🎯 microsoft/VibeVoice landed on trending. Worth a proper look.

🔗 https://github.com/microsoft/VibeVoice
📝 Open-Source Frontier Voice AI
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The VibeVoice GitHub repository is an open-source collection of cutting-edge voice AI models, including both Text-to-Speech (TTS) and Automatic Speech Recognition (ASR) technologies. At its core, VibeVoice leverages a next-token diffusion framework, combining the power of a Large Language Model (LLM) with a diffusion head to generate high-fidelity acoustic details. The repository features several key models, including VibeVoice-ASR, VibeVoice-TTS, and VibeVoice-Realtime, each designed to handle specific tasks such as long-form speech recognition, multi-speaker dialogue generation, and real-time streaming TTS.
For usage, users can explore the VibeVoice-ASR playground or run VibeVoice-Realtime on Colab.
From a technical standpoint, VibeVoice employs continuous speech tokenizers operating at an ultra-low frame rate of 7.5 Hz, significantly boosting computational efficiency.
The repository is geared towards researchers and developers interested in advancing voice AI capabilities, with detailed documentation and contribution guidelines available.
In summary: VibeVoice is a powerful, open-source toolkit for voice AI research and development, offering a range of innovative models and technologies to explore - and with great power comes great responsibility to use it ethically.

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🧠 Channel: https://t.iss.one/GithubRe

🚀 Meet different-ai/openwork: a gem from today's GitHub trending list.

🔗 https://github.com/different-ai/openwork
📝 The open-source alternative to Claude Cowork (powered by opencode)
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OpenWork is a free, open-source desktop app for sharing AI workflows, offering an alternative to Claude Cowork and Codex. It allows you to reuse skills, MCPs, and connected services across tools, teammates, and machines. With OpenWork, you can create something once and share it with coworkers or friends, or keep it for yourself. The app provides a dedicated workspace, but it's not required - you can use OpenWork from the agent you already have.

Key features include the ability to add one OpenWork MCP to compatible agents, such as Codex, Claude Code, or Cursor, and reuse skills and connections across your tools. The desktop app is available for macOS, Windows, and Linux, and an admin interface is available for larger organizations to publish capabilities, manage access, and configure shared or per-user connections.

To install OpenWork, simply copy and paste a prompt into your AI agent, and follow the steps to set up your first workspace. You can also use OpenWork from any agent, such as Codex, Claude Code, or OpenCode, by adding the OpenWork MCP.

From a technical standpoint, OpenWork uses a remote MCP server URL and provides tools like search_capabilities and execute_capability to find and run capabilities. The app also includes OpenWork Den, a control plane for managing OpenWork across a team or organization, which allows you to provision inference at scale, control access, and publish skills and plugins.

Get started with OpenWork today and discover a new way to share AI workflows - unlock the power of collaborative AI with OpenWork!

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🧠 Channel: https://t.iss.one/GithubRe

📌 Spotted on GitHub Trending: obra/superpowers — let's break it down.

🔗 https://github.com/obra/superpowers
📝 An agentic skills framework & software development methodology that works.
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Superpowers is a comprehensive software development methodology designed for coding agents, built on a set of composable skills and initial instructions. It enables agents to work autonomously, following a structured approach to development. The key features of Superpowers include brainstorming, test-driven-development, subagent-driven-development, and requesting-code-review, all of which are triggered automatically.

To use Superpowers, you can install it as a plugin in various coding agents like Claude Code, Antigravity, Codex App, and more. The installation process varies depending on the agent, but generally involves installing the plugin from the official marketplace or repository.

From a technical standpoint, Superpowers is built using a skills library that includes testing, debugging, collaboration, and meta skills. The skills are designed to be flexible and work across multiple coding agents. The writing-skills skill provides a guide for creating and testing new skills.

Superpowers is suitable for developers of all levels, from enthusiastic juniors to experienced professionals. The community-driven approach encourages collaboration, knowledge sharing, and feedback. The project is licensed under the MIT License and is maintained by Prime Radiant.

In a nutshell, Superpowers transforms your coding agent into a supercharged development partner, streamlining your workflow and boosting productivity - code smarter, not harder.

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🧠 Channel: https://t.iss.one/GithubRe

🎯 MoonshotAI/FlashKDA landed on trending. Worth a proper look.

🔗 https://github.com/MoonshotAI/FlashKDA
📝 FlashKDA: high-performance Kimi Delta Attention kernels
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Introducing FlashKDA, a high-performance library built on top of CUTLASS, providing optimized KDA kernels. Key features include support for SM90 and above, CUDA 12.9 and above, and PyTorch 2.4 and above. To use FlashKDA, simply install it using pip and import it in your Python script. You can then use it as a backend for flash-linear-attention by calling chunk_kda under torch.inference_mode().

Some technical highlights of FlashKDA include its ability to auto-dispatch from flash-linear-attention and its support for variable-length batching. The library also provides a flash_kda.fwd kernel API for custom usage.

Audience for FlashKDA includes developers and researchers working with PyTorch and CUDA, particularly those interested in optimizing their KDA kernels. With its high-performance capabilities and ease of use, FlashKDA is a great tool for anyone looking to accelerate their deep learning workloads.

In a nutshell, FlashKDA is a game-changer for KDA kernel optimization - try it out and experience the speed difference for yourself!

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🧠 Channel: https://t.iss.one/GithubRe

🔍 Deep-diving into NanmiCoder/MediaCrawler — fresh off the trending list.

🔗 https://github.com/NanmiCoder/MediaCrawler
📝 小红书笔记 | 评论爬虫、抖音视频 | 评论爬虫、快手视频 | 评论爬虫、B 站视频 ｜ 评论爬虫、微博帖子 ｜ 评论爬虫、百度贴吧帖子 ｜ 百度贴吧评论回复爬虫 | 知乎问答文章｜评论爬虫
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MediaCrawler is a powerful multi-platform media data collection tool that supports crawling public information from popular platforms like Xiaohongshu, Douyin, Kuaishou, Bilibili, Weibo, Tieba, and Zhihu.

The core technology is based on the Playwright browser automation framework, which allows for login state saving and JS expression signature parameter acquisition without requiring JS reverse engineering.

Key features include:
- Keyword search and specified post ID crawling
- Second-level comment support
- Specified creator homepage crawling
- Login state caching
- IP proxy pool support
- Comment word cloud generation

To get started, simply install the required dependencies using uv sync, configure your browser settings, and run the crawler program using uv run main.py.

The project also provides a WebUI for visualized operation and supports multiple data storage formats, including CSV, JSON, and SQLite.

Audience: This project is suitable for developers and researchers interested in web crawling and data collection.

Technical highlights include the use of Playwright for browser automation and the implementation of a self-media content decomposition agent.

One-liner takeaway: MediaCrawler is a versatile and efficient tool for collecting media data from various platforms, making it an excellent choice for those seeking to gather insights from online content.

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🧠 Channel: https://t.iss.one/GithubRe

🎯 alibaba/open-code-review landed on trending. Worth a proper look.

🔗 https://github.com/alibaba/open-code-review
📝 Open-source & free — Battle-tested at Alibaba's scale. Hybrid architecture code review tool: deterministic pipelines + LLM Agent, precise line-level comments, built-in fine-tuned ruleset (NPE, thread-safety, XSS, SQL injection), OpenAI & Anthropic compatible.
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The alibaba/open-code-review GitHub repository offers an AI-powered code review CLI tool, originating from Alibaba Group's internal AI code review assistant. It reads Git diffs, sends changed files to a configurable LLM via an agent, and generates structured review comments. The tool achieves higher precision and F1 scores compared to general-purpose agents, while consuming fewer tokens.

Key features include:

* ocr review for reviewing code changes
* ocr scan for reviewing entire files
* Support for multiple platforms, including Windows, macOS, and Linux
* Integration with various agents, such as Claude Code, Codex, and Cursor

To use Open Code Review, simply configure a model endpoint and install the CLI tool using npm install -g @alibaba-group/open-code-review. The tool is designed for developers, DevOps teams, and organizations looking to improve their code review process.

The core design combines deterministic engineering with an agent, ensuring precise file selection, smart file bundling, and fine-grained rule matching. The tool also provides a range of features, including review rules customization, configuration options, and integration with CI/CD pipelines.

In summary, Open Code Review is a powerful tool that helps teams improve their code quality and reduce errors. With its high precision and flexibility, it's an essential tool for any development team - automate your code reviews and take your development to the next level.

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🧠 Channel: https://t.iss.one/GithubRe

🚀 Meet paperswithbacktest/awesome-systematic-trading: a gem from today's GitHub trending list.

🔗 https://github.com/paperswithbacktest/awesome-systematic-trading
📝 A curated list of awesome libraries, packages, strategies, books, blogs, tutorials for systematic trading.
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The Awesome Systematic Trading GitHub repository is a comprehensive collection of resources for quantitative traders. It features 97 libraries and packages for research and live trading, 40+ strategies described by institutionals and academics, 55 books for beginners and professionals, 23 videos and interviews, as well as blogs and courses.

The repository is organized into categories such as backtesting and live trading, trading bots, analytics, and data sources, making it easy to find specific tools and information.

For example, the backtesting and live trading section includes popular libraries like vnpy, zipline, and backtrader, which are all built with Python.

The repository is a valuable resource for anyone interested in systematic trading, from beginners to professionals.

In short, Awesome Systematic Trading is a one-stop-shop for all your quantitative trading needs - explore it and take your trading to the next level!

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🧠 Channel: https://t.iss.one/GithubRe