Parametrized recursive descent parser in JS

Hacker News

I wrote an abstract recursive descent parser in JS which accepts an array of terminal (in regexp form, or as a list of string literals) and a list of non-terminal definitions and returns the function that will parse the text. The parser "generator" has just 125 lines of code and it is an extremely lightweight solution to quickly produce purpose made languages in the browser without need for any tooling. Together with `` template strings to write your custom made language code in, it makes for a lot of fun in JS. :D Someone asked for the code in the recent thread about parsers. You need to search for it a bit though. CTRL+U :) https://megous.com/dl/parser/tests/index.html

Visit product View on Hacker NewsMar 21, 2017

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2points

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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

45%45% 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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

13%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, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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