Stack Error – Simple and informative errors for Rust

Hacker News

Stack Error is a pragmatic error handling library for Rust that provides helpful messages for debugging, and structured data for runtime error handling. Features: * Informative error messages: stack error messages and optionally add file/line context to your messages. This helps convey not just what went wrong, but also how it went wrong, making debugging faster. * Programmatic error handling: include optional error codes and URIs for robust runtime handling. * Library-Friendly: define custom error types easily while staying compatible with Rust’s error ecosystem. If designing good error structures for your projects slows you down, and you need something more library-friendly and structured than anyhow, Stack Error might be what you’re looking for.

Visit product View on Hacker NewsNov 21, 2024

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

57%57% 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: compatible · Missing: supports, reddit linkedin, podcasting

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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