perl-lsp – annotation free static analysis for Perl

Hacker News

Built in Rust on top of ts-parser-perl and tower-lsp. Witness bags with graph traversal (value edges, call flow edges). How it works: - builds witnesses and scopes by walking the parse tree. Fully known values get concrete types, otherwise they get an edge pointing to the last witness we have for this symbol - a worklist runs to a fixed point: reducers fold each symbol's witnesses and chase edges. At this point, the witness bag is complete and ready to use - all downstream features are queries into the bag, following along the graph edges that are relevant Extensible - you can bring your own Rhai plugin to shortcut common patterns (exporters and OOP frameworks), or even add new types of definitions (worker tasks and web routes). The build/query timing distinction with edges lets you late bind types, which is duly similar to how Perl itself works: `Foo->new` isn't known if it's the constructor for Foo or calling a method on the sub name `Foo`; late binding lets you make that call after the worklist folds.

Visit product View on Hacker NewsJun 23, 2026

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

79%79% predicted probability of success on Product Hunt, 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

48%48% 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 · Missing: mobile apps, ios, personal

46%46% 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, podcasting

46%46% 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, intuitive

42%42% 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

17%17% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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