Sweesh – An online coaching platform for startup founders

Hacker News

Hi, am Dennis Abari the founder of Sweesh Technologies . Sweesh (https://www.sweesh.us) is an online coaching platform where founders of companies receives one on one training from other CEOs and founders in the world who are the coaches. Anyone can become a coach. The coaching takes place in meeting rooms. This training comes in the form of live interactive video meeting sessions, they can also dial-in to meeting by calling a meeting room phone number. So founders connect with their coach, give them updates about their company, their challenges and obstacles, and they receive leadership trainings, feedbacks and reviews on their startup on how to raise their own successful companies. There are also free public video meeting rooms for any who want to run a video meeting session or run an online conference. Each meeting room can accommodate 200 participants. Would welcome feedbacks and reviews from the platform. Thanks

Visit product View on Hacker NewsSep 2, 2019

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

79%79% 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 · Missing: mac, agents, macos

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

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

51%51% 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: platform, reviews · Missing: plus, intuitive, host

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training, active · Missing: arr, mrr, revenue

28%28% 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.

Incorrect prediction on native model

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