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This is the repository of tl, the compiler for Teal, a typed dialect of Lua. The core compiler has no dependencies and is implemented as a single tl. lua file which you can load into your projects. Running tl. loader() will add Teal support to your package loader, meaning that require() will be able to run .tl files. Alternatively, you can find pre-compiled binaries for Linux x86_64 and Windows x86_64 at the releases page. The packages contain a stand-alone executable that can run Teal programs (without the need of a separate Lua installation) and also compile them to Lua.

...It supports several output formats (Text, TAP, JUnit, etc.) that can be used directly or work with continuous integration platforms (Jenkins, Maven, etc.). LuaUnit may be installed as a rock or directly added to your project. For simplicity, LuaUnit is contained into a single file and has no external dependency. LuaUnit may also be used as an assertion library, to validate assertions inside a running program. In addition, it provides a pretty stringifier that converts any type into a nicely formatted string (including complex nested or recursive tables).

...Tabline is tabby.nvim and status line is feline.nvim. You can find my config for tabby and feline here as well as the auto-generated highlight groups here. If you would like a single consumable file that contains that can be added to your own config check out misc/feline.lua and misc/tabby.lua.

Luacheck is a static analyzer and a linter for Lua. Luacheck detects various issues such as usage of undefined global variables, unused variables and values, accessing uninitialized variables, unreachable code and more. Most aspects of checking are configurable: there are options for defining custom project-related globals, for selecting set of standard globals (version of Lua standard library), for filtering warnings by type and name of related variable, etc. The options can be used on the...

...It supports common recurrent architectures including vanilla RNNs as well as LSTM and GRU variants, letting users compare behavior and output quality across model types. It is straightforward: you provide a single text file, train the model to minimize next-character prediction loss, then sample from the trained network to generate new text one character at a time in the style of the dataset. The project is designed for experimentation, offering tunable settings for depth, hidden size, dropout, sequence length, and sampling temperature to control creativity and coherence. ...