New OpenLoopz Landing Page Based on HN Feedback

Hacker News

Hi everyone, This is the new landing page for OpenLoopz: https://openloopz.com/welcome It’s based on the excellent feedback I received from the original post. I know it’s far from perfect and there is still a lot of material I haven’t had time to add yet, but it’s a start. I plan to iterate and improve on this over time as I learn more about marketing and promotion. I hope it gets the message across about what OpenLoopz is and why you might want to use it! I will be adding more pages soon, such as testimonials, pricing info, FAQ, use cases, etc, and more detail in general. Please let me know what you think and thanks again to everyone who took the time to provide feedback for the original post. It really made a difference.

Visit product View on Hacker NewsMar 9, 2015

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Made the leaderboard

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

64%64% 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

51%51% predicted probability of success on Indie Hackers, 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

42%42% 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 · Strong signals: soon · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

18%18% 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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