Awesome List Updates on Jan 16, 2025

10 awesome lists updated today.

1. Awesome Selfhosted

XWiki - Second generation wiki that allows the user to extend its functionalities with a powerful extension-based architecture. (Demo, Source Code (⭐1.1k)) LGPL-2.1 Java/Docker/deb

Terrateam - GitOps-first Terraform automation with pull request-driven workflows, project isolation via self-hosted runners, and layered runs for ordered operations. Free for up to 3 users.

scad (⭐3) - Shell Colorized Aliases for Docker/Podman (SCAD). Defines docker / podman aliases and functions. These aliases adhere to the newer style of organizing and invoking docker with management commands rather than the notoriously confusing “random solo commands”. Requires GRC (⭐2k) and jq (⭐32k).

xxh (roman-geraskin) (⭐5) - plugin for xxh-shell-zsh (⭐39) that copies your ~/.zshrc to a remote host and sources it with xxh-shell-zsh (⭐39).

zpy (⭐91) - Manage Python environments, dependencies, and isolated app installations, with a ZSH frontend for uv (⭐61k) or pip-tools (⭐7.9k).

more-completions (⭐46) - 13500 ZSH compsys completions! Most were generated by python scripts that parsed --help output and man page output. As such they are of varying quality. Architecture prefixed completions are in the architecture_src directory.

completion-generators (⭐1) - Has a table of tool names and the commands for outputting completion scripts for those tools. On every load, will check that table and run the completion command for every tool found in your $PATH and save its output to a file _. If the path of this repo is in $fpath, completions will work immediately.

vite-plugin-ssr-config (⭐1) - Configure server-side rendering (SSR) with React, React-Query, React-Router.

vite-plugin-builder (⭐2) - Enable dual compilation for Server-Side Rendering (SSR) and Client-Side Rendering (CSR).

NotchNook - Customizes your Mac's menu bar to seamlessly integrate with the notch design.

wurli/visimatch.nvim (⭐60) - Adds highlights to any text matching the current selection in visual mode.

wurli/split.nvim (⭐30) - Provides a mapping to split text by delimiter, giving an inverse of the native J command.

Human-level concept learning through probabilistic program induction - Science, 2015. [All Versions]. [Preprint]. [Supplementary Material]. People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms—for action, imagination, and explanation. This work presents a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world’s alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches.