[verdict][curated prompt][source: one-place.com]

Can I vibecode One Place?

// the value is the network, the data or the infrastructure

You can build a scheduled personal property monitor, but not One Place: the value is the maintained real-estate corpus, crawler fleet, AI extraction, deduplication, image/semantic search, geo enrichment, and constant source upkeep across millions of listings.

confidence: high

what it is: AI-powered European real-estate search, boards, lead pipeline, and agentic deal discovery

Buildability index · an editorial game

Price 39 $/month weight: plus 3
Time multi-week weight: minus 2
Category ai search no weight
Moat proprietary data · infrastructure scale weight: minus 4
Confidence high weight: plus 1
What you lose 5 items weight: minus 2
Site one-place.com (si apre in una nuova scheda) no weight

think twice

The moat outweighs the price: rebuilding this is a project, not an evening.

Gioco editoriale: il verdetto dice se un agente può, l’indice se conviene.

// free tier: The free plan gives unlimited search and saved searches, capped at 5 boards.

What you build

Run saved searches on a schedule, crawl configured source pages, use an LLM API key to extract listing fields, dedupe into SQLite, email matching finds, and expose an MCP server for chat/coding agents.

what you need

cron-style scheduled search runner
Playwright scraping
LLM API key for listing extraction
SMTP or Resend email alerts
MCP server for agent access

The scheduled personal monitoring agent is buildable. The market-wide index is the product.

The prompt

The verdict is NOT REALLY, and the prompt stays anyway: it does not replace the app, it rebuilds the part that really is code. The rest is the moat.

One-shot prompt EN

Build me a scheduled personal property monitoring agent, the honest consolation
build for One Place: it watches the sources I configure, it does not rebuild a
live nationwide real-estate index. Requirements:

- Node + Express + better-sqlite3 on localhost:4920, with a small web UI for saved
  searches, matches, and lead notes.
- Saved searches live in SQLite: name, source URLs, natural-language criteria, hard
  filters, cadence, email recipients, and last-run time.
- A node-cron runner wakes on each cadence, fetches configured source/search pages with
  Playwright, and stores raw HTML snapshots for debugging.
- Send cleaned listing HTML to an LLM API key from .env and extract strict JSON: title,
  price, currency, surface, rooms, location text, description, image URLs, source URL, and
  confidence.
- Match each extracted listing against the saved search criteria with deterministic
  filters first, then an LLM yes/no explanation for fuzzy preferences like renovation
  potential or sea view.
- Store listings and match decisions in SQLite, dedupe by canonical URL first and fuzzy
  title+price+surface+location second; keep price-change history.
- Email new matches via Resend or SMTP creds in .env, including the match reason, key
  fields, source link, and unsubscribe/disable link for that saved search.
- Include an MCP server exposing tools: list_saved_searches, run_search_now,
  get_recent_matches, explain_match, update_search, and add_lead_note, so Claude/Codex can
  operate it from chat.
- No accounts, no telemetry, binds to localhost only. Out of scope: nationwide coverage,
  anti-bot arms races, paid data resale, mobile apps, and collaborative CRM.
- README: crawler ethics, robots/terms warning, required API/email keys, how to run the
  scheduler, and how to connect the MCP server.

Curated prompt: written and reviewed by hand for this app. In English on purpose — it is the language coding agents work best in.

What you lose

millions of already-normalized listings
maintained crawlers across real-estate portals
AI extraction, deduplication, and image/semantic search at scale
geo/POI enrichment and currency/unit normalization
hosted boards, lead pipeline, sharing, and agentic search

Why people still pay

They pay because property search is a moving data pipeline: portals block scrapers, listing HTML changes, duplicates multiply, and the useful part is having the whole market normalized and searchable before a deal disappears.

moat: Proprietary data Infrastructure scale what a moat is

proprietary data/crawler scale/AI extraction pipeline