Tiny game made overnight for the 2020 Blender 24hr art competition

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It was Saturday night, deep into a dark chocolate bar and well-paired Cabernet, when Cranksy [0] pinged me on IRC gauging interest in collaborating on this year's Blender [1] competition. "Would it be too much work to just throw an ANSI on the screen and play an ogg file?" he asked. Inebriated enough to get a little enthusiastic about something like this, I joined #blender on efnet, and noted this year's words had already been chosen. "Damn, we've got a late start." I thought as I read the channel's topic: Monkeys / Rescuing / Between Realities It must have been all the COVID-19 news I've been endlessly assaulted with, because it was immediately clear that these words would fit perfectly with a game of monkeys saving other monkeys from viruses by transporting them to another reality. A bottle of wine and one sleepless night of drunken coding later and a tiny game [2] is born. WARNING: there's a potentially NSFW ANSI graphic in-game, if ANSI can possibly be considered NSFW. Unusual for these kinds of things, I'm also releasing the entire C source code [3] of everything needed to rebuild and fork/hack if desired. The only external dependencies are SDL2, SDL_Mixer, whatever those pull in like libvorbis/libogg, and OpenGL. If you clone the repository, be sure to use a recursive clone as there are submodules. Also, if you'd like to be notified via email of interesting creations like these, you can submit your email address in the text box @ http://pengaru.com. If you're interested in collaborating on making demos, intros, or video games, reach out - I prefer email, Cranksy is on twitter. Please be kind to my server, hopefully it doesn't get hugged to death, thanks! [0] http://cranksy.net [1] http://mistigris.org/blender/blend.html [2] http://pengaru.com/~vc/tmp/hungrycat-sars-rev1.zip (Includes native OSX/Windows/Linux executables) [3] git://git.pengaru.com/sars

Visit product View on Hacker NewsApr 21, 2020

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, email, tiny · Missing: mac, agents, macos

83%83% 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: ide, io · Missing: https docs, excited, just released

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% 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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