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Sword – A compile time dependency injection library for Swift
Hacker NewsSword – A compile time dependency injection library for Swift
Hi HN! I made a compile time dependency injection library for Swift, inspired by Dagger. As you declare dependencies and specify how to satisfy them using Swift Macros, Sword automatically generates dependency injection code at compile time. Sword walks through your code and validates dependency graphs, ensuring that every object's dependencies can be satisfied, so there are no runtime errors. I would love to hear what you think about it!
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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, using, code · Missing: agents, macos, agent73%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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released42%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 · Missing: mobile apps, ios, personal37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive37%37% 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, revenue14%14% 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, crypto14%14% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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