My

My first RubyMotion app, lulcards. Send hilarious postcards.

Hacker News

My first RubyMotion app, lulcards. Send hilarious postcards.

http://lulcards.com Been working on this since January. RubyMotion experience was very positive, made development both enjoyable and I'd like to think faster. (Found a few bugs though!) Running on AWS via OpsWorks. Using Amazon SWF for backend fulfillment workflows, which is amazing and not something I see mentioned often. Postcard printing/mailing by Amazingmail, payments by Stripe, address resolution by SmartyStreets (who are great and hooked me up with with free unlimited API calls for a year.) Would love any feedback or suggestions, thanks!

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: stripe, using · Missing: mac, agents, macos
85%85% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
65%65% 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
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
30%30% 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
12%12% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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