Endvr.io – we're trying to make the recruitment process smarter

Hacker News

https://endvr.io This is our MVP and we're looking for feedback plus interesting opportunities. Our service connects recruiters to talented folks in smarter ways than blanket emails or insincere LinkedIn messages. We let talent specify hidden requirements such as salary and only let them be contacted when a recruiter or employer has something genuinely relevant to them. Those hidden requirements are never disclosed, only used by our system to connect people together. Recruiters/employers have to be up-front about their opportunities and we expect them to apply a reasonable code of practice; otherwise face a ban for wasting people's time. We believe there's a real problem and we can help give people a better experience. Help us figure out how by giving us your invaluable feedback! support@endvr.io

Visit product View on Hacker NewsNov 9, 2014

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Actual performance

2points

4comments

Did not reach leaderboard

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

73%73% 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 HuntUnlikely to reach the leaderboard · Strong signals: email, code · Missing: mac, agents, macos

47%47% 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: lua, io · Missing: https docs, excited, just released

37%37% 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: plus · Missing: platform, intuitive, reviews

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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