Awesome List Updates on Sep 18, 2025

7 awesome lists updated today.

1. Awesome R

VidCrop - A simple video cropping tool that supports multiple formats and precise trimming.

Musicer - A lightweight local music player that supports multiple formats for enjoying music anytime.

Nearly Free Speech - 25+ programming languages, pay for what you use.
- SBCL and GNU CLISP

parcom/email (⭐76) - types and parsers for RFC5322 email addresses. The implementation is RFC-compliant and particularly memory-efficient for well-behaved addresses.
- NEW As of 2025

svelte-streamdown (⭐4) - Port of streamdown. An all in one markdown renderer optimized for streaming with built in styles, math, mermaid, code highlighting support and more.

JesterKit EXE (⭐199) - An adapter to distribute your SvelteKit web app as a single executable binary with zero runtime dependencies. Unlike static builds, it preserves all Kit features like SSR, API endpoints, server hooks, etc.

Knowledge-Based Embodied Question Answering - IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. [All Versions]. This paper proposes a novel Knowledge-based Embodied Question Answering (K-EQA) task, in which the agent intelligently explores the environment to answer various questions with the knowledge. Different from explicitly specifying the target object in the question as existing EQA work, the agent can resort to external knowledge to understand more complicated question such as “Please tell me what are objects used to cut food in the room?”, in which the agent must know the knowledge such as “knife is used for cutting food”. To address this K-EQA problem, a novel framework based on neural program synthesis reasoning is proposed, where the joint reasoning of the external knowledge and 3D scene graph is performed to realize navigation and question answering. Especially, the 3D scene graph can provide the memory to store the visual information of visited scenes, which significantly improves the efficiency for the multi-turn question answering. Experimental results have demonstrated that the proposed framework is capable of answering more complicated and realistic questions in the embodied environment. The proposed method is also applicable to multi-agent scenarios.