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Crisp – Open-source display manager for macOS (DDC, HiDPI, presets)

Hacker News

Crisp – Open-source display manager for macOS (DDC, HiDPI, presets)

I recently bought a new 1440p monitor and connected it to my mac, but at native resolution the UI felt so tiny and unfamiliar. I'm fairly new to mac in general so I checked my options for scaling, but quickly learnt (afaik) that this wasn't a thing for external monitors. I found some options in BetterDisplay and Lunar, but it felt stupid to pay for a feature that should be included. FreeDisplay seemed like the perfect option, but when I tried it on my mac it didn't work for me. Development seemed stale so rather than adding a PR, I forked it and fixed it. After that I wanted to do some changes and add some features, which suddenly ended up in a full rewrite. It covers the thing I initially wanted, which was HiDPI, but I also wanted it to look native, so I added some native elements. I kept the display arranger and fixed a lot of the issues with e.g. auto and combined brightness, profiles for the monitors, presets etc. Can be installed with brew (brew install --cask didriksg/tap/crisp) or use the dmg file in the releases in the repo. It’s unsigned though, so you’ll probably get some warnings I’ve been using it for a week now and thought I’d share it with anyone that wants it. Open source and PRs are welcome.

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, new · Missing: agents, agent, cursor
95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
63%63% 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
16%16% 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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