Tr
Tree-sitter Integration for Swift
Hacker NewsTree-sitter Integration for Swift
I have created a Swift package ( https://github.com/daspoon/tree-sitter-kit ) enabling tree-sitter parsers to be written in Swift; specifically, as an array of production rules which map symbol types to pairings of syntax expression and type constructor. A member macro derives a tree-sitter grammar and embeds the generated parser in its expansion. This project is a work in progress, and I will be grateful for any feedback. Thanks, Dave
Share cardActual performance
52points
10comments
Made the leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: mac · Missing: agents, macos, agent39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting32%32% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit14%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, crypto7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
Similar products
A
A Swift Package Manager Xcode Integration42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
A Swift Package Manager Xcode Integration
Hacker News1An
Angular 2 AoT Compilation and Tree Shaking Example46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Angular 2 AoT Compilation and Tree Shaking Example
Hacker News9Im
Implementation of a 3-pointer AVL Tree46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Implementation of a 3-pointer AVL Tree
Hacker News2Vi
Visualizing Binary Tree59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Visualizing Binary Tree
Hacker News2Ld
Ldd as a Tree and Much More49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Ldd as a Tree and Much More
Hacker News2To
Toy LISP compiler in C# (LINQ) expression tree73%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Toy LISP compiler in C# (LINQ) expression tree
Hacker News2Tr
Tree Dangler58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Tree Dangler
Hacker News1I
I ported Tree-sitter to Go57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
I ported Tree-sitter to Go
Hacker News222KD
KD-tree recommender system in Literate Coffeescript49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
KD-tree recommender system in Literate Coffeescript
Hacker News2Tr
Trailer Swift43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Trailer Swift
Hacker News32