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Burst God – QR Slate Solution for Shooting Video with Burst Photography

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Burst God – QR Slate Solution for Shooting Video with Burst Photography

A few years ago I was working on a commercial and learned all about how great looking videos/gifs can be produced using burst photography. In my spare time, I did some experimenting with shooting burst photography clips to make videos. However, the editing process was horrible because the timeline was thousands of single frame images. Exporting the images as long clips is a solution but it still requires the manual effort of breaking up clips. I created Burst God to make the editing prep work considerably easier. The solution is a QR code film slate that is photographed between burst shot sequences. After the shoot, the software determines the shots and allows quick and easy video exporting. Plus, the video files are named according to the slate data. There are a lot of options for frame control. For example, with "Pattern Mode" you can create a repeating pattern such as 1,2,3. With input frames of ABC, the output video would be frames ABBCCC, which can yield interesting looks. Examples are available on the Burst God website. The software is free and can be downloaded at: https://github.com/BuzzaBoo/BurstGod The QR Film Slate is just an html file. The Software is just a few Python scripts. Burst photography for video can yield some great looking and interesting footage. With Burst God, it's possible to use burst photography video for something like a music video or short film or even a feature film without a painful and unorganized editing process. Although the software is made specifically for burst photography shoots, it could also be used for batch processing any image sequences into videos.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · 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: single, using, code · Missing: mac, agents, macos
64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
49%49% 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
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.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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.

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