Built an AI Flat-Finder in a Weekend: Scraping Reddit to Beat Broker Spam

A developer built TruRent in ~10 hours to solve a specific problem: Indian rental sites like NoBroker, MagicBricks, and 99acres are ~70% broker spam, even with "direct owner" filters. Reddit has honest owner listings, but they're unsearchable. The solution? A conversational AI that scrapes Reddit and lets you chat with the listings.

How It Works

• Stack: Next.js 16 with raw fetch to Anthropic — no SDK, full control of the streaming loop.
• Model: Claude Haiku 4.5. The task doesn't need Sonnet, and Haiku is 5x cheaper.
• Tools: Only two — search and get_details. Comparison and ranking happen in the model's prose, not separate tools. More tools = more failure modes.
• Streaming: NDJSON to the browser. Easier than parsing SSE.
• Scrape pipeline: PullPush API to pull Reddit posts, then Haiku to extract structured listings from raw post text, Nominatim for geocoding.

Numbers

• 1,412 posts scraped
• ~600 passed a local pre-filter
• Only 131 ended up being real listings — dataset is tiny but the pipeline is source-agnostic. Swap the fetcher and nothing else changes.

Key Design Decision

The builder deliberately used fewer tools and let the model reason over richer tool outputs. Comparison and ranking happen in the model's prose, not via separate tools. This reduces failure modes and keeps costs low. The same principle applies: let the model think, not just filter.

Try the demo: https://trurent-five.vercel.app/