Splendidis – a macOS app to create attractive snapshots

Hacker News

Hello everyone, Splendidis can be used to create more attractive snapshots. It works with text (especially sample code - hilightJS is used for syntax highlighting), images and (short) videos. Modifiers (wallpaper, logo, etc.) can be used to improve the image and create a unique visual identity when sharing content on social networks. Settings can be saved and easily applied to new documents. Splendidis offers quick ways to create a snapshot of a screen (or all screens at once), a window or a connected iOS or Android device (Android SDK must be installed at this time). Splendidis also offers the possibility of creating short presentation videos from a screen, a window or a connected iOS device. Audio/video comments can be recorded at the same time. Effects can then be added to the video tracks. There is an Alpha version of Splendidis on the site. The app should work from macOS 10.15 (Catalina) to macOS 13 (Ventura). But I only tested it on macOS 12.0 (Monterey) Intel so far. Audio/video/screen recording permissions are required for Splendidis to work. the app also captures keystrokes while recording. a future option will show keyboard shortcuts in the video. More options will be added from time to time to create unique visuals. A first stable version should arrive within a few weeks and will be sold between 20€ (without video recording) and 50€ (with video recording). The "text part" will remain free. All comments and suggestions are welcome. Thank you for your attention.

Visit product View on Hacker NewsMay 16, 2023

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

97%97% 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, new · Missing: agents, agent, cursor

95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% 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

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

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

25%25% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: arr, active · Missing: mrr, revenue, profit

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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