My New Years project, Parcoa: Objective-C Parser Combinators

Hacker News

My experience parsing text on iOS is sullied by exposure to Haskell's Parsec and Python's Parcon. Given the ability to create pure, composable, functions using Objective-C blocks, I thought Cocoa an ideal environment for parser combinators. I've built a somewhat easy to read internal DSL for combining parsers. For example: ParcoaParser *yes = [Parcoa string:@"yes"]; ParcoaParser *no = [Parcoa string:@"no"]; ParcoaParser *answer = [yes or: no]; https://github.com/brotchie/Parcoa

Visit product View on Hacker NewsJan 4, 2013

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs

44%44% 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

21%21% 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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