Stackjoy – a language agnostic code generator

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This is an example tutorial on how to use Stackjoy to quickly generate customizable components for an Angular codebase. We have been using Stackjoy as independent contractors for a while but it was always just a bunch of code glued together to serve our own needs. A few months ago we decided to make it more ready for a wider audience. We’re not yet fully there but we’re at a point where we can start showing what it can do. It was built with Angular so we’re starting with Angular generators…in fact, we used Stackjoy to build Stackjoy. It may not be something you’d use now for your software development but we have many more ready-to-use examples coming. Stackjoy is language agnostic so it works for any programming language. If it is something you could see yourself using we’d really appreciate your feedback. Even if it’s not, we’d appreciate your feedback too so we can better understand why and hopefully we can address it in the future. Lastly, if you have any suggestion for code generators you’d like to see, we are happy to hear about it and put it on our list.

Visit product View on Hacker NewsNov 26, 2022

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

73%73% 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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

56%56% 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

49%49% 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: month, way · Missing: mobile apps, ios, personal

40%40% 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

17%17% 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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