using modern AI coding assistants and a ruthless sprint plan. You’ll get a daily checklist, reusable templates, timeboxed tasks, and code snippets so you can replicate the sprint.

Why this matters in 2026

By 2026 the editor‑to‑deploy loop is dramatically faster. Local LLMs and lightweight agents that can run on dev machines or cloud sandboxes, decreasing round‑trip time for code generation and testing.

Recent developments (late 2025 → early 2026) that make this possible:

Local LLMs and lightweight agents that can run on dev machines or cloud sandboxes, decreasing round‑trip time for code generation and testing.
Integrated Figma‑to‑code and component marketplaces for fast UI scaffolding.
Edge serverless platforms with free tiers and instant HTTPS for deploying demo apps.
Low friction authentication like passwordless WebAuthn and built‑in TestFlight or progressive web apps (PWAs) for quick sharing.

High‑level recap: Rebecca’s 7‑day path

Rebecca had a clear problem: group chat indecision about where to eat. She set a tight goal: build a simple web app that recommends restaurants based on shared interests. Tools she used: AI assistants (Claude, ChatGPT), a lightweight frontend (React/Vite), a small serverless backend (Edge Functions/Netlify/AWS Lambda), and a quick dataset (public restaurant APIs + curated favorites). The result was a usable micro‑app in seven days.

How to replicate Rebecca's sprint: the one‑week, timeboxed plan

Below is a practical, timeboxed schedule. Each day has a concrete outcome, a checklist, and templates you can reuse. Assume ~6–8 focused hours per day; scale down to 3–4 hours if you’re balancing studies.

Day 0 — Pre‑sprint (1–2 hours): prep and scope

Goal: have a clear problem statement and a one‑sentence product pitch.

Write a one‑line pitch: "A simple web app that recommends restaurants to a small friend group based on shared preferences."
Define success criteria: e.g., "Users can get 3 recommendations in < 10s and share a recommendation link with a friend."
Create repo and baseline files: README, .gitignore, LICENSE.
Timebox: 60–120 minutes. Deliverable: public repo skeleton + pitch in README.

Day 1 — UX & MVP feature map (6–8 hours)

Goal: a clickable prototype and prioritized feature list.

Sketch core flows (5–6 screens): landing, pick vibe/tags, recommendations, share link, settings.
Create a low‑fidelity prototype (Figma or HTML wireframes). Use Figma community templates to speed up.
Prioritize features into MUST/HAVE/NICE. Keep MUST to 3 items.
Deliverable: prototype link + prioritized backlog in project board (GitHub Projects/Trello).

Day 2 — Data model & API contract (6–8 hours)

Goal: define your data shape and mock an API.

Create a minimal data model (restaurants, tags, user group preferences).
Write an API contract with endpoint examples (GET /recommendations?vibe=cozy&location=...).
Use a mock server (json-server, MSW) to get the frontend running before backend work.
Deliverable: API spec (OpenAPI minimal), mock server running.

Day 3 — Frontend skeleton (6–8 hours)

Goal: interactive frontend that calls the mock API and shows recommendations.

Scaffold with Vite + React (or Svelte/Remix if you prefer). Use TypeScript for clearer types.
Implement core screens and state (simple router, search params for share links).
Connect to mock API; show loading, error, empty states.
Deliverable: functional UI with mock data; accessible share URL updates.

Day 4 — Backend: simple logic & integration (6–8 hours)

Goal: replace mocks with a real serverless function and lightweight data source.

Implement an Edge Function (Netlify Functions, Vercel Serverless, or AWS Lambda@Edge).
Keep logic simple: filter by tags and score by shared preferences.
Use a tiny datastore: SQLite, JSON file, or an Airtable base for speed.
Deliverable: live endpoint that returns recommendations.

Day 5 — AI/Smartness & personalization (6–8 hours)

Goal: add the core differentiator — a lightweight recommender or prompt‑based scoring.

Option A: rule‑based scorer that weights tags and proximity.
Option B: prompt with a small LLM agent to refine recommendations based on group vibes (use costs and latency carefully).
Implement an offline caching layer to avoid API overages.
Deliverable: smarter recommendations and logged scoring explanations (helps in UX and debugging).

Day 6 — Polish, auth, and tests (6–8 hours)

Goal: make the app shareable and robust.

Add passwordless share links or optional email invites (PWA + Web Share API for mobile).
Basic tests: one integration test for the recommendation flow (Cypress/Playwright).
Accessibility and mobile polish: 3 key fixes from Lighthouse report.
Deliverable: demo build and test that one core flow passes.

Day 7 — Deploy, gather feedback, and iterate (4–6 hours)

Goal: launch a working demo and get first feedback.

Deploy to a free static/edge host with CI (Vercel/Netlify). Create a short README demo video (60–90s).
Share with 5 testers (friends/classmates) and collect metrics: clicks to recommendation, share link success, time to first recommendation.
Plan next steps from feedback — fix the top 3 issues the next week.
Deliverable: deployed demo + feedback notes + next actions.

Actionable checklist (printable)

Use this one‑page checklist each day. Mark Done/Blocked/Notes.

Day 0: repo + pitch + success criteria
Day 1: prototype + backlog
Day 2: data model + mock server
Day 3: frontend skeleton + mock integration
Day 4: deploy serverless API + data source
Day 5: add smarter ranking (rule or LLM) + caching
Day 6: basic tests + accessibility + share features
Day 7: deploy + 5 testers + feedback log

Reusable templates

Copy these templates to speed up your builds. Below are short, ready‑to‑paste examples.

README template (replace placeholders)

# PROJECT NAME

One‑line pitch: A simple web app that recommends restaurants for a small group.

## Why
Problem: Decision fatigue in group chats. This micro‑app makes quick, shareable restaurant picks.

## How to run
1. git clone ...
2. cd project
3. npm install
4. npm run dev

## API
GET /recommendations?vibe=<vibe>&location=<lat,lng>

## License
MIT

API contract (OpenAPI minimal)

GET /recommendations
Query:
  - vibe: string
  - lat: number
  - lng: number
Response 200:
  - recommendations: [{ id, name, score, reason }]

Issue template

Title: [Bug/Feature] short description
Steps to reproduce:
Expected:
Actual:
Notes/Env:

Pull request checklist

[ ] Linked issue
[ ] Unit/integration tests added
[ ] Linted and formatted
[ ] Deployed preview link

Timeboxed task examples (detailed 2‑hour slots)

Break each day into 2–3 focused blocks. Use the Pomodoro method or strict timers.

Block 1 (2 hrs): Planning & setup — create branch, update README, sketch quick wireframe.
Block 2 (2 hrs): Implementation — code a single screen or endpoint; aim for a green test.
Block 3 (2 hrs): Review & deploy — merge to main, run CI, deploy preview, log next tasks.

Simple code snippets to bootstrap

Minimal serverless function (Node/Edge style) for /recommendations:

export async function handler(event) {
  const { vibe = 'any', lat, lng } = event.queryStringParameters || {};
  // Very small rule based scorer
  const restaurants = loadRestaurants(); // local JSON or DB
  const results = restaurants
    .map(r => ({...r, score: score(r, vibe, lat, lng)}))
    .sort((a,b) => b.score - a.score)
    .slice(0,5);
  return {
    statusCode: 200,
    body: JSON.stringify({ recommendations: results })
  }
}

Simple React hook to fetch recommendations and keep URL shareable:

import { useState, useEffect } from 'react';

export function useRecommendations(vibe, latLng) {
  const [data, setData] = useState(null);
  useEffect(() => {
    const q = new URLSearchParams({ vibe, ...latLng }).toString();
    fetch(`/api/recommendations?${q}`)
      .then(r => r.json())
      .then(setData)
      .catch(() => setData({ error: true }));
  }, [vibe, latLng]);
  return data;
}

Measuring success and learning outcomes

For student projects the goal is not only deployment but meaningful learning. Track these metrics:

Time to first deploy: target ≤ 7 days
User flow completion: % of testers who get 3 recommendations
Feedback loop: # qualitative comments from testers
Iteration velocity: features shipped after launch

Common pitfalls and how Rebecca avoided them

Students often fail because they overbuild, ignore deployment, or avoid feedback. Rebecca avoided these traps by:

Strict scoping: she shipped a single core flow first.
Using AI for scaffolding but verifying the logic herself (trust, but verify).
Prioritizing shareability: a short URL and a demo video made feedback fast.

"Once vibe‑coding apps emerged, I started hearing about people with no tech backgrounds successfully building their own apps." — Rebecca Yu (as quoted in TechCrunch)

Advanced strategies (for weeks 2–4)

After your week‑one micro‑app, consider these next moves that mirror industry trends in 2026:

Introduce private LLMs for personalization while respecting privacy (on‑edge inference).
Move to a component‑based design system so future projects reuse UI patterns.
Open a small public API so classmates can build simple integrations (gamification, calendar sync).
Use A/B tests to measure which recommendation explanation boosts trust.

Templates you can copy right now

We’ve packaged three files you can clone and adapt in under 10 minutes: README, API contract, and a serverless function scaffold. Copy them into your repo and start Day 1 immediately.

Checklist recap — one minute to remember

Pick one small, well‑defined problem
Timebox seven days with daily deliverables
Ship a shareable demo on Day 7
Collect and act on feedback

Why teachers and mentors should adopt this sprint

This seven‑day format is perfect for classroom sprints and mentorship programs. It enforces rapid decision‑making, demonstrates CI/CD basics, and produces tangible portfolio work. For mentors: provide checkpoints at end of Days 2, 4, and 7; for teachers: grade on process and outcomes equally.

Final takeaways

Rebecca’s build is not magic — it’s deliberate constraints, modern tooling, and feedback loops. In 2026 the barrier to shipping is lower; the skill is in scoping and iteration. If you follow this deconstructed sprint, you’ll have a working micro‑app, a public repo, and concrete learning outcomes in one week.

Call to action

Ready to replicate Rebecca’s sprint? Join our next 7‑day micro‑app challenge: clone the starter repo, follow the day‑by‑day checklist, and submit your demo for feedback. Click to download the printable checklist and templates — or sign up for our mentorship cohort to get daily feedback during the sprint.

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