Iris – an AI-powered rental search built specifically for San Francisco

Hacker News

We built Iris after watching friends spend weeks doom-scrolling Craigslist, Zillow, Apartments.com and broker sites — only to miss good units or run into stale / scammy listings. Iris is a SF-only rental marketplace with a few differences from existing platforms: 1. Search by natural language (“1BR near BART under $3.2k”) or images (upload inspiration photos) 2. Verified listings only from property managers, owners, and authorized agents 3. SF-specific filters: rent control toggle, transit lines, neighborhood context What surprised us since launch: 1. A large share of inventory never shows up cleanly on national portals 2. Renters care more about context (block, transit, light, noise) than raw filters 3. Narrow vertical focus (one city) lets us build features Zillow can’t justify Would love feedback from people who’ve built: - Vertical marketplaces - Local-first products Happy to answer questions. -- Manan Shah

Visit product View on Hacker NewsDec 28, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, context · Missing: mac, macos, cursor

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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