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DropZap World’s game mechanics for going beyond match 3 games

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DropZap World’s game mechanics for going beyond match 3 games

There are several game mechanics in my DropZap World game that I believe to be original with respect to color matching gameplay. I will describe these below. This video should make the description more clear: https://www.youtube.com/watch?v=M8fI1LkDZxc >>Basic Gameplay<< To pass a level, you will need to achieve the goal specified, which is currently one of two types: destroy all the foreground square pieces or destroy all the background lit squares. Now, as the name “DropZap World” hints, gameplay consists of circle pieces dropping and zapping . Suppose the current drop piece is red and you choose to drop it in the second column of the grid. This piece would fire its red lasers in eight directions when it lands on top of another piece (of any color) or at the bottom of the grid. Lasers affect pieces of the same color but simply go through (plain) pieces of different colors without affecting them in any way. Returning to our example, when the red drop piece lands: * Red pieces in the same row or column as this piece are destroyed. * Red pieces in one of the two diagonals containing this piece are mutated (e.g., turned into a mirror). After the destruction and/or mutation step, pieces may drop and thus an elaborate chain reaction could form using the rules above. >>Earning stars through laser art.<< When you pass a level, you earn zero to three stars depending on the art generated from your gameplay shown on level end. >>Using laser art to make “art moves”<< Laser art snapshots can be used to make art moves. Art moves are only available when there are no normal moves available because there is no space in the grid where you can drop the drop piece. To make an art move, select one of the art snapshots so that all the firing pieces in the art snapshot also fire in the main grid. >>Beta testing on iOS and/or tvOS<< If you would like to beta test DropZap World on iOS and/or tvOS, please email a.michail@me.com.

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TrustMRRFits verified-revenue profile · Strong signals: ios, video, way · Missing: mobile apps, personal, entrepreneurs
61%61% predicted probability of success on TrustMRR, 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 · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: email, using, plain · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
53%53% 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
47%47% 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
15%15% 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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