Current social media platforms are abusing us

Hacker News

Hi HN, over the years of using the social media (Instagram, Pinterest, etc..), I’m quite weary of the trend that people tend to polish their content so well and post the perfect aspect of their lives. I feel like that I’m not one of them, and anything I post will be either not so polished to fit the “magazine” style of the community or so insignificant for anyone else to care about. Scrolling in those apps more and more and seeing more and more perfect lives, I feel like I was abused by those platforms and made me anxious about myself. I want an online space that can give my voice back, allowing me to record my real life and experiences, and engage with people without feeling like judging by the platform and others. I made an attempt to solve this problem by building a new social media platform called MemoFlow (https://www.memoflow.com/) that generates post feed by a recommendation system that puts content information and your interest first, rather than based on your existing social circle like what everyone else is doing. Our community is very inclusive. You can not only post about your life, but also your questions, your anxieties, and any aspects of the REAL you, and be discovered by the people who shares the same feeling as you. It’s like on Twitter, when you want to express something private, you sign up for an account that no one knows and dumps all your feelings into it. You don’t want your friends and family to discover this account, but you want people who understands you to engage with you, which you well know in advance that this isn’t going to happen, because Twitter won’t expose your tweets to anyone. But on MemoFlow, you’ll actually be discovered. I’d like to invite you to test my platform and any ideas/feedbacks are well appreciated.

Visit product View on Hacker NewsMar 30, 2023

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

76%76% 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

63%63% predicted probability of success on Indie Hackers, 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

50%50% 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 · Strong signals: apps · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, 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 · 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 · Strong signals: real life · 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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