Query your Rust codebase and generate types for anything

Hacker News

Hello HN! As a long-time professional Rust developer. I've always been frustrated by the difficulty and "hackiness" of producing bindings to other languages, whether a frontend, an FFI library, etc. Not just in Rust but in any language. After many years of trying existing solutions and trying to make my own, I've finally developed a solution I'm very happy with. RTK (aka Rust Type Kit) allows you to write Lua scripts that perform queries on your code, such as method calls to Axum's `.route`, function definitions, and more, and then receive rich type information including all argument types, function paths, proc macro attributes, and more. Your Lua script can then read this information and emit an output file in any language of your choosing. Or, you can emit compiler errors and use it as a linter of sorts. You can even directly re-emit Rust code itself and use this as a richer proc macro solution! The code example is a bit verbose, so I encourage you to take a look at the repo's README. I look forward to hearing your thoughts, or any usecases you may come up with!

Visit product View on Hacker NewsJun 28, 2025

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

76%76% 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 NewsStrong engagement from HN community · Strong signals: exist, lua, existing · Missing: https docs, excited, just released

68%68% 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 · Strong signals: including · Missing: supports, reddit linkedin, podcasting

49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

27%27% 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

12%12% 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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