FoldMation – Interactive origami learning and creation

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Hi, I've created an application where you can follow step by step origami fold instructions, and a Creator where you can make these interactive folds. On comparing to video instructions, you have the ability to quickly skip/rewind steps and replay a complicated step many times. On the creation side, there have been one or two attempts at this before, but those solutions rely on mouse drags as the user interface. This greatly limited the kinds of folds possible. The foldMation Creator uses commands, keywords and values to compose a domain specific language/step and provides a (relatively speaking) easy to use user interface to compose the steps. For those interested in using the Creator, please go through the tutorial at the top of the create page. Btw, the DSL for foldMation uses https://github.com/mationai/mation-spec . I created it since I couldn't find anything out there that is similar, allowing me to specify a well structured data with English-like readable syntax. Let me know what you think?

Visit product View on Hacker NewsFeb 14, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, using · Missing: mac, agents, macos

70%70% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive

64%64% predicted probability of success on AppSumo, 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

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

12%12% 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.

Correct prediction on native model

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