3Seeds – Our approach to helping startups develop their product

Hacker News

Hi everyone, As non-tech founders we confronted a huge problem one year ago: how to develop our product once it was validated. Recruiting and hiring would have meant selling our livers. So we ended up working with a great team in Eastern Europe. We wanted to replicate our experience and we would be delighted to share with you what we’ve learned after 3 months in manual mode and pick your brains on a couple of issues... The shameless part: 3Seeds started as a way to match software projects and startups with SW dev teams in Eastern Europe. Successfully matching a few dozen projects was a tremendous learning experience for us (and maybe just as many sleepless nights…) Check here a few thoughts on our experience: http://bit.ly/1jY0UJd Now we’re ready to move to the next iteration and improve our product. What were your challenges in getting your product developed? Did you hire or outsource? Let me know your feedback, you’re always awesome – http://3seeds.co.uk

Visit product View on Hacker NewsFeb 26, 2014

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

94%94% 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

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

34%34% 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

27%27% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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