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A marketplace of vetted VC experts to help startups fundraise faster

Hacker News

A marketplace of vetted VC experts to help startups fundraise faster

Hi HN! My name is Artem ( https://www.linkedin.com/in/artem-bezrukov-a59a5424/ ). I worked for 5 years in an international VC fund Altair VC; early investors in Miro, Deel, Pandadoc and many more great startups. Recently I found out that my experience is very helpful to startups to help them excel their pitch, better show their strenghts and better negotiate. So I decided to build a marketplace for such professionals (usually with 3-5+ years of experience) to help companies fundraise easier and faster. We welcome both startups (you can start with a free call, get some advice) and VC professionals.

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Actual performance

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Did not reach leaderboard

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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.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
30%30% 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
26%26% 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
15%15% 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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