Daminik – An Open source digital asset manager

Hacker News

Hey guys! We just published our little open source project on GitHub and would love to get some feedback. It's a very early release (alpha) but we thought it would be better to do it earlier so that we could get direct feedback. Over the last 10 years we have built small to large web-projects of all kind. Almost every project involved managing images and files in one way or another. How do we manage our images and where? What happens if an image or logo changes, does it get updated across all sites? Which Host/CDN to choose? Is everything we do GDPR-compliant? With Daminik, we are trying to build a simple solution to solve these problems. One DAM with an integrated CDN. That’s it. You can check it out at: https://daminik.com/?ref=hackernews (hackernews being the invitation code) Repo: https://github.com/daminikhq/daminik We would love to get your guys feedback on our alpha.

Visit product View on Hacker NewsJul 12, 2024

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

72%72% 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 · Strong signals: new, code, open · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive

54%54% 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

34%34% 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

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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