Awesome List Updates on Apr 04, 2025

10 awesome lists updated today.

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1. Awesome Cpp

Miscellaneous

• Hexi (⭐280) - Header-only, lightweight C++ library for binary streaming & serialization. [Apache-2.0/MIT]

2. Awesome Angular

Authentication / Google Developer Experts

• @badisi/ngx-auth (⭐13) - Authentication and authorization support for Angular based desktop and mobile applications.

Layout Components / Google Developer Experts

• @berg-layout/angular - This is the Angular version of berg-layout (⭐36).

Inspired by Angular / Google Developer Experts

• fornax (⭐12) - A lightweight, opinionated, and highly customizable Bun-powered full-stack web framework designed to simplify building single-page applications with custom components, routing, and flexible styling options.

3. Awesome Generative Deep Art

Everything to Markdown to LLMs

• Mistral OCR / Mistral AI: A document understanding API

4. Static Analysis

Programming Languages / Other

• Fortitude — Fortran linter, inspired by (and built on) Ruff, and based on community best practices. Supports latest Fortran (2023) standard.

• Prusti ⚠️ — A static verifier for Rust, based on the Viper verification infrastructure. By default Prusti verifies absence of panics by proving that statements such as unreachable!() and panic!() are unreachable.

Other / Other

• Twiggy — Analyzes a binary's call graph to profile code size. The goal is to slim down wasm binary size.

• packj (⭐666) ⚠️ — Packj (pronounced package) is a command line (CLI) tool to vet open-source software packages for "risky" attributes that make them vulnerable to supply chain attacks. This is the tool behind our large-scale security analysis platform Packj.dev that continuously vets packages and provides free reports.

• dennis (⭐50) ⚠️ — A set of utilities for working with PO files to ease development and improve quality.

5. Awesome Selfhosted

Software / Document Management

• Documenso - Digital document signing platform (alternative to DocuSign). (Source Code (⭐11k)) AGPL-3.0 Nodejs/Docker

Software / Miscellaneous

• Yamtrack (⭐994) ⚠ - Media tracker for movies, tv shows, anime, manga, video games and books. (Demo (⭐994)) AGPL-3.0 Docker/Python

Software / Note-taking & Editors

• Docs - Collaborative note taking, wiki and documentation platform that scales. (Source Code (⭐13k)) MIT K8S

Software / Software Development - Feature Toggle

• Flagsmith - Dashboard, API and SDKs for adding Feature Flags to your applications (alternative to LaunchDarkly). (Source Code (⭐5.8k)) BSD-3-Clause Docker/K8S

• GO Feature Flag - Simple, complete, and lightweight feature flag solution (alternative to LaunchDarkly). (Source Code (⭐1.7k)) MIT Go

6. Awesome Agriculture

Automation and Robotics

• Ant Robotics - development from Ecoterra bot

• Acorn Rover - precision farming rover, Odrive, Python.

• Earth Rover - Ag AGV ROS1 precision farming rover

• EcoTerra Bot - Delta & Rover

• FarmBot - Open source precision gardening project.

• Romi project - Europe-funded research project

• Weedinator - Line following weeding robot

Datasets

• Growstuff - Record keeping & crop database, nice API

• TERRA REF - 1PB public domain high resolution sensor data from sorghum breeding trials (data publication with large files available on globus.org at ncsa#terra-public)

7. Awesome Lit

Community

• Discord

8. Awesome Php

Table of Contents / Middlewares

• PSR-15 Middlewares (⭐412) - Inspiring collection of handy middlewares.

9. Awesome Nix

Development / Discovery

• MCP-NixOS (⭐178) - An MCP server that provides AI assistants with accurate information about NixOS packages, options, Home Manager, and nix-darwin configurations.

10. Awesome Agi Cocosci

Concepts / AI Concept Representation

• Connecting Touch and Vision via Cross-Modal Prediction - CVPR'19, 2019. [All Versions]. [Project (⭐74)]. Humans perceive the world using multi-modal sensory inputs such as vision, audition, and touch. This work investigates the cross-modal connection between vision and touch. The main challenge in this cross-domain modeling task lies in the significant scale discrepancy between the two: while our eyes perceive an entire visual scene at once, humans can only feel a small region of an object at any given moment. To connect vision and touch, this work introduces new tasks of synthesizing plausible tactile signals from visual inputs as well as imagining how we interact with objects given tactile data as input. To accomplish the goals, the authors first equip robots with both visual and tactile sensors and collect a large-scale dataset of corresponding vision and tactile image sequences. To close the scale gap, the authors present a new conditional adversarial model that incorporates the scale and location information of the touch. Human perceptual studies demonstrate that the model can produce realistic visual images from tactile data and vice versa.

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