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Simple crate to use anyhow with Tauri
Hacker NewsSimple crate to use anyhow with Tauri
Hey, I've created a crate that makes it easier to return anyhow errors from your tauri commands! It's only +-100 lines of code, but made my experience with tauri sooo much nicer. Thanks to this small size, it's also easy to copy-paste it, if you don't want to rely on "another package". Feel free to leave feedback! ps: I've linked the GitHub repo, the crate is published here: https://crates.io/crates/anyhow-tauri
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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos62%62% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released44%44% 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, personal31%31% 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, revenue16%16% 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, crypto0%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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