Search on Site: Your One-Tap Specific Site Search Tool

Hacker News

Discover a seamless and intuitive way to search within a specific website using "Search On Site" Chrome Extension! Forget the hassle of navigating through numerous pages to find what you need. With Search On Site, a streamlined site-specific search is at your fingertips. Here's how you do it: 1. Jump to the navigation bar with a simple (Ctrl+L). 2. Type in the keyword "son" and hit the TAB or Space. 3. Voila! Start typing your query and get directed to the precise information you seek on the specified site. But wait, there's more! With our added context menu search functionality, right-clicking any highlighted text on a webpage will instantly enable a search on the targeted site. It’s searching made simple, swift, and smart! Whether for research, shopping, or just quenching your curiosity, Search On Site is your go-to extension for a hassle-free and focused search experience. Add Search On Site to your Chrome browser today and redefine how you search on your favorite websites!

Visit product View on Hacker NewsOct 16, 2023

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

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

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Strong signals: intuitive · Missing: plus, platform, reviews

62%62% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Correct prediction on native model

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