Awesome List Updates on May 21, 2025

9 awesome lists updated today.

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1. Awesome Angular

Cheatsheet / Google Developer Experts

angular-interview-questions (⭐16) - Angular interview questions and answers to help you prepare for your next technical interview in 2025.

Online Training / Google Developer Experts

ng.guide - Learn Angular by building real-world apps.

HTTP / Google Developer Experts

ngx-signal-pagination (⭐1) - Pagination for Angular, powered by signals.

SEO / Google Developer Experts

seo-manager-pro (⭐1) - A powerful SEO Manager for Angular, React, Vue, and Vanilla JS projects. Easily set meta tags, Open Graph tags, Schema.org structured data, canonical URLs, robots meta, and more!

Drag and Drop / Google Developer Experts

cdk-drag-snap-to-point (⭐7) - A demo to showcase cdkDrag features to achieve drop only on certain points.

Mixed utilities / Google Developer Experts

rxap - Reactive Application Platform, or RxAP for short, is a collection of software modules and tools. With the help of RxAP, the development effort of web and cloud applications can be significantly reduced.

2. Awesome D

Version Managers

dvm (⭐58) - A small tool to install and manage DMD (self-hosting) compiler.

ldcup (⭐1) - A small tool to install and manage LDC2 (LLVM backend) compiler.

3. Awesome Keycloak

Community Extensions

Identity provider for German eID governikus/keycloak-eid-identity-provider

4. Awesome Playcanvas

Games / Browser Games

The Walking Dead: Those Beyond - A community survivor camp experience game.

5. Awesome Capacitor

Capgo plugins

Native Market (⭐7) - A native market plugin for linking to google play or app store.

Native Biometric (⭐54) - This plugin gives access to the native biometric apis for android and iOS

Camera Preview (⭐17) - Camera preview

Updater (⭐609) - Live update for capacitor apps

Uploader (⭐15) - Background Uploader for capacitor apps

Purchases (⭐192) - In-app Subscriptions Made Easy with RevenueCat sdk

Flash (⭐15) - Switch the Flashlight / Torch of your device.

Screen Recorder (⭐13) - Record device's screen

Crisp (⭐8) - Crisp native SDK for capacitor

Native Geocoder (⭐28) - Capacitor plugin for native forward and reverse geocoding

In App Browser (⭐91) - Capacitor plugin in app browser

Mute (⭐7) - Detect if the mute switch is enabled/disabled on a device

Native Audio (⭐47) - A native plugin for native audio engine

Shake (⭐12) - Detect shake gesture in device

Navigation Bar (⭐12) - Set navigation bar color for android lolipop and higher

IVS Player (⭐3) - Ivs player for capacitor app

Indicator (⭐1) - hide and show home button indicator in Capacitor app

Native Purchases (⭐12) - In-app Subscriptions Made Easy

Data Storage (⭐88) - Capacitor SQLite Storage

Usage Stats Manager (⭐0) - Capacitor plugin for android usage stats manager

Streamcall (⭐3) - Capacitor plugin for streamcall

Social Login (⭐104) - Capacitor plugin for social login

JW Player (⭐2) - Capacitor plugin for jw player

Ricoh360 Camera (⭐0) - Capacitor plugin for ricoh360 camera

6. Awesome Agi Cocosci

Domain Specific Language / Design Practises

Abstract Hardware Grounding Towards the Automated Design of Automation Systems - ICIRA'24, 2024. [All Versions]. [Preprint]. Crafting automation systems tailored for specific domains requires aligning the space of human experts’ semantics with the space of robot executable actions, and scheduling the required resources and system layout accordingly. Regrettably, there are three major gaps, fine-grained domain-specific knowledge injection, heterogeneity between human knowledge and robot instructions, and diversity of users’ preferences, resulting automation system design a case-by-case and labour-intensive effort, thus hindering the democratization of automation. This work refers to this challenging alignment as the abstract hardware grounding problem, where the authors firstly regard the procedural operations in humans’ semantics space as the abstraction of hardware requirements, then the authors ground such abstractions to instantiated hardware devices, subject to constraints and preferences in the real world—optimizing this problem is essentially standardizing and automating the design of automation systems. On this basis, this work develops an automated design framework in a hybrid data-driven and principle-derived fashion. Results on designing self-driving laboratories for enhancing experiment-driven scientific discovery suggest the proposed framework’s potential to produce compact systems that fully satisfy domain-specific and user-customized requirements with no redundancy.

Domain Specific Language / Design Automation

Hierarchically Encapsulated Representation for Protocol Design in Self-Driving Labs - ICLR'25, 2025. [All Versions]. [Project]. Self-driving laboratories have begun to replace human experimenters in performing single experimental skills or predetermined experimental protocols. However, as the pace of idea iteration in scientific research has been intensified by Artificial Intelligence, the demand for rapid design of new protocols for new discoveries become evident. Efforts to automate protocol design have been initiated, but the capabilities of knowledge-based machine designers, such as Large Language Models, have not been fully elicited, probably for the absence of a systematic representation of experimental knowledge, as opposed to isolated, flatten pieces of information. To tackle this issue, this work proposes a multi-faceted, multi-scale representation, where instance actions, generalized operations, and product flow models are hierarchically encapsulated using Domain-Specific Languages. The authors further develop a data-driven algorithm based on non-parametric modeling that autonomously customizes these representations for specific domains. The proposed representation is equipped with various machine designers to manage protocol design tasks, including planning, modification, and adjustment. The results demonstrate that the proposed method could effectively complement Large Language Models in the protocol design process, serving as an auxiliary module in the realm of machine-assisted scientific exploration.