CodefyPHP Framework: Domain-Driven, CQRS, EventSourcing

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A few years ago after lock down, I decided that I needed to refactor an huge PHP project that people were using. I was in awe of Domain-Driven Design and knew that I wanted to go this route. Like most of us, we curate a framework based on the libraries that we constantly use. I knew that I wanted to turn that curation into components. So, I set on a mission to revive dead libraries and breathed new life in semi updated ones in order to come up with a framework that met my mission and goal. After several years of work on and off, CodefyPHP was born. CodefyPHP is a PHP framework for codefying and building complex applications using Domain-Driven Design, CQRS, and Event Sourcing.

Visit product View on Hacker NewsSep 20, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, 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

73%73% predicted probability of success on Indie Hackers, 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

60%60% 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

45%45% 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

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.

Incorrect prediction on native model

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