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Relevant placeholder images (alternative to placeholder.com etc.)

Hacker News

Relevant placeholder images (alternative to placeholder.com etc.)

I made https://imgy.dev/ last week. Provides more relevant placeholder images for comps and so on. Same idea as the old Placeholder site, but prettier. Just embed an image tag linking to the service, and tell it what you want. For example img src=" https://imgy.dev/puppy " Will give you an image of a puppy. You can also specify image size to crop to or use landscape / portrait. See the site for examples. Very simple build. I needed it and it was quick to dev. Took about a week to get the API uncapped before I could share it. Uses pexels API. Give it a go. Let me know if it breaks.

Visit product View on Hacker NewsOct 25, 2022

Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

51%51% 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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

30%30% 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

12%12% 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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