Claude Code + OpenSpec 正在加速 AICoding 落地：从模型博弈到工程化的范式转移｜得物技术

BDD styled end-2-end testing framework for NodeJS. Powered by Playwright, Puppeteer, WebDriver

Concourse resource for Kubernetes

Program to display 2D structures of molecules, and create, edit and match SMARTS patterns. Think picto, with a slider.

Files Type Name Latest commit message Commit time README.markdown NOTE: Work on v2 of Glimpse has shifted toGlimpse/Glimpse.Prototype The Diagnostics

CLion 2021.2 Shortcuts: Windows Get CLion Learning Center Get started Configure the IDE Configure projects Work with source code Static code analysis

CLAE: Representation Learning with Class Level Autoencoder. The source code is for the following paper which will be available soon! Hui Yu, Kai Wang, Yan Li, Wu Zhao. Representation Learning with Class Level Autoencoder for Intelligent Fault Diagnosis, IEEE Signal Processing Letters, 2019.

The Main Aim of this project is to segment and cluster an audio sample based on speaker when number of speakers are not known before hand. Main challenge in the process of speaker recognition is separting audio based on speaker.It can enhance the readability of an automatic speech transcription by structuring the audio stream into speaker turns and, when used together with speaker recognition systems, by providing the speaker's true identity.Other challenges are due to multiple speakers present

openEHR Service Model specifications. This repository contains the document and model sources (Asciidoctor and MagicDraw) for the openEHR Service Model (SM) specifications. The published form may be accessed from the openEHR website SM specifications index page.