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Reading background videos from the SD card in a GBA game

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Reading background videos from the SD card in a GBA game

Hey there! I'm sharing here my Pump It Up port for the Game Boy Advance. The latest update adds a feature where the game can display background videos, reading them from the flashcart's SD card (in this case, an EverDrive GBA X5). Support for the EZ Flash Omega is still in the works. The code uses elm-chan's FatFs (a small FAT32 library) to find the right sectors, and then DMAs each uncompressed video frame directly into VRAM, enabling 60FPS video playback efficiently. With the 32MiB ROM size limit no longer being an issue, I opted to not compress the videos at all, which allowed to also run other things in the game loop like GSM audio decoding, the chart-reading routines or the wireless adapter multiplayer code. Here's the game source code: https://github.com/afska/piugba To try the video feature, you need an actual Game Boy Advance plus the EverDrive flash cart, so it's a bit niche. But I hope the video and the explanation illustrates how it works :) Let me know what you think!

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Indie HackersFits the IH revenue-focused audience · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting
77%77% 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: coding, code · Missing: mac, agents, macos
72%72% 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
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, efficient · 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
30%30% 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 · Strong signals: audio · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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