A mix between Hacker News and Twitter

Hacker News

Tech Noise - A social/news mobile app powered the tech community. I just launched my startup ‘Tech Noise’. It’s a mix between Hacker News and Twitter.It has the minimal social/news aspect of hacker news with the subtle feel and design of Twitter along with its social features. I would appreciate any feedback either good or bad, and especially bad. Likewise, you can submit feedback once you created an account and navigate to the settings screen by clicking on the settings bottom tab icon where you’ll see a button to submit feedback. In addition, the app is available for download. Please keep in mind the app is open for testing to the public and I expect bugs lol. Once again, I would appreciate feedback :). Remember, KEEP MOVING FORWARD, NEVER give up, and stay on your purpose and achieve your dreams and goals. YOU got this, keep the faith, and KEEP WORKING AND BELIEVING - dream, work, build, and create. Peace and much love family!

Visit product View on Hacker NewsSep 8, 2022

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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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

91%91% 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, open · Missing: mac, agents, macos

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, 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.

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

34%34% 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

32%32% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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