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The Black Eye Galaxy (M64), 11 hours of exposure from my backyard

Hacker News

The Black Eye Galaxy (M64), 11 hours of exposure from my backyard

The Black Eye Galaxy (M64) is one of the coolest things in the universe, due to its dark dust lane. I captured this beautiful image in 11 hours and explain here how it was captured, and explain why this galaxy is so remarkable. If you're into astrophotography, I've included many details that you'll find interesting, about the gear and editing process. There's a lot of beauty in the universe that we can photograph and share, so what's your favorite deep-space subject to photograph or look at, and why is it so interesting to you?

Visit product View on Hacker NewsJun 24, 2025

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Hacker NewsStrong engagement from HN community · 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 · Missing: plus, platform, intuitive

50%50% 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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

34%34% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% 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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