A Tool to Handle the Errors Generated by the Rust Compiler

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A lot of programmers have given up learning Rust because of the error messages it generates. Furthermore, the concept of borrowing and ownership are relatively new and many aren't able to climb this "steep learning curve". Although tools currently exist to improve code quality in terms of indentation, performance etc. there aren't much tools in cargo which can handle a panicking developer. This is my humble attempt at generating a motivational text generator for a beginner learning Rust to keep going on this journey. Install: `cargo install cargo-jdi` Usage: `cargo jdi` Reminder: Just do it! Nothing is impossible!. URL: https://crates.io/crates/cargo-jdi

Visit product View on Hacker NewsJul 28, 2019

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

55%55% predicted probability of success on Indie Hackers, 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, intuitive

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

49%49% 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, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, code · Missing: mac, agents, macos

33%33% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

18%18% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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