Java to PHP Compiler

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I had this crazy idea like some years ago.. I wasn't expecting it be factible, and there's still some semantic problems with this approach Anyways, the whole idea was to take some java source, generate an AST, and output it back as syntactically correct PHP. why? 50% fun + 50% exploration So I have this really basic compiler, which can roughly output its own code; what's still missing? - static vs instance detection (-> vs ::) - class' properties - method visibility - enums - inner types - annonymous classes - dealing with method overriding There's a huge gap in the semantic area (eg: String.format vs sprintf ) that is way out of scope for this hack. Source: https://gist.github.com/eridal/a850a8a665a255b74d2c

Visit product View on Hacker NewsDec 12, 2014

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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.

best fitHighest predicted score across all platforms for this description.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

42%42% 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: way · Missing: mobile apps, ios, personal

39%39% 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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos

28%28% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

16%16% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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