Rust+Svelte=Terminal

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Link to project: https://github.com/iondodon/manter Demo: https://youtu.be/ZjAXMMMoKGg This project aims to create a terminal with functionalities that improve the experience of using terminals. Examples of useful functionalities would be autocomplete suggestion, showing the current branch of a project, prepared scripts that can be reused, and others. You can come up with your ideas. The project uses technologies such as Svelte on the front-end, which offers flexibility regarding the implementation of the interface. On the back-end side, the most important part is the PTY. All the back end is implemented in Rust. The basic framework of the project is Tauri. At the moment the terminal has suggestions only for some commands (cd, ls). To support more commands it is needed to add them in the Manter's "library" located at src/cli/library/library.ts. It is possible to have custom script based suggestions. For example if we write in the terminal “git checkout” and after we press Space, a dropdown will appear with all available branches. A good analogy to understand the purpose of this project is the following - traditional terminals are like simple code editors while this terminal is like an IDE. Anyone is welcome to contribute to this project with and ideas. Imagine the terminal you would like to use. Feel free to open any issue with suggestions and bugs.

Visit product View on Hacker NewsFeb 15, 2023

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

78%78% 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.

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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

46%46% 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

40%40% 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

11%11% 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.

Incorrect prediction on native model

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