Rust-loguru – A flexible Rust logging library like Python's Loguru

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I've just released rust-loguru, a logging library for Rust that aims to combine intuitive API design (inspired by Go/Python's Loguru) with Rust's performance characteristics. Some highlights: - Consistently outperforms the standard log crate by 50-80% in benchmarks. - Multiple log levels and an extensible handler system -File rotation with better performance than log4rs and other alternatives - Error handling utilities and context helpers I built this because I wanted a logger that was both ergonomic to use and high-performance. The benchmarks comparing it to slog, log, and tracing are available in the repo. I'd appreciate any feedback, especially on the API design and performance characteristics. Are there any features you'd like to see added to the roadmap? [GitHub Link: https://github.com/j-raghavan/rust-loguru ] [Crates.io: https://crates.io/crates/rust-loguru ]

Visit product View on Hacker NewsApr 28, 2025

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

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

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

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

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

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

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

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

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

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

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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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