animaterm - a Rust TUI library

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Hi all, I wanted to share with you a library that I have created. It is written in Rust, a language that I have been learning recently. You can also check it on crates.io: https://crates.io/crates/animaterm With this library you can create animated user interfaces within terminal. UI elements can be stored as regular text files (with ANSI escape codes) allowing for community mods. It also reads user input. You can follow a link below for more info. Check the docs here: https://docs.rs/animaterm/0.1.1/animaterm/ In order to see complete documentation one must build it locally with: cargo doc --open Also this lib includes a studio app where you can build your UI, it looks like this: https://ibb.co/1Xypfy9 Feedback is appreciated :)

Visit product View on Hacker NewsNov 10, 2022

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

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

48%48% 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, podcasting

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

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

46%46% 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

17%17% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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