Messer, image resizing/optimization in the macOS status bar

Hacker News

Hi Hacker News! Wanted to show you my latest project, Messer: https://apps.apple.com/de/app/messer/id1586304988?l=en&mt=12 (clickable link in the comment) It's a small native app for the most basic task of all: converting, resizing and optimizing images. I'm currently trying to grow my following, which means I post images a lot, however, these images need to be in a specific format/ratio. Usually, I would go to one of the many websites that resize and convert images, but the experience is subpar and it's a drag to do this multiple times a day. Therefore I created a fully native app that does this for me, converting an image takes a second and since it's deeply integrated with macOS it works by simply dragging and dropping, first you drag your image to the menu bar, adjust your parameters, and then drag the image out directly where you need it There are a couple more features I would like to implement like an embedded code formatter (a la carbon.now.sh or ray.so), so if you know of a Swift library that can output colored/formatted code, let me know. Enjoy! (and please rate if possible!)

Visit product View on Hacker NewsSep 20, 2021

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best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, apple · Missing: agents, agent, cursor

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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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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