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Designer News iPhone app weekend hack (promos)

Hacker News

Designer News iPhone app weekend hack (promos)

hey HN, A few of us run a little dev firm just outside of our alma mater, and recently we've become adamant followers of Designer News (in addition to HN of course). We spent a weekend stringing together a mobile solution and added some features like badge filtering and the ability to save favorite posts. We don't have API access so we couldn't give you all the ability to login and comment, but we did our best with what we had. We'd appreciate your support and feedback. Also, none of us are members over at DN is if someone could share this post over there and let them know about our app, we would be ever so thankful. Promo codes below. Be sure to let us know who is claiming what. Thanks for your support and thanks to Allan, Kelly and the layervault crew for all that they do! EAKFKMW3LR3K 4JWT7J9KAHHJ 3RPP3T4FK9KM 6TY4TNLLNNRT FFWETLWFTPF4 HNNKHHYKMYKK WJPHLKMFA6RH PKL9P44FTPEM WAEMWYW6LFJM RXM4L3XHW4RN

Visit productView on Hacker NewsMar 18, 2013
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Actual performance

3points
6comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% 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: new, code · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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