Sparser – A Multilanguage Parser

Hacker News

* https://sparser.io * https://github.com/Unibeautify/sparser This is my attempt at creating a universal language parser. It attempts to solve a couple of problems: * Support multiple languages * Recursively extend support to languages embedded within other languages * Output a uniform format for all supported languages This is a personal project so any feedback would be helpful. Something interesting I found after I built it is that this parser is not as fast to write output as many other JavaScript parsers, but its output is much faster to read from due to the simplicity and predictability of the format.

Visit product View on Hacker NewsMay 3, 2019

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

64%64% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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, intuitive

36%36% 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, revenue

19%19% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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