Asdf Clone Written in Rust

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I think that asdf ( https://asdf-vm.com ) was a great idea for a project. It helps consolidate installing and running different programming languages into a similar UX. It also is built with a plugin interface that makes it easy to build support for new languages. However it is so slow. I was just testing `node -v` and it was taking ~900ms. That kind of overhead is completely unusable. My shell prompt uses runtimes inside of it for various things so this effectively makes every command take multiple seconds to complete. So I rebuilt it in Rust but using the same plugin ecosystem so it should be a drop-in replacement. I also added a couple of features that I wanted from asdf (aliases and fuzzy-matching). Let me know what you think! Just know that people have only been using this for a few days so if you see any bugs, they're likely not big hairy issues, just overlooked edge-cases and will be fixed soon. https://github.com/jdxcode/rtx

Visit product View on Hacker NewsJan 30, 2023

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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

46%46% 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

45%45% 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

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.

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