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How to fund my startup through Seedrs - Workplace productivity software

Hacker News

How to fund my startup through Seedrs - Workplace productivity software

I'm raising investment for my startup 'Hey Jimmy' through Seedrs. Check it out at http://seedrs.com/startups/hey-jimmy I’m building an office timesheet application for every business in the world with staff. This application is going to regiment ‘clocking in and out’ procedures and increase productivity during that time. Have a read through the Seedrs listing for more specifications on the product. Expect a closed beta release in a couple of weeks!

Visit product View on Hacker NewsSep 13, 2012

Actual performance

4points

4comments

Did not reach leaderboard

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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

52%52% 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

37%37% 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.

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

25%25% 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

14%14% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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