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Patchouli JSON, an ergonomic DSL for Swift JSON Patch
Hacker NewsPatchouli JSON, an ergonomic DSL for Swift JSON Patch
I made this after working on various Swift projects that used JSON Patch and wishing for a way to conveniently patch JSON in code. Could be useful if your Swift project wants to deliver canned customised JSON payloads, or to shape/modify JSON passing by. Uses https://github.com/raymccrae/swift-jsonpatch for the actual JSON patching. Has a tiny generic patching engine at its core, which you could use to write patchers for anything (they get to use the DSL too).
Share cardActual performance
1points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released51%51% 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, personal43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: tiny, code · Missing: mac, agents, macos27%27% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive26%26% 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, revenue13%13% 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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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