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Pawxel – (yet) a(nother) screenshot tool

Hacker News

Pawxel – (yet) a(nother) screenshot tool

About a year ago, I sat down and wanted to replace the Windows screenshot tool Snipping Tool for myself, because I needed something at work that got more editing capabilities. From my macOS days I remembered the tool Shottr ( https://shottr.cc ) very well, but found nothing that was quite similar (in design & features, but mostly design ;-)). Without further ado, the idea for Pawxel was born and I would never have thought, that one year later, the idea I had drawn in my notebook (UI design & some code ideas) would become reality. The intention of this whole project wasn't to show it to the public ever, but eventually colleagues and friends told me to do so. I reconsidered my decision to do it closed-source and open-sourced it. There is still a lot to do! Many things I had on my agenda didn't found a direct way into the first versions of the application (like Blur, Text recognition etc.) but I think it's still enough to have a beta-worthy state. Under the hood the application uses Qt5 and FramelessHelper (a cool library to create a frameless and native window). I think the biggest major changes before there will be any kind of 1.0 is moving to Cmake (eventually, I am in the C++ world for like 8 years and couldn't get used to it, don't sue me) and to Qt6. Would really like to hear some feedback! This thing works on Windows and Linux, and for those on the Apple-side, I really recommend Shottr as it is a great tool!

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Actual performance

2points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
90%90% 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: mac, macos, apple · Missing: agents, agent, cursor
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
52%52% 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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