Stripe-quality API client libraries with a single button click

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TL;DR- Libninja will generate world-class client libraries for you, without you needing to fiddle with openapi-generator, maintain build pipelines, and so forth. If you just want Rust, reach out, and I'll create your client library at no cost, because I want to help Rust's ecosystem grow. -- Hi HN, I got into Rust in a big way about 9 months ago. While the language is quickly maturing, one large gap in the ecosystem is immature or non-existent client libraries for common API services like Plaid, Stripe, Twilio, Github, Slack, and so on. The existing openapi-generator templates create Rust libraries that don't even compile out of the box, so I started down the rabbit hole of creating a Rust-based toolchain to generate Rust client libraries based on an OpenAPI spec. Rust is an incredible language to build codegen tools in, because of algebraic data types, Rust macro system, and Rust's incredibly powerful libraries for AST transformation (quote, syn, and more - almost solely due to dtolnay - thank you!) Before long, I realized it's a common painpoint for API companies to maintain build pipelines, tweak openapi-generator templates, and so forth, to get client library generation working. I added support for more languages, and soon after, Libninja was born. If you're an API company, Libninja will generate world-class client libraries for you. Client libraries: - help your users integrate faster (meaning you get revenue faster) - reduce the number of bug reports and support requests (because there's no ambiguity about using your API) - Give your users a phenomenal developer experience (editor autocomplete, API documentation in docstrings, and more) PS- If you just want a Rust client, reach out, and I'll create your client library at no cost, because I want to help Rust's ecosystem grow.

Visit product View on Hacker NewsSep 12, 2022

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

87%87% 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.

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

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: soon, users · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: month, users, way · Missing: mobile apps, ios, personal

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

15%15% 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.

Correct prediction on native model

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