We built the first data enrichment for developer communities

Hacker News

Hi folks In the world of DevTool and open-source companies, having a sound understanding of your community member’s technical skills and interests is a huge help to improve products, find contributors, and build stronger relationships. From open-source contributions over work experience to social profiles: valuable data is available. However, it’s spread across the many online profiles developers use, making it difficult to aggregate and act on. crowd.dev already pulls the activity and key attributes of your community members from the platforms they interact with you into one place. And starting today, we are taking it a step further and introducing member enrichment by crowd.dev - the first data enrichment tool built to enhance developer profiles.

Visit product View on Hacker NewsJan 31, 2023

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: activity, open · Missing: mac, agents, macos

93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

70%70% predicted probability of success on Indie Hackers, 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

49%49% 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: platform · Missing: plus, intuitive, reviews

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% 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

16%16% 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.

Correct prediction on native model

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