Build a Weekend 'Dining' Micro‑App with Claude and ChatGPT: A 7‑Day Playbook

Beat decision fatigue: build a dining micro‑app in a weekend with Claude + ChatGPT

Everyone in your group chat asks “Where do you want to eat?” and the thread dies. You want a lightweight app that recommends restaurants based on shared tastes—fast, maintainable, and cheap. This playbook shows how to recreate Rebecca Yu’s seven‑day dining micro‑app pattern in 2026 by combining Claude and ChatGPT, pragmatic web scraping for restaurant data, and low‑friction deployment paths for non‑developers.

Why micro apps matter in 2026

Micro apps — personal, single‑purpose, ephemeral apps — have surged because modern LLMs and no‑code tools let people ship valuable products in days, not months. Anthropic’s Cowork and Claude Code (2025–26) brought agentic workflows to knowledge workers, and OpenAI’s evolving ChatGPT toolset keeps streamlining prompt‑to‑code cycles. The result: individuals can prototype and publish small utilities like a dining recommender without long build cycles.

“When I had a week off before school started, I decided it was the perfect time to finally build my application.” — Rebecca Yu, on vibe‑coding a dining app

What you’ll build — and what this guide delivers

End result after seven days: a functioning dining micro‑app that:

• Collects restaurant data via web scraping or public APIs.
• Runs a small recommendation engine powered by Claude and ChatGPT prompts.
• Is accessible via a simple web UI (no‑code or React) and deployed serverlessly.

Every day includes explicit tasks, code snippets (for devs), and no‑code alternatives (for non‑devs). Benchmarks and production considerations are included so your weekend project stays maintainable.

7‑Day playbook — high level

• Day 1: Define scope, personas, and a minimal data model.
• Day 2: Source restaurant data — choose scraping vs APIs.
• Day 3: Build a resilient scraper pipeline (or configure a scraping service).
• Day 4: Design the LLM prompts and recommendation logic.
• Day 5: Rapid UI prototype — no‑code or lightweight React/Vue.
• Day 6: Integrations, testing, legal & anti‑abuse controls.
• Day 7: Deploy, monitor, and prepare for iterations.

Day 1 — Scope, personas, and data model (2–4 hours)

Cut scope ruthlessly. A micro app succeeds when it does one thing well. Define:

• Primary user: you + 3 friends (fast decisions).
• Core action: “Suggest 3 restaurants for this group now.”
• Must‑have filters: cuisine, price level, max distance, open now.

Create a minimal data model—store only fields you need:

• id, name, address, lat/lon, cuisine tags, price_level, rating, hours, source_url, last_updated

For non‑devs: sketch this as an Airtable base. For devs: create a simple JSON schema or SQLite table.

Day 2 — Choose data sources: scraping vs public APIs (2–6 hours)

Options:

• Public APIs (recommended when available): Google Places, Yelp Fusion, Foursquare. Pros: stability, structured data, TOS clarity. Cons: API keys, quotas, costs.
• Web scraping: Use when APIs are incomplete or you need niche sources (blogs, local guides). Pros: broader coverage. Cons: rate limits, CAPTCHAs, legal caution.
• Hybrid: Use APIs for backbone, scrape to enrich specific fields (menus, opening specials).

Decision checklist:

• Is the data available via a well‑documented API? Use it first.
• Can you comply with the site’s robots.txt and Terms of Service? If not, don’t proceed.
• Estimate cost: scraping scale vs API billing.

Day 3 — Build the scraper pipeline (dev) or configure a scraping service (no‑code)

Goal: collect a seed dataset (200–1000 restaurants depending on city size).

Dev path: Playwright + Python + simple parser

Use Playwright for robust rendering of JS sites, with a proxy pool and backoff. Below is a compact pattern for scraping restaurant list pages and writing CSV. Replace proxies and selectors to match your target site.

# Python (Playwright) - simplified
from playwright.sync_api import sync_playwright
import csv, time

selectors = {
  'item': '.restaurant-card',
  'name': '.name',
  'rating': '.rating',
  'cuisine': '.tags',
}

with sync_playwright() as p:
  browser = p.chromium.launch(headless=True)
  page = browser.new_page()
  page.goto('https://example.com/restaurants')
  time.sleep(1)
  items = page.query_selector_all(selectors['item'])
  rows = []
  for it in items:
    name = it.query_selector(selectors['name']).inner_text().strip()
    rating = it.query_selector(selectors['rating']).inner_text().strip()
    cuisine = it.query_selector(selectors['cuisine']).inner_text().strip()
    rows.append((name, rating, cuisine))

  with open('restaurants.csv', 'w', newline='', encoding='utf-8') as f:
    writer = csv.writer(f)
    writer.writerow(['name','rating','cuisine'])
    writer.writerows(rows)

  browser.close()

Hardening tips:

• Rotate user agents and IPs; use a reputable proxy pool for scale.
• Respect rate limits—exponential backoff and random jitter.
• Cache results and only re‑scrape changed pages (ETags, last_modified).
• Use headless browsers only when necessary; prefer API endpoints or static HTML when possible.

No‑code path: use a scraping service

If you’re not a developer, pick a scraping service (Apify, ScrapingBee, Octoparse, webscraper.app) or a managed scraping API. Configure a task to export results to Google Sheets, Airtable, or S3. Typical workflow:

1. Create a job with the target URL and CSS/XPath selectors.
2. Schedule a daily or weekly run to refresh data.
3. Send output to an Airtable base or Google Sheet for the app to consume.

Day 4 — LLM prompts and the recommendation engine (3–6 hours)

Here you define how Claude and ChatGPT convert preferences into ranked lists. Keep the model off critical workflows—use it for scoring and natural language UX.

Approach

1) Create a deterministic scoring function (weightable fields). 2) Use LLMs for soft signals: vibe matching, natural language clarifications, and descriptive snippets.

Scoring example (simple):

• Distance: 30%
• Price match: 20%
• Cuisine match: 25%
• Rating: 15%
• Open now: 10%

Sample prompt — ranking helper

System: You are a restaurant ranking assistant. Input: a list of restaurants with attributes (name, cuisine tags, price_level, rating, distance_km, open_now). Output: a JSON array sorted by score. Use weights: distance 30, price 20, cuisine 25, rating 15, open_now 10. Score range 0-100.

User: [ ...restaurants...]

Use Claude for multi‑turn clarifications (e.g., when users say “we want a cozy place that’s not too loud”) and ChatGPT for generating the frontend text snippets and microcopy. In 2026 both models support richer tool integrations and can call lightweight webhooks (agentic flows), so consider using them to ask quick clarifying questions to users via the UI.

Day 5 — Rapid UI prototype: no‑code vs lightweight dev

Pick one path based on skill level and time.

No‑code path (fastest: hours)

• Airtable as the DB. Create a base with your restaurant table and fields.
• Glide or Softr to build a simple front end: search controls for cuisine, price, distance; results list card with CTA to open maps or call.
• Zapier/Make as glue: trigger recalculations, ask the model via webhook to generate the “why this?” blurb when a user taps a recommendation.

Result: a working web app accessible via a short URL. No code required.

Developer path (React + serverless function)

Use Vite + React for a minimal app. Expose a serverless endpoint (/api/recommend) that pulls filtered restaurants from your Airtable/DB and calls the LLM for ranking/snippets.

// Pseudocode (Node serverless)
app.post('/api/recommend', async (req, res) => {
  const {prefs} = req.body
  const data = await fetchRestaurants(prefs)
  const ranked = await callLLMForRanking(data, prefs)
  res.json(ranked)
})

Local benchmark: small dataset (500 records) queries under 200ms for DB filter, LLM call latency dominates. Cache LLM results for repeated queries.

Day 6 — Testing, compliance, and anti‑abuse

Before you launch to friends, validate these items:

• Legal: Confirm scraping activity aligns with robots.txt and site TOS; prefer public APIs when in doubt. Document your data sources and retention policy.
• Privacy: Collect the minimum personal data. If you store user preferences, label them and provide an option to delete data.
• Rate limits & CAPTCHAs: If scraping, add exponential backoff and CAPTCHA handling (service or manual). For scale, use a scraping API that handles CAPTCHAs/residential IPs.
• UX testing: Run 5 quick usability sessions. Key checks: clarity of recommendation, filter discoverability, latency under 1–2s for common flows.
• Safety: LLM hallucinations—never let the model assert unverifiable facts (e.g., “will be open after hours”). Always attach source_url and last_updated timestamps to results.

Day 7 — Deploy, monitor, and plan iterations

Deployment options:

• Vercel / Netlify: Ideal for React + serverless functions and free hobby tiers.
• Glide / Bubble: Publish to a shareable URL or mobile wrapper. Great for non‑devs.
• Desktop preview: Anthropic Cowork (2025–26) and similar agentic tools let you run local workflows for prototypes; useful for demos and personal automation.

Post‑deploy checklist:

• Enable logging & error tracking (Sentry or simple CloudWatch/Stackdriver).
• Set up uptime alerts and quota monitoring for LLM and API keys.
• Schedule automatic data refresh (cron) and incremental scrapes every 24–72 hours depending on volatility.

Operational best practices — keep the micro app maintainable

Caching: Cache LLM outputs (snippets, ranked lists) for short windows (1–24 hours) to reduce cost and latency. Use Redis or a hosted cache for serverless apps.

Idempotent scrapes: Save source_url + last_updated; only fetch details when changed. It saves bandwidth and reduces risk of tripping anti‑scraping defenses.

Proxy & CAPTCHA strategy: For scraping at small scale, rotate a small residential proxy pool and throttle requests. For larger scale, use a managed scraping API that handles CAPTCHA solving and dynamic rendering.

Cost control: Monitor LLM token usage—prompt engineering reduces tokens. Use Claude for longer contextual reasoning flows and ChatGPT for UI text where appropriate; compare costs and latencies.

Sample prompts and implementation snippets

1) Quick prompt for generating appetizer copy (ChatGPT)

Prompt: "Write a one‑line justification for recommending 'Luna Pasta' to a group that likes Italian food, is price sensitive, and prefers casual spots. Keep it < 100 chars."

2) System prompt for Claude to clarify ambiguous user intent

System: You are an assistant that asks clarifying questions to narrow dining preferences. If the user's preference is ambiguous, ask for 1 specific clarification.
User: "Some of us want sushi but others want cheap eats."
Assistant: "Do you prefer staying within a 3km radius, or is cuisine priority over distance?"

Benchmarks & expectations (practical numbers)

• Initial scrape (200–500 records) with Playwright on a single cloud VM: 5–20 minutes depending on rendering.
• Serverless recommendation latency: DB filter 50–200ms; LLM call 300–1500ms depending on model and network.
• LLM cost: vary by provider and model—expect $0.01–$0.10 per complex recommendation call for full context. Cache aggressively.

These figures depend on model choice and provider. Do a small A/B cost test in your first 24 hours to estimate monthly run costs.

Advanced strategies & 2026 trends

As we move through 2026, these patterns will shape micro app development:

• Agentic assistants: Tools like Claude Code and Cowork let non‑developers run scripted pipelines and local file integrations—useful for automating dataset refreshes without writing orchestration code.
• Model ensembles: Combine Claude for contextual understanding and a faster, cheaper ChatGPT variant for microcopy to balance cost and quality.
• Vector personalization: Store user preference embeddings to return personalized results using nearest‑neighbor search (useful as the user base grows beyond the creator + friends).
• On‑device inference: For privacy‑sensitive micro apps, consider running smaller models locally for preference handling while using cloud LLMs for heavy lifting.

Checklist — ship on a weekend

• Day 1: Airtable or JSON schema created.
• Day 2: Data source chosen and sample exported (CSV).
• Day 3: Scraper configured or scraping service task created.
• Day 4: LLM prompts defined and tested on three sample queries.
• Day 5: UI prototype published (Glide or React + Vercel).
• Day 6: Legal check, CAPTCHA/rate handling, basic UX test.
• Day 7: Deployed, monitoring enabled, invite 3 friends to test.

Common pitfalls and how to avoid them

• Over‑engineering: Resist adding social login or full user profile in week one—get to recommendations quickly.
• Relying on one data source: Backfill from at least two sources to avoid sudden outages or API changes.
• Ignoring TOS: Scraping without permission invites risk—document decisions and prefer APIs when in doubt.
• No caching: LLM calls for identical requests cost money. Cache aggressively for identical preference sets.

Actionable takeaways

• Define a tight MVP scope and data model on Day 1.
• Prefer structured APIs when possible; use scraping services if needed.
• Use Claude for multi‑turn clarification and ChatGPT for UI copy; cache outputs.
• Deploy on Vercel/Netlify or publish via Glide for the fastest path to friends.
• Monitor token usage and scraping volume to keep costs predictable.

Final thoughts & next steps

Micro apps are low‑risk, high‑learning projects. By combining Claude and ChatGPT, pragmatic scraping, and no‑code deployment, you can recreate Rebecca Yu’s Where2Eat pattern in a single week. The key is incrementalism: ship a basic recommender, then iterate with real usage data.

Ready to build?

If you want a starter kit: pick one path now. For non‑developers, create an Airtable and configure Glide today. For developers, fork a lightweight Vite + serverless template, wire in Playwright or a managed scraper, and test your LLM prompts with small datasets.

Start this weekend: map one city block of restaurants, implement the scoring prompt, and share a link with your first 3 testers. Iterate from feedback—most useful improvements come after real use.

Call to action: Build your weekend dining micro‑app, invite three friends, and ship a first version by Sunday night. If you want a checklist or starter prompts exported to Airtable, sign up for a ready‑to‑use starter pack or clone a starter repo to skip the initial setup and get straight to recommendations.

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