Awesome List Updates on Feb 08, 2025

7 awesome lists updated today.

1. Awesome Magento2

Magewire PHP - A Laravel Livewire port for building complex AJAX-based components with ease. Used by the Hyvä Checkout.

Yireo LokiComponents (⭐8) - A library for building AJAX-driven form components with ease. Used by the Yireo Loki Checkout.

wezterm (⭐21k) - A GPU-accelerated cross-platform terminal emulator and multiplexer

ghstats (⭐156) ⚠ - Dashboard for tracking GitHub repos traffic history longer than 14 days. MIT Docker

BookBounty (⭐234) ⚠ - Retrieve missing Readarr books from Library Genesis. MPL-2.0 Docker

SAMA - Next-Gen self-hosted chat server and clients. (Demo, Source Code (⭐122), Clients (⭐31)) GPL-3.0 Nodejs/Docker

Wakupator (⭐145) - Wake On LAN Machine Manager based on network traffic. MIT C

kener - Status page with incident management, easy to use and customize. (Demo, Source Code (⭐4.1k)) MIT Nodejs/Docker

jsonforms (⭐2.5k) - Customizable JSON Schema-based forms with React, Angular and Vue support out of the box.

ng-formworks (⭐5) - An Angular JSON Schema Form builder, similar to, and mostly API compatible with, Angular Schema Form, React JSON Schema Form, and JSON Form.

ngx-reactive-form-class-validator (⭐24) - A lightweight library for dynamically validate Angular reactive forms using class-validator (⭐11k) library.

angular-compile (⭐90) - Angular Dynamic Compile. Convert strings to Angular components.

Analysis of Langevin Monte Carlo via Convex Optimization - Journal of Machine Learning Research, 2019. [All Versions]. This paper provides new insights on the Unadjusted Langevin Algorithm. The authors show that this method can be formulated as the first order optimization algorithm for an objective functional defined on the Wasserstein space of order $2$. Using this interpretation and techniques borrowed from convex optimization, the authors give a non-asymptotic analysis of this method to sample from log-concave smooth target distribution on $\mathbb{R}^d$. Based on this interpretation, the authors propose two new methods for sampling from a non-smooth target distribution. These new algorithms are natural extensions of the Stochastic Gradient Langevin Dynamics (SGLD) algorithm, which is a popular extension of the Unadjusted Langevin Algorithm for largescale Bayesian inference. Using the optimization perspective, the authors provide non-asymptotic convergence analysis for the newly proposed methods.