No-Code Micro-App + One-Page Site Tutorial: Build a Restaurant Picker in 7 Days

Beat decision fatigue and slow launches: build a restaurant picker micro-app and a conversion-focused one-page site in 7 days

Pain point: You need a fast, user-friendly micro-app that helps groups pick a restaurant, plus a lightweight landing page that converts — but you don't have a dev team, and you can't afford slow iterations. This tutorial shows exactly how to replicate Rebecca Yu’s dining app concept and pair it with a high-converting one-page site using no-code builders, AI-assisted prompts, and simple analytics — delivered in seven days.

Why this matters in 2026

Micro-apps and AI-assisted no-code development matured through late 2024–2025. By early 2026, building single-purpose apps (a.k.a. micro-apps, vibe-coded apps, or personal apps) is mainstream. Tools now support RAG (retrieval-augmented generation), vector search, and edge-hosted serverless functions without traditional backend work. That means you can build a privacy-conscious dining recommender and a conversion-optimized landing page fast — and measure what matters.

“Once vibe-coding apps emerged, I started hearing about people with no tech backgrounds successfully building their own apps,” — Rebecca Yu, on building Where2Eat in seven days.

What you'll ship by Day 7

• A working micro-app that recommends restaurants based on group preferences (Airtable + no-code front end + AI ranker).
• A conversion-optimized one-page site with clear CTA, analytics, and pixel events for marketing.
• A simple chatbot interface that helps users refine choices and share results.
• End-to-end tracking for signups, shares, and recommendation conversions.

Choose the stack (pick one path)

Rapid no-code (fastest)

• Backend/data: Airtable (or Google Sheets)
• Front-end micro-app: Glide or Softr
• AI ranking + prompts: Make (Integromat) or Zapier calling OpenAI/Anthropic
• Landing page: one-page.cloud or Webflow/Carrd — prioritise edge-powered hosting for fast TTFB.
• Analytics: Plausible or GA4 + server-side events

Flexible / more control

• Backend: Airtable or Supabase (with vector DB like Weaviate or Supabase Vector)
• App: Bubble or a static site with edge functions (Vercel / Cloudflare Pages)
• AI + embeddings: OpenAI/Anthropic + vector search
• Landing page: one-page.cloud or Webflow (exported to Cloudflare Pages)

7-day sprint plan (high level)

1. Day 1 — Plan & data model: decide UX, build Airtable base, gather seed restaurants.
2. Day 2 — Basic front end: create a Glide app or Bubble prototype that reads Airtable.
3. Day 3 — Ranking logic + AI prompts: wire Make/Zapier to call the LLM and return ranked results.
4. Day 4 — Chatbot & share: add a conversational UI to refine preferences and a share link.
5. Day 5 — One-page site copy & design: build the landing page, headline, CTA, features, FAQ, and schema markup.
6. Day 6 — Analytics & tracking: integrate Plausible/GA4, set up GTM events, test funnels.
7. Day 7 — Polish & launch: performance tuning, privacy (GDPR/CCPA) checks, publish and run a soft launch.

Day-by-day tasks with actionable steps

Day 1 — Data model & MVP UX

Goal: capture the minimum data to make useful recommendations.

• Create an Airtable base with these tables: Restaurants, Users, Groups, Events.
• Restaurants fields: Name, Cuisine (multi-select), Price level (1–3), Rating (external), Neighborhood, Tags (vegan, kid-friendly), Latitude, Longitude, URL, Short description, Embeddings (for vector search later).
• Groups fields: Group ID, Members (linked to Users), Current preferences (JSON or linked records).

Sample Airtable record (Restaurants):

<strong>Name:</strong> Tasty Noodle
<strong>Cuisine:</strong> Ramen, Japanese
<strong>Price:</strong> 2
<strong>Tags:</strong> late-night, spicy

Day 2 — Build the micro-app front end

Goal: a simple UI to collect preferences and show recommendations.

• Use Glide or Softr: connect your Airtable base. Build screens: Home (join a group), Preferences (cuisine, budget, distance, vibes), Results (ranked list).
• Add actions: Save preferences to the Group record, call an automation webhook to get ranked results.
• Design tips: Keep the hero CTA prominent, use one main action per screen, keep microcopy short and actionable.

Day 3 — AI ranking & prompts (the secret sauce)

Goal: assemble a deterministic, explainable ranking that an LLM can help refine.

Two approaches: a) weighted scoring (simple), b) AI ranking (flexible and conversational).

Weighted scoring (simple, stable)

Compute a score in Airtable or a serverless function:

<strong>Score = (Cuisine_match * 0.4) + (Price_score * 0.2) + (Distance_score * 0.15) + (Rating_score * 0.15) + (Tag_match * 0.1)</strong>

Each component is normalized to 0–1. This is invisible to users and very fast.

AI-assisted ranking (best for nuance)

Use a single webhook (Make or Zapier) that sends the group's preferences and top ~50 candidate restaurants to an LLM to produce a ranked list. This allows context like “we want something quiet and romantic after 9pm” to be respected.

Example prompt (trim and adapt for your provider):

<strong>SYSTEM: You are a polite restaurant recommender. Rank the provided restaurants 1–10 according to the user's preferences. Return JSON with fields: id, score (0-100), short_reason.</strong>

USER: The user group preferences: {"cuisines": ["Italian","Mediterranean"], "max_price": 2, "vibe": "quiet, romantic", "distance_km": 5 }. Here are candidate restaurants: [ {id: 1, name: "A", cuisine: "Italian", tags: ["romantic"], rating: 4.6, price:2}, ... ]

Return only JSON.

Actionable tip: limit candidate restaurants to 30–50 to keep calls cheap and fast. Use an initial filter (price/distance) before calling the LLM — and cache results to reduce repeat inference cost and latency.

Day 4 — Chatbot & share mechanics

Goal: let people refine choices conversationally and share decisions.

• No-code chatbot: embed a chat widget from Tidio, Landbot, or Crisp. Connect to Make/Zapier so when a user asks for a “quiet place”, you call the same LLM ranking webhook and return updated results. Consider how in-ecosystem social features and decentralised discovery (see notes on live social platforms) change share behaviour.
• Share link: generate a short URL (Bitly or your own redirect) containing the chosen restaurant ID and a human-friendly message for social or group chat. Track shares via UTM parameters.

Sample system prompt for the chatbot assistant:

<strong>SYSTEM: You are a concise assistant that helps groups choose a restaurant. When a user asks, confirm 1–2 clarifying details, then return up to 3 ranked suggestions with a one-line reason each.</strong>

Day 5 — One-page site: conversion-first layout

Goal: create a landing page that explains the micro-app, captures signups, and drives downloads/visits.

• Hero: headline addressing the user's pain (decision fatigue). Example: “Where to eat? Let your group decide in 60 seconds.”
• Primary CTA: “Get started — it's free” or “Try the group picker”. Keep button above the fold, visible on mobile.
• Social proof: small testimonial or quote (e.g., “Made dinner decisions in 2 minutes”) to reduce friction.
• How it works: 3-step visual sequence: 1) Create or join a group, 2) Set vibes, 3) Get ranked picks & share.
• FAQ + Privacy: short section explaining data retention and whether results use location data.

SEO & schema tips (important in 2026): include JSON-LD for FAQ and LocalBusiness where appropriate, and serve pre-rendered meta tags for crawlers. WYSIWYG builders and tagging now support head tag editing for faster indexing.

Day 6 — Analytics, events, and conversion tracking

Goal: measure the funnel from landing → app join → recommendation → share.

• Install a lightweight analytics tool (Plausible or Fathom) for privacy-first tracking. Add GA4 if you need richer integrations, but prefer server-side events for conversion data in 2026's cookieless environment.
• Define events: page_view, start_group, submit_preferences, show_recommendations, click_result, share_result.
• Set up UTM campaign templates for marketing channels. Use GTM or one-page.cloud's pixel manager to fire events on button clicks.

Sample event snippet (fires on recommendation click via GTM dataLayer push):

<strong><script>
window.dataLayer = window.dataLayer || [];
function trackRecommendationClick(restaurantId){
  dataLayer.push({
    event: 'recommendation_click',
    restaurantId: restaurantId
  });
}
</script></strong>

Day 7 — Polish, test, and launch

• Performance: compress images (WebP/AVIF), remove unused fonts, enable CDN edge caching — critical for conversion pages. Consider the benefits of edge-powered landing pages for TTFB-sensitive funnels.
• Privacy: add a simple cookie banner, document processing of personal data (Airtable), and offer deletion on request.
• Soft launch: invite 20–50 users and watch these KPIs for 48–72 hours: time-to-first-recommendation, recommendation click rate, and share rate.
• Iterate: change hero headline or CTA color if conversion rate is below benchmarks (aim for 8–12% for invite-based micro-apps; landing page conversion 10–20% depending on intent).

Advanced strategies (2026 trends to leverage)

• Embeddings + vector search: Precompute embeddings for your restaurant descriptions and use a vector DB (Supabase Vector, Weaviate) to quickly find candidates semantically close to group preferences. See notes on site search & observability when you add a semantic layer.
• On-edge inference and latency reduction: Use edge functions to call your LLM backend to reduce round-trip time for mobile users — and pair with low-latency networking plans discussed in future networking briefs.
• Cookieless conversion tracking: Adopt server-side event collection for marketing pixels — this is increasingly required after late-2025 privacy shifts.
• Local LLM fallback: For privacy-sensitive groups, support less-personalized local rules (weighted scoring) so the app can operate offline or with minimal cloud calls. For edge hardware considerations, benchmark local AI accelerators.

Testing & iteration: what to A/B test first

1. Hero headline: talk about time saved vs. social proof.
2. Primary CTA copy: “Get started” vs “Find a table now”.
3. Recommendation verbosity: show 3 concise suggestions vs. 7 detailed cards.

Cost & scaling checklist

• Airtable: free → paid depending on record count. Keep dataset small (1k–5k records) for cost efficiency.
• LLM calls: reduce candidate set and use short prompts to control spend. Cache repeated queries.
• Hosting: one-page sites are cheap on Cloudflare Pages or Vercel; micro-app front ends from Glide often include hosting in the plan.
• Analytics: Plausible/Fathom monthly fee vs. GA4 free but more complex to manage server-side.

Real-world example and KPIs

Rebecca Yu’s Where2Eat shows the power of a single-purpose app: built quickly to solve a real user problem, used within a close group, and iterated based on real usage. For your launch, track these KPIs in the first 30 days:

• Activation time: time from visit → first recommendation (target < 60s)
• Recommendation CTR: percent of users who click one of the suggested restaurants (target 30%+ for interested users)
• Share rate: percent who share a result back to a chat or social (target 10%+ initial)
• Landing page conversion: percent of visitors who try the app (target 10–20% depending on traffic intent)

Common pitfalls and how to avoid them

• Overcalling the LLM: Don’t send the full database. Pre-filter and send a candidate subset.
• Slow UX: Use optimistic UI and local scores while waiting for LLM results — show “fast picks” from weighted scoring immediately.
• Poor onboarding: Remove friction: enable social auth or a one-tap join, and explain value in 7 words.
• Privacy gaps: Document usage of location and personal data; provide an easy opt-out. Consider the edge-first verification patterns for local trust signals.

Example prompts and templates you can copy

Data enrichment prompt (to create tags and descriptions)

<strong>Prompt: Given the restaurant name and short description, generate 5 tags (comma-separated) and a concise 20-word description useful for recommendation ranking.</strong>
Example input: "Tasty Noodle – cozy late-night ramen with spicy broths, open until 2am."
Output: "tags: ramen, late-night, spicy, casual, counter-service | description: Cozy late-night ramen spot with bold, spicy broths; great for small groups after outings."

Recommendation prompt (compact JSON output)

<strong>SYSTEM: Rank restaurants for the given group, return JSON: [{id, score, reason}] only.</strong>
USER: Preferences: {"cuisines":["Korean"], "vibe":"casual, lively", "max_price":2}
Candidates: [{id:1, name:"Kimchi House", ...}, ...]

Final checklist before you press publish

• Micro-app: responds in <60s, shows 3–5 suggestions, supports share link.
• Landing page: hero CTA visible on mobile, testimonial, 3-step how-it-works, GDPR/Privacy link.
• Analytics: events firing for start_group, show_recommendations, click_result, share_result.
• Costs: initial estimates for Airtable, LLM calls, hosting are within budget.

Closing — start building (and iterate fast)

Micro-apps like Rebecca Yu’s Where2Eat prove that small, focused tools solve real problems fast. In 2026, you don't need heavy engineering to ship useful experiences: combine a simple data model, a no-code front end, an LLM-assisted ranking webhook, and a conversion-focused one-page site to deliver high-impact results in a week.

Actionable takeaways:

• Use Airtable + Glide for speed, Bubble for flexibility.
• Pre-filter candidates, then use an LLM to rank; cache results to reduce cost. For local inference and hardware considerations see benchmarks.
• Focus landing page attention on one clear CTA and measure the funnel with event-level analytics.

Ready to replicate Where2Eat with your own spin? Start the 7-day sprint today: build the data model on Airtable, prototype the UI in Glide, and publish a conversion-optimized one-page site on one-page.cloud for instant visibility. Launch small, measure, and iterate — the micro-app era rewards speed and real user feedback.

Get started now: create a free Airtable base, sign up for Glide or Bubble, and spin up a one-page.cloud landing page to capture your first users.

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