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Case Study: A Week to Production—How a Small Team Built and Scaled a Dining Micro App

UUnknown

2026-02-11

10 min read

A practical case study on how Rebecca Yu shipped Where2Eat in a week — scoping, stack, monitoring, and scaling advice for teams.

Hook — If you need production code, not a half-baked demo

Every engineering team I talk to has the same pain: you can prototype a feature in an afternoon, but turning that prototype into a reliable, monitored dining micro app that your colleagues actually trust takes weeks. You need clear scoping, reliable infra, observability that catches problems before users notice, and a scaling plan that doesn’t blow the budget. This case study reconstructs how Rebecca Yu shipped Where2Eat — a dining micro app built in a week — and how an engineering team can copy that lifecycle to go from MVP to a small, stable user base.

Executive summary — What we rebuilt from Rebecca’s week

Rebecca built a focused dining recommendation web app in seven days using LLM-assisted “vibe coding.” We’ll reconstruct that lifecycle from a product-engineering lens so teams can ship similar micro apps in a short timeline while avoiding common pitfalls.

Primary goal: Help a small friend group pick a restaurant quickly.
Timebox: 7 days to a production-ready MVP.
Core tech pattern: Static frontend + an edge-first API for recommendations, small managed DB, serverless hosting.
Outcome: Reliable app for small user base (10–200 users/day) with monitoring, rate limits and cost controls.

Scoping: prioritize ruthlessly for an MVP

The secret to a one-week delivery is a razor-sharp scope. Rebecca focused on one user story: group decision-making for where to eat. If your team treats this as a product lifecycle exercise, use the same discipline.

Define the single core use case

Avoid multi-feature creep. For Where2Eat the team locked the scope:

Users can create a session (a short-lived room or link).
Participants vote or “vibe” preferences (cuisine, budget, distance).
The app returns ranked suggestions.
Shareable link to the final choice.

Prioritize acceptance criteria

Make acceptance criteria testable and measurable. Example for day-one shipping:

End-to-end time from session creation to recommendation ≤ 3s for cached results.
Successful recommendation for 90% of input combinations.
Error rate < 1% in first 1,000 sessions.
Cost < $50/week at 200 daily sessions.

Design decisions and dev tools (what to pick on day zero)

Rebecca’s approach mirrors the 2024–2026 trend: pair human engineers with LLM copilots for fast code generation, but keep the architecture simple and production-aware. By late 2025, edge runtimes, serverless databases, and standardized observability made this pattern repeatable.

Recommended stack for a week-to-prod micro app

Frontend: Next.js or SvelteKit (static-first pages, client hydration for interactivity).
API / Edge: Vercel Edge Functions or Cloudflare Workers for low-latency recommendations.
DB: Managed Postgres (Supabase) or serverless MySQL (PlanetScale) for shared state; or SQLite for single-instance prototypes.
Cache: Redis-compatible (Upstash) for quick TTL caches of results and request dedupe.
Auth: Magic links (Clerk, Supabase Auth) to avoid password build time.
LLM: OpenAI/Anthropic API for suggestion generation — but treat as an expensive backend and add caching.
CI/CD: Vercel/GitHub Actions — auto-deploy main to production.
Observability: Sentry (errors & RUM), OpenTelemetry traces exported to Grafana/Datadog or Honeycomb.

Why edge + cache?

Edge functions cut cold-start latency and deliver results near users. But using an LLM at the edge is expensive and often impossible due to runtime size; instead, run small orchestration at the edge and call a centralized LLM backend (or use model + cache). The pattern: edge → orchestrator → cache → LLM. Cache first, LLM second.

Prototype to production: an 8-step playbook

Day 0: Create a GitHub repo, add a README with scope and acceptance criteria.
Day 1: Build the static UI (session creation, preferences). Use component library (Tailwind + Radix) to ship fast.
Day 2: Implement a simple API route that returns deterministic results from a small seeded dataset.
Day 3: Add LLM-based suggestion generator behind the API (feature-flag it).
Day 4: Add auth and session sharing, basic analytics (simple events to Postgres or Mixpanel).
Day 5: Wire observability (Sentry + basic custom metrics), add tests, and set up CI/CD.
Day 6: Manual & automated smoke tests; deploy to production; invite first users.
Day 7: Iterate based on feedback and monitor key metrics for regressions.

Example: edge API pseudocode

// Edge function (pseudo-JS)
export default async function handler(req) {
  const { sessionId } = await req.json();
  // Quick cache lookup
  const cacheKey = `rec:${sessionId}`;
  const cached = await cache.get(cacheKey);
  if (cached) return new Response(JSON.stringify(cached));

  // Orchestrate user preferences + DB lookups
  const prefs = await db.getSessionPrefs(sessionId);
  const candidateList = await db.queryNearbyRestaurants(prefs);

  // Call a centralized recommender (LLM or deterministic service)
  const recs = await fetch(`${ORCHESTRATOR_URL}/rank`, {
    method: 'POST',
    body: JSON.stringify({ prefs, candidateList }),
  }).then(r => r.json());

  // Cache and return
  await cache.set(cacheKey, recs, { ttl: 60 * 5 });
  return new Response(JSON.stringify(recs));
}

Infrastructure & deployment: keep ops simple

Micro apps succeed when infrastructure is predictable and low-cost. Rebecca’s week-focused approach uses managed services and autoscaling serverless components so the team can focus on product.

Hosting options

Vercel / Netlify: Best for tight Next.js workflows with instant deployments.
Cloudflare Workers + Pages: Ultra-low latency edge compute; pair with D1 for SQL-like storage for small apps.
Render / Fly / Railway: If you need Docker-based services quickly.

Database and data model

For Where2Eat, use a simple model:

tables:
- sessions (id, owner_id, created_at, expires_at)
- participants (session_id, user_id, preferences JSON)
- restaurants (id, name, geo, tags JSON)
- events (session_id, event_type, payload JSON, ts)

For 10–200 daily sessions, a managed Postgres is fine. If you need single-file simplicity for local dev, use SQLite and switch to Supabase/PlanetScale for production.

CI/CD and deployments

Hook GitHub to Vercel for atomic previews. For teams who want more control, a simple GitHub Actions workflow to build, run tests, and deploy to a container host is sufficient.

name: CI
on: [push]
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Setup Node
        uses: actions/setup-node@v4
      - run: npm ci && npm run build && npm test
      - name: Deploy
        uses: vercel/action@v2
        with:
          vercel-token: ${{ secrets.VERCEL_TOKEN }}
          vercel-org-id: ${{ secrets.VERCEL_ORG_ID }}
          vercel-project-id: ${{ secrets.VERCEL_PROJECT_ID }}

Monitoring and observability: instrument before you need it

Teams often skimp on monitoring until users report problems. For micro apps, lightweight observability is essential: errors, performance metrics, and behavioral analytics.

What to instrument on day one

Errors: Sentry or an equivalent — capture unhandled exceptions, stack traces, and user context.
Latency: Track p50/p95/p99 for API endpoints and LLM response times.
Business metrics: Sessions created, recommendations delivered, share clicks.
Cost metrics: LLM tokens per session, number of LLM calls, and cache hit rate.

OpenTelemetry + tracing

By 2026, OpenTelemetry is the de facto way to capture distributed traces. Add a small SDK to the edge function and the orchestrator service so you can correlate user sessions to slow LLM calls or DB queries.

Alerts and SLOs

Set simple alerts that matter:

Error rate > 1% over 5 minutes
p95 latency > 2s for recommendations
LLM spend forecast > 120% of budget

Scaling from prototype to a small user base

Scaling is mostly about patterns, not raw capacity. For Where2Eat’s user profile (friends, small groups), follow these tactics to scale predictably.

Caching & batching

Cache recommendations per session with a short TTL (5–15 minutes).
Batch LLM calls: if multiple similar requests arrive within 1s, collapse into a single LLM invocation.
Precompute popular combinations during off-peak hours.

Guardrails

Rate limit per IP/session to avoid abuse (Cloudflare or API Gateway).
Quota LLM usage per user to control cost.
Graceful fallback: deterministic heuristics if LLM is unavailable.

Cost-control pattern

LLM calls are the dominant variable cost. Use this formula:

Estimated weekly cost = (avg tokens per call * calls per session * sessions per week * token price) + infra.

Measure tokens early and add caching to keep cost predictable. For broader analysis of unexpected business losses and cost forecasting, see cost impact analysis.

Product lifecycle: iterate with measured experiments

Once you have a small, stable user base, adopt an iterative cycle.

Collect qualitative feedback directly in the app (a one-click “Was this helpful?”).
Instrument experiments: A/B test prompt variants or rule weights against success metrics (did participants reach consensus?).
Run weekly retrospectives and prioritize one small experiment per sprint.

Security, privacy and compliance

Even small apps can leak sensitive data.

Minimize PII stored; prefer short-lived session IDs.
Use tokenized secrets and never commit API keys — GitHub secrets + environment variables.
Review your LLM prompt content — strip or anonymize PII before sending to a third-party model.
Add a clear privacy notice if storing location data; consider opt-in consent flows for EU users (GDPR) and state-specific requirements (e.g., CCPA/CPRA).

If you plan to offer data or content to external models or training pipelines, read the developer guide for compliant training data and design model access and retention policies accordingly.

Lessons learned — practical takeaways

“Vibe coding” plus modern serverless tooling gives you speed; disciplined scoping and ops give you production reliability.

Scope tightly — 1 user journey, 4–6 endpoints.
Instrument day one — errors, latency, business events, and cost.
Cache first — TTLs are your friend; LLMs are expensive and slow without caching.
Use managed services to avoid ops time sinks, but know the limits (cold starts, quotas).
Safety net — deterministic fallback for external service failures.

Appendix — Example monitoring snippet (Sentry + Next.js)

// _app.js (Next.js)
import * as Sentry from '@sentry/nextjs';

Sentry.init({
  dsn: process.env.SENTRY_DSN,
  tracesSampleRate: 0.1, // increase as needed
});

export default function App({ Component, pageProps }) {
  return ;
}

2026 trends that matter to micro apps

When Rebecca built Where2Eat, the “micro app” trend was rising. By 2026 those trends matured and matter to teams building small production apps:

Edge-first ops: Edge runtimes and regional caches are standard — use them to reduce p95 latency. Read more about edge-first approaches and live signals in production here.
LLM economics: New lightweight open models in late 2025 reduced cost-per-call but made hybrid caching and local reranking a best practice — and have implications for AI partnerships and vendor strategy.
Observability standardization: OpenTelemetry traces are widely supported; instrumenting traces is low-friction and high-value.
Composability: Auth, payments, and analytics are now plug-and-play — prefer composable managed services to reduce time-to-prod. If you need to design robust billing and model governance, see notes on architecting model audit trails.

Common pitfalls and how to avoid them

Over-architecting: Don’t introduce microservices on day one; start monolith + serverless functions.
Ignoring cost: Measure tokens and add budget alerts before inviting users.
Lack of observability: If you can’t answer “why did this session fail?” within minutes, add tracing and events.
Feature creep: Lock core flow until you validate metrics.

Final checklist — Ship a dining micro app in one week

Define the one core user story and measurable acceptance criteria.
Choose a simple stack: Static frontend + edge API + managed DB.
Implement deterministic fallback before adding LLMs.
Instrument Sentry, metrics, and an events pipeline (Mixpanel/Postgres).
Cache recommendations and batch LLM calls.
Deploy with continuous delivery and test smoke paths.
Set alerts for errors, latency, and spend.

Closing — Build fast, operate reliably

Rebecca’s Where2Eat demonstrates what’s possible when a focused scope meets modern tools. As of 2026, teams can ship micro apps quickly by combining local LLM experiments, edge runtimes, managed services, LLM-assisted coding, and a disciplined product lifecycle. The trick isn’t to code faster — it’s to plan and instrument smarter so that fast prototypes become useful, trustworthy products.

Ready to replicate this pattern? Grab the downloadable production checklist and a starter template (Next.js + edge API + Sentry) on javascripts.store to cut your time to production in half.

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