I've been building this thing

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...it's an image proxy that does on-the-fly image processing for animated GIFs. This is the site: https://www.gifsonic.com And here are docs with examples: • https://www.gifsonic.com/docs/ • https://www.gifsonic.com/docs/example/?t=width • https://www.gifsonic.com/docs/example/?t=height • https://www.gifsonic.com/docs/example/?t=reverse • https://www.gifsonic.com/docs/example/?t=background_color • https://www.gifsonic.com/docs/example/?t=meme_top_text (my favorite) • etc. etc. What do you guys think about the whole thing? I would love some feedback before I open it to signups. Does it look OK? Would you ever use such a service? What could I improve?

Visit product View on Hacker NewsJun 30, 2017

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

65%65% 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 · Missing: mobile apps, ios, personal

46%46% 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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% predicted probability of success on Product Hunt, 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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