Search Results for "workflow"

Dead simple plugin to center the currently focused buffer to the middle of the screen.

HARPOON 2 is a highly popular Neovim plugin created by ThePrimeagen that enhances editor navigation by letting users mark and quickly jump to frequently used files and terminals within their Neovim workflow. Unlike traditional global marks in Vim, Harpoon’s marks are per-project and automatically updated, meaning they follow files even as content changes, which streamlines switching between contextually relevant files during development. It comes with an intuitive menu system that lists marked files and offers commands to add, remove, or navigate to those files with minimal keystrokes. ...

Continuously assess cloud security posture by flagging misconfigurations and suspicious activity. Consolidate container and host scanning in a single workflow. Automate scanning locally in your CI/CD tools without images leaving your environment and block vulnerabilities pre-deployment. Visualize all network communication across apps and services. Apply microsegmentation by automating Kubernetes-native network policies. Unify threat detection and incident response across containers, Kubernetes, and cloud with out-of-the-box Falco rules leveraging syscalls, Kubernetes audit logs and cloud logs. ...

Show register content when you try to access it in Neovim. Written in Lua. This plugin adds an interactive and visually pleasing UI for selecting what register item to paste or use next. It offers basic syntax highlighting, a preview, and an equally (if not more) efficient experience using Neovim registers. One simply uses " or CtrlR as one would normally, and then enjoys the benefit of seeing the contents of all filled registers without having to use the :reg command beforehand. It...

char-rnn is a classic codebase for training multi-layer recurrent neural networks on raw text to build character-level language models that learn to predict the next character in a sequence. 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...