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OpenSCAD configurable calendar 3D model

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OpenSCAD configurable calendar 3D model

I created a highly configurable calendar 3D model using OpenSCAD. This project uses Zeller’s Congruence to automatically adjust the calendar layout for any year. Just change the year in the configurator, and the model updates accordingly. The link contains a little more of the backstory and the downloadable model from a 3rd-party site, additionally the full source code is included in the post. Some highlights: - Implemented date calculations entirely in OpenSCAD (with no built-in date support, which was challenging but fun). - Customizable layout, multi-material printing support, and translation for all text elements. - Configurable holes for magnets/screws to mount on a fridge or wall. - Utilizes JustinSDK/dotSCAD and davidson16807/relativity.scad for string manipulation. - Correctly takes into account leap years. Would love to hear your feedback. Thanks for checking it out!

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
78%78% 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: model, using, code · Missing: mac, agents, macos
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
59%59% 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
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
38%38% 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
19%19% 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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