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iPhone Google Reader App (Go RSS)

Hacker News

iPhone Google Reader App (Go RSS)

As an avid Google Reader user I was really disappointed with the reading experience on my iPhone. I felt that the existing apps seemed to be more interested in adding features and cramming as much text into a small space rather than improving the readability of my feed and helping me decide which articles interest me or not. So I created my own https://itunes.apple.com/us/app/go-rss/id579197528?ls=1&mt=8 I have a finite number of Promo Codes. So if you are an iPhone user and a Google Reader fan and want to check it out, email feedback@gobits.ca and I'll send you one (as long as supplies last). I would love to hear feedback about what you like or hate. Features * Add/Remove subscriptions * Tagging articles * Setting Article status as Read, Keep Unread, Starred, Shared Sharing with: * Email * Facebook * Twitter * Google plus * Google history

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Actual performance

7points
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
90%90% 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: apple, google, apps · Missing: mac, agents, macos
77%77% 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, ide · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: apps, google · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
46%46% 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, subscription · Missing: mrr, revenue, profit
22%22% 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.

Correct prediction on native model

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