Lump.co – instant photo galleries via web search

Hacker News

This is my little side project Lump.co. It's like Pinterest, but you build the gallery by searching the web for photos, rather than having to download photos, collect them, and then reupload to Pinterest. It's quick as heck, works pretty well on iOS, and is a blast to use. Motivation/Scratching an itch: I'm always sending Google Image Search results to people, but I got really frustrated by the long URLs and inability to edit/reorder the results. I love working on Lump because I use it ten times a day, just for myself. Things like: 1. Picking things to buy visually: http://lump.co/amazon-standing-ashtray 2. Finding recipes by my "desired result" (what I want my food to look like.. sounds silly but it works!) http://lump.co/pork-shoulder-recipes 3. Understanding concepts by quick visuals (I find this to be a really effective way to learn when you don't have time to read a lot of material) http://lump.co/understanding-ios-delegates It's still pretty buggy at this point, and I think it really needs to be a native iOS app for the best UI/UX, but for now I am ready to submit it for HN's denunciation. :) I'd be eager to hear what everyone thinks!

Visit product View on Hacker NewsFeb 19, 2014

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

82%82% 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: google, visual · Missing: mac, agents, macos

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, google, way · Missing: mobile apps, personal, entrepreneurs

63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

14%14% 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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