HackerNoon

Matryoshka RLM Tested: Can Coding Agents Query Huge Files Without RAG?

I drove its lattice-mcp tools by hand from a TypeScript client, checked every answer against grep, and hit five gotchas.
Beebom

All Extraction Gizmos in Fortnite and How to Get Them

Extraction Gizmos are non-tradable consumable items that help with extractions and boost combat capabilities in Fortnite. The Portable Extractor and Extraction Accelerator are by far the best Gizmos ...

Easily Build FreeCAD STL Files Using ChatGPT 6 Astra and Text Prompts

Build custom CAD designs with natural language prompts by integrating ChatGPT 6 Astra and FreeCAD. Output precise Python code for 3D printing.
GitHub

speech-emotion-recognition

Open-source SenseVoiceSmall model for Mandarin, Cantonese, English, Japanese, and Korean ASR, language ID, emotion recognition, and audio event detection.
blog.csdn

沃特金斯鲸类声学分类：MFCC与梅尔频谱图预处理实战

若不做统一，后续 MFCC 提取时 librosa.feature.mfcc () 默认使用 sr=22050 ，会导致频率轴严重畸变——比如 1kHz 实际成分在 44.1kHz 录音中对应第 44 个 Mel bin，但在 8kHz 录音中被压缩到第 8 个 bin，模型学到的“频率位置”完全是错的。
blog.csdn

基于WEKWS的端到端低资源语音唤醒实践：从训练到部署

文章浏览阅读49次。语音唤醒（KWS）是智能设备低功耗监听的基石，它需要在连续音频流中精准识别特定唤醒词，同时兼顾极低的功耗与误唤醒率。传统级联方案流程繁琐且误差累积，而端到端模型通过单一神经网络直接完成关键词分类，大幅简化了训练与部署流程。WEKWS作为这一范式的代表性开源 ...