Weld VS weather_bandit

Compare Weld vs weather_bandit and see what are their differences.

weather_bandit

Dragon App (Weather bandit a personal weather forecast expert with a mask) (by bunni-service)
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Weld weather_bandit
- 2
289 2
- -
0.0 0.8
5 months ago over 2 years ago
Rust Rust
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Weld

Posts with mentions or reviews of Weld. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning Weld yet.
Tracking mentions began in Dec 2020.

weather_bandit

Posts with mentions or reviews of weather_bandit. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-01-03.

What are some alternatives?

When comparing Weld and weather_bandit you can also consider the following projects:

parity-bitcoin - The Parity Bitcoin client

swhkd - Sxhkd clone for Wayland (works on TTY and X11 too)

basiliq - REST API server that abstracts the need to write CRUD methods by exposing a standardized API to interact with a Postgres database

rust-cli-boilerplate - Rust project boilerplate for CLI applications

httpmock - HTTP mocking library for Rust.

AppFlowy - AppFlowy is an open-source alternative to Notion. You are in charge of your data and customizations. Built with Flutter and Rust.

connect_rusty - Dragon App (Connect Rusty a classic two-player connection board game)

bnf - Parse BNF grammar definitions

rust-sokoban - Rust Sokoban book and code samples

tangram - Tangram makes it easy for programmers to train, deploy, and monitor machine learning models.