search engine through new freelance and remote jobs

Hacker News

Would like to ask for your feedback and suggestions for my web project: http://www.guruka.com/ What is it: free search engine through new remote projects and jobs from 10+ sites and boards (like Freelancer.com, oDesk, eLance, Stackoverflow Jobs and others) Who needs it: pro freelancers looking for new projects or occasional freelancer who are looking for interesting projects (to practice new skills). Or even software market researchers to see what kind of projects are in demand. Thanks again for your attention

Visit product View on Hacker NewsDec 10, 2013

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Actual performance

13points

5comments

Made the leaderboard

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

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: occasional · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

30%30% 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

29%29% 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

13%13% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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