Parabeac – Open-source design to Flutter converter

Hacker News

Hey HN! I’m Ivan Huerta & I’m a co-founder of a software startup in El Paso, TX named Parabeac. (https://github.com/Parabeac/Parabeac-Core) We open-sourced a design to Flutter conversion tool because tools like this need the cumulative genius of the developer community. Which is why we’re here on HN hoping to receive raw feedback & introduce people to our community. The idea span out of a dev shop where we became huge advocates for Flutter & played around with producing apps with low-code tools (unsuccessful). Producing full-fledged apps without developers is a reach, but we can make simple applications possible through the use of plugins or what we call eggs. However we’re not there yet. (Eggs are tools that use naming semantics to override interpretation & generation) We can certainly make it easier for designers to work with developers as we continue to improve the quality & configurability of the code exported for developers. And we’re so in love with Flutter & its future that we just want to make this the best we can for teams using Flutter. Building the interpretation algorithms were A LOT of fun! If you’re interested in looking at how they work they’re documented here in our wiki. (https://github.com/Parabeac/parabeac-core/wiki/Parabeac-Interpretation-Engine-Overview) We are anxious to get feedback & criticism on what we’ve been working on!

Visit product View on Hacker NewsAug 19, 2020

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

88%88% 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: apps, using, code · Missing: mac, agents, macos

76%76% predicted probability of success on Product Hunt, 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

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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