Case Study: Building a Dining Picker Micro App from Idea to Launch

Hook: Ship a useful product in a week — seriously

Decision fatigue is a real content friction point: group chats become a battleground of indecision, and creators lose momentum building bespoke tools because of technical friction. In 2026, that problem is a feature opportunity. This case study reconstructs how Rebecca Yu built Where2Eat — a dining picker micro app — in seven days using modern AI-assisted tooling. If you’re a publisher, creator, or product lead wondering how to ship a focused micro app quickly, this stepwise rebuild will give you prompts, tech choices, launch metrics, and repeatable templates.

Why this matters to creators and publishers in 2026

Micro apps are the new content products: lightweight, targeted, and profitable when they solve a single user pain. By 2026, several trends make micro apps especially powerful for creators:

• AI-assisted development: LLMs and multimodal assistants can generate UI code, API integrations, and tests in minutes.
• Composable backend services: Managed databases (
• Privacy-first analytics: Lightweight trackers (Plausible, PostHog) plus event schemas let creators measure product-market fit quickly.
• Micro business models: Paid invites, sponsorships, or newsletter cross-sells make micro apps viable revenue lines.

What we’re reconstructing

This article is a step-by-step reconstruction of Rebecca Yu’s project based on her public posts and interviews. I’ve filled technical and metric gaps with realistic, defensible assumptions and modern 2026 tooling. Where I estimate values, I label them clearly. Treat this as an exact blueprint you can copy, adapt, and ship.

Outcome (most important): launch metrics & signals

High-level reconstructed launch results — important because creators want to know if this effort is worth it:

• Build time: 7 days (prototype to deployed MVP)
• Initial closed beta users: ~25 friends for 2–3 days
• First public week: ~150 sign-ups (reconstructed)
• Key engagement: ~60% of sign-ups used recommendations within first session
• Retention (7-day): ~28% (reconstructed)
• Top conversion actions: share invite, save favorites, create group link

Step 1 — Define the scope, user flow, and North Star

Rebecca’s problem statement was simple: remove decision friction about where to eat with friends. For a micro app, scope discipline is everything. The minimal valuable experience (MVE) should answer:

1. How do users express preferences? (quick toggles, tags)
2. How do users generate suggestions? (single-click recommend)
3. How do users act on suggestions? (share link, open map, save)

Pick one North Star metric for fast learning. For Where2Eat it was: percentage of groups that choose a restaurant using the app within a session. That captures both utility and conversion in a single signal.

Step 2 — Tech choices that enable a 7-day build

To ship fast you need predictable, integrated choices. Here’s the stack reconstructed from public details and contemporary best practices in 2026:

• Frontend: SvelteKit or Next.js (Edge rendering) — pick one you or your AI helper can scaffold quickly.
  • Why: fast hydration, small bundles, good developer DX for iterative UIs.
• Hosting: Vercel or Netlify Edge — instant deploys and serverless functions.
• Database/Auth: Supabase for instant Postgres + auth + Realtime.
  • Why: simple row-based storage for users, groups, favorites. Supabase Edge Functions for small server hooks.
• Places data: Foursquare Places or Yelp Fusion (Places API) to source restaurant metadata and distance.
• AI layer: Anthropic/Claude 3 or OpenAI GPT-4o for ranking and converting user preferences to query embeddings.
  • Use an embeddings + vector DB (e.g., Pinecone, Milvus, or Supabase Vector) for semantic matching of preferences to restaurants.
• Analytics: PostHog (self-host or cloud) or Plausible for event tracking and funnel analysis.
• Optional: Stripe for a paid “private beta” or tipping model.

Step 3 — The AI-assisted development workflow

Vibe-coding (or AI-assisted coding) is a collaboration: you provide intent, constraints, and rapid feedback. Below are the reconstructed prompts and output used at each milestone. Save these as templates for your own micro app builds.

Initial product prompt (ideation → wireframe)

System: You’re a product designer who builds mobile-first web apps in 1 week. Output a 4-screen wireframe and short interaction flow for a dining recommendation app.
User: Build a dining picker for small friend groups. Inputs: preferences (cuisine tags), location, price range. Outputs: 5 ranked restaurant suggestions and a shareable group link. Keep UI minimal and make offline fallback. 

Expected AI output: four screens (Landing, Preference Pick, Results + Carousel, Share Link), and JSON describing actions and fields. Use that to scaffold pages in SvelteKit/Next.js.

UI component prompt (generate code)

System: You’re a front-end engineer. Provide a Svelte component for a multi-select preference picker (tags) with keyboard support and accessible labels.
User: Create a component named TagPicker.svelte: props: selectedTags, availableTags; events: change. Include minimal style classes. 

AI returns a ready-to-paste component. Always review and run tests, but this speeds development dramatically.

Backend + API prompt (serverless function)

System: You’re a backend engineer familiar with Supabase and serverless. Create an edge function that receives user preferences, queries Foursquare Places API, ranks results using an embeddings similarity call to GPT-4o or Claude, and returns top 5. Include pseudocode for API keys and caching logic.

This yields an actionable function you can adapt — remember to secure keys with environment variables and enable request throttling.

Test & QA prompts

System: You’re an automated QA assistant. Generate 10 end-to-end Cypress tests for the dining picker covering preference selection, results display, share link creation, and error states like location denied.

Use these tests to validate the app in CI before sharing with friends.

Step 4 — Data model & events (what to track)

Plan events before launch. Minimal event schema to prove product-market fit:

• user_signed_up (auth.method)
• preferences_saved (tags[])
• recommendations_requested (group_id, latency_ms)
• recommendation_selected (restaurant_id, position_rank)
• share_link_created (group_id)
• invite_accepted (group_id)

Hook these into PostHog or Plausible. Build a simple funnel: recommendations_requested → recommendation_selected → invite_accepted. Track conversion rates and time to decision.

Step 5 — Quick UX details that boost conversion

Small UX choices matter in micro apps. Rebecca’s Where2Eat focused on low-friction interactions:

• One-click preferences: present 8–12 common tags, plus an “anything” option.
• Instant results: prefetch place data while the user types location to keep perceived latency low.
• Group link: a single URL others can open to join the voting/choice session.
• Map + directions CTA: deep link to Maps or Google Maps to reduce drop-off from suggestion to visit.

Step 6 — Deployment checklist

1. Environment variables configured (API keys, DB URL)
2. Auth and session expiry policies
3. Rate limits on Places API and AI calls
4. Privacy notice for location data
5. Analytics events instrumented
6. CI pipeline with basic E2E tests

Step 7 — Launch strategy & early growth

Rebecca launched to friends first. That’s the fastest way to validate assumptions with real behavior. Here’s a repeatable creator launch plan:

1. Closed beta among 10–30 friends to validate flows and measure North Star.
2. Invite-only week: use a gated link shared in your newsletter or social to create scarcity.
3. Public soft-launch: post a short explainer on your platform (Substack, Twitter/X, Threads) with a CTA to try the app.
4. Email follow-ups to users who created a group but didn’t select a suggestion.

Reconstructed launch metrics and interpretation

Based on Rebecca’s public account and realistic assumptions, these are the reconstructed metrics and what they imply:

• Sign-ups vs engaged users: ~150 sign-ups in week one with ~60% session engagement tells us the product is immediately useful for a core set of users.
• Retention: ~28% 7-day retention signals the app solves recurring pain for a subset (group organizers); aim to push that to 40% with added integrations (reservations, calendar).
• Time to pick: median session reduced decision time by 40% (reconstructed). This is a defensible North Star for dining apps.
• Viral coefficient: sharing features (group link) gave a small viral boost — about 0.2 invites per active user in week one (reconstructed). Improve via incentives.

Product-market fit signals to watch

For creators, product-market fit (PMF) doesn’t need millions of users. Watch these 2026-era PMF signals:

• Repeated organic invites from users (quality of referrals matters more than count).
• Decreasing session latency and increasing completion rate for the North Star funnel.
• Users asking for features that map to monetizable experiences (e.g., reservations, curated lists).
• Newsletter cross-traffic converting at >=10% for engaged subscribers.

Lessons learned & tactical takeaways for creators and publishers

From this reconstruction, distilled lessons you can apply immediately:

• Scope ruthlessly: A single use-case and one clear North Star will get you to launch faster than feature bloat.
• Instrument everything: The value of your micro app is proven by event conversions more than download counts.
• Leverage composable services: Plug-and-play APIs and managed databases let you focus on product instead of infra.
• Use AI for boring work: Generate UI components, serverless templates, and tests with LLMs — but always code review and secure secrets.
• Monetize later: Prove utility first; add monetization like paid private groups, sponsorships, or local partner deals after you validate PMF.

Advanced strategies for scale (2026 forward)

If the micro app finds traction, here are next-level moves influenced by late 2025/early 2026 trends:

• Personalized ranking with user embeddings: Store a small preference embedding per user and personalize results without heavy recompute.
• Local partner integration: Integrate with restaurant reservation APIs or local delivery partners to capture additional revenue.
• Offline-first progressive web app: Make the dining picker work when signal is weak (restaurants often have spotty service).
• Edge AI for latency: Use model inference at the edge (serverless model endpoints) to reduce round trips for recommendation ranking.

Checklist: Copy-and-paste prompts and templates

Use these as starting points for your next micro app:

• Product wireframe prompt (see above)
• TagPicker.svelte prompt (UI generation)
• Edge function prompt (backend for ranking)
• PostHog event schema JSON (simple mapping of events)

Risks & mitigation

Micro apps come with specific risks. Be pragmatic:

• API rate limits: Cache popular queries and set sensible backoff.
• Privacy: Be transparent about location usage and store minimal PII.
• Model hallucination: When AI generates ranking rationale or descriptions, validate against known data fields (rating, distance).

Final verdict: Who should build a dining micro app?

If you’re a creator with an audience of local users, a publisher with a city guide, or an influencer who organizes meetups — build this. It’s a high-value, low-cost product that deepens audience engagement and creates a new conversion funnel from readers to app users.

“Once vibe-coding apps emerged, I started hearing about people with no tech backgrounds successfully building their own apps.” — Rebecca Yu (reconstructed context)

Next steps: a 7-day sprint plan you can follow

1. Day 1: Define scope, wireframes, and North Star. Prime your LLM with product prompts.
2. Day 2: Scaffold frontend and TagPicker component. Build auth and basic routes.
3. Day 3: Implement backend function to call Places API and return results.
4. Day 4: Hook AI ranking (embeddings + vector DB) and caching.
5. Day 5: Instrument analytics and add share link flow.
6. Day 6: End-to-end tests, privacy policy, and deploy to Vercel/Netlify.
7. Day 7: Closed beta with 10–30 friends, collect feedback, iterate.

Call to action

If you’re ready to ship your own version of a dining picker micro app, start with the wireframe prompt and the TagPicker component prompts above. Want a tailored sprint plan for your audience? Book a free strategy session with our team at compose.website (link in bio) — we’ll map your content audience to a micro app idea in 30 minutes and supply the exact prompts and stack to get you to beta in 7 days.

Related Reading

• Choosing Between Buying and Building Micro Apps: A Cost-and-Risk Framework
• How to Use Micro-Apps for In-Park Wayfinding and Real-Time Offers
• Beginner’s Guide to Launching Newsletters with Compose.page
• The Evolution of Lightweight Auth UIs in 2026
• Henry Walsh’s Large-Scale Worlds: Inside the Painter’s Upcoming Year
• Fallout Shelter IRL: Designing In-Game Events That Mirror Reality TV Challenges
• Live Reaction Jam: Play & Discuss the New Filoni-Era Star Wars Slate on Harmonica
• Taming Toxicity: Two Calm Responses Gamers Can Use in Heated Community Arguments
• The Tech You’ll Actually Use in 2026: Home Gadgets from CES Worth Buying for Your Apartment