Show HN/Ask HN: Omnifocus killer for windows – feedback

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Hey guys, I am building version 2 of taskorami and want to crowd fund it because I quit my job and want to focus on it. Here is the link to the campaign: https://taskorami.crowdtilt.com/taskorami-2-0 Questions: - what do you think of the campaign page? - was using crowdtilt a good choice over kickstarter? They don't take any commission. - any other feedback? Thanks. PS: I am running AB Tests so the content you see might be different - but not TOO different. PPS: My goal is to build a GTD killer app for windows (eventually add web and mobile).

Visit product View on Hacker NewsMar 3, 2014

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

68%68% 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

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, 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

41%41% 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

40%40% 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

17%17% 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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