Can One Place be vibe coded?
AI-powered European real-estate search, boards, lead pipeline, and agentic deal discovery
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.
Jump to the build brief ↓Checked Jul 2026
What you pay today, before any DIY hosting
high editorial confidence
Buildability by layer
Screens, forms, and focused interactions
The repeatable job the product performs
Availability and legality of required data
Uptime, queues, support, and maintenance
Security, compliance, and user confidence
The achievable core
- 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.
- Build a focused single-user workflow with real persistence, search, and export.
- A responsive interface with real empty, loading, success, and error states.
The parts a prompt cannot buy
- 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
- The useful dataset is owned, accumulated, or expensive to reproduce.
- Reliability at the vendor's scale is an operations problem, not a prompt.
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.
The useful dataset is owned, accumulated, or expensive to reproduce.
Reliability at the vendor's scale is an operations problem, not a prompt.
The brief
Context, requirements, acceptance criteria, non-goals, and the full production standard — as Markdown, ready for any coding agent.
Build brief — a focused alternative to One Place
Context
**One Place** — AI-powered European real-estate search, boards, lead pipeline, and agentic deal discovery. It currently costs $39/mo.
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.
This brief describes a focused, single-operator replacement for the part of One Place that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest.
What you are building
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.
Build a focused single-user workflow with real persistence, search, and export.
A responsive interface with real empty, loading, success, and error states.
Requirements
Functional
Cron-style scheduled search runner.
MCP server for agent access.
Data and integrations
Playwright scraping.
LLM API key for listing extraction.
SMTP or Resend email alerts.
Each of these needs a real account, credential, or quota. Set them up before writing feature code.
Non-functional
Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support.
Security: server-side secrets, validated input, and no credentials in the client bundle.
Reliability: retries with backoff on external calls, and a clear failure state when a provider is down.
Portability: the operator can export their data and leave without losing it.
Implementation brief
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.
Delivery standard
Inspect the repository first, then write a short implementation plan before writing code.
Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data.
Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations.
Include responsive layouts plus genuine empty, loading, success, validation, and failure states.
Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data.
Add structured logs around every external call and return actionable errors without leaking sensitive details.
Write unit tests for the core logic and one automated test of the main user journey.
Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations.
Acceptance criteria
A clean install starts the app using only the README and .env.example.
The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable.
Invalid input, missing configuration, provider failure, and an empty database each have a usable state.
The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control.
Tests, type checking, linting, and a production build all pass with no ignored failures.
No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified.
Non-goals
Do not build these, and do not claim to have replaced them:
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.
The useful dataset is owned, accumulated, or expensive to reproduce.
Reliability at the vendor's scale is an operations problem, not a prompt.
What you still own after launch
Secure credentials, rotate secrets, and handle provider rate limits.
Run migrations, backups, restores, and dependency updates.
Test the critical journey after every model, API, or hosting change.
Monitor failures and fix the edge cases a first prompt will miss.
Risk
**Operational risk.** The code is achievable; dependable data, integrations, and ongoing operations are the real cost.
Editorial confidence in this assessment: high. No independent one-shot implementation is linked yet.
Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/one-place
You still own the product
- Secure credentials, rotate secrets, and handle provider rate limits.
- Run migrations, backups, restores, and dependency updates.
- Test the critical journey after every model, API, or hosting change.
- Monitor failures and fix the edge cases a first prompt will miss.
Before you start
Can One Place be vibe coded?
Not faithfully. 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.
What can an AI coding agent reproduce from One Place?
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. Build a focused single-user workflow with real persistence, search, and export. A responsive interface with real empty, loading, success, and error states.
What will a DIY One Place replacement still be missing?
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; The useful dataset is owned, accumulated, or expensive to reproduce.; Reliability at the vendor's scale is an operations problem, not a prompt.
What do I still own after building a One Place alternative?
Secure credentials, rotate secrets, and handle provider rate limits. Run migrations, backups, restores, and dependency updates. Test the critical journey after every model, API, or hosting change. Monitor failures and fix the edge cases a first prompt will miss.