[verdict][prompt curatoriat][sursă: one-place.com][traducere: la coadă]
Pot să vibecodez One Place?
// valoarea stă în rețea, în date sau în infrastructură
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.
încredere: ridicată
ce e: AI-powered European real-estate search, boards, lead pipeline, and agentic deal discovery
Indice de constructibilitate · joc editorial
- Preț 39 $/lună greutate: plus 3
- Timp câteva săptămâni greutate: minus 2
- Categorie căutare ai nu cântărește
- Moat date proprietare · scara infrastructurii greutate: minus 4
- Încredere ridicată greutate: plus 1
- Ce pierzi 5 elemente greutate: minus 2
- Site one-place.com (si apre in una nuova scheda) nu cântărește
Moat-ul cântărește mai mult decât prețul: refăcut, e un proiect, nu o seară.
Gioco editoriale: il verdetto dice se un agente può, l’indice se conviene.
// plan gratuit: The free plan gives unlimited search and saved searches, capped at 5 boards.
Ce construiești
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.
ce îți trebuie
- 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.
Promptul
Verdictul e NU CHIAR, iar promptul rămâne oricum: nu înlocuiește aplicația, reconstruiește partea care chiar e cod. Restul e moat-ul.
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. Prompt curatoriat: scris și revizuit manual pentru această aplicație. În engleză intenționat — e limba în care agenții de coding se descurcă cel mai bine.
Ce pierzi
- 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
De ce se plătește în continuare
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: Date proprietare Scara infrastructurii ce e un moat
proprietary data/crawler scale/AI extraction pipeline
Ești de acord?
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