⚡ microsoft/generative-ai-for-beginners is making waves. Here's the full picture.

🔗 https://github.com/microsoft/generative-ai-for-beginners
📝 21 Lessons, Get Started Building with Generative AI
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The Microsoft Generative AI for Beginners GitHub repository offers a comprehensive 21-lesson course on building Generative AI applications. The course covers the fundamentals of Generative AI, including Large Language Models (LLMs), Prompt Engineering, and responsible AI development.

Key features of the course include:
- Python and TypeScript code samples
- Support for Azure OpenAI Service, Microsoft Foundry Models, OpenAI API, and Foundry Local
- A Course Setup lesson to help with development environment setup

To get started, you'll need basic knowledge of Python or TypeScript and a GitHub account. The course is suitable for beginners and intermediate learners.

Takeaway: With this course, you can learn the fundamentals of Generative AI and start building your own applications in no time.

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🧠 Channel: https://t.iss.one/GithubRe

🌟 Panniantong/Agent-Reach caught my eye on GitHub Trending today.

🔗 https://github.com/Panniantong/Agent-Reach
📝 Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
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Agent Reach 是一个帮助 AI Agent 获取互联网能力的工具。它让 Agent 能够读取网页、搜索推特、观看 YouTube 视频、刷小红书等，不需要任何配置。只需复制一句话给你的 AI Agent，就能安装 Agent Reach 并解锁这些功能。

主要特点：
- 完全免费，所有工具开源，所有 API 免费
- 隐私安全，Cookie 只存在本地，不上传不外传
- 持续换代，每个平台都是「首选 + 备选」多后端路由
- 兼容所有 Agent，任何能跑命令行的 Agent 都能用

技术亮点：
- 依赖 pip install 装好 agent-reach 命令行
- 自动配置搜索引擎，通过 MCP 接入 Exa（免费，无需 API Key）
- 支持多种平台，包括网页、YouTube、Twitter、Reddit、Facebook、Instagram 等

使用方法：
- 复制这句话给你的 AI Agent：帮我安装 Agent Reach：https://raw.githubusercontent.com/Panniantong/agent-reach/main/docs/install.md
- 更新也是一句话：帮我更新 Agent Reach：https://raw.githubusercontent.com/Panniantong/agent-reach/main/docs/update.md

目标受众：
- AI 爱好者
- 开发者
- 企业用户

总结：Agent Reach 是一个让 AI Agent 能够获取互联网能力的工具，简单、免费、安全、兼容所有 Agent。安装 Agent Reach，一键解锁互联网能力。

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🧠 Channel: https://t.iss.one/GithubRe

🔍 Deep-diving into TencentCloud/TencentDB-Agent-Memory — fresh off the trending list.

🔗 https://github.com/TencentCloud/TencentDB-Agent-Memory
📝 TencentDB Agent Memory is a team-level memory hub for AI Agents — turning conversations, docs, and code into four reusable memory assets (Chat Memory, Skill, LLM-Wiki, Code-Graph) that are governed, shared, and equipped across agents and frameworks.
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The TencentDB Agent Memory is a powerful tool designed to enhance the memory capabilities of AI agents, allowing them to learn from workflows, retain task context, and reuse past experiences. At its core, it features symbolic short-term memory and layered long-term memory, which work together to ensure that agents not only remember more but also reason better.

The symbolic short-term memory offloads heavy tool logs into compact Mermaid symbols, reducing token usage and improving task success. Meanwhile, the layered long-term memory distills fragmented conversations into structured personas and scenes, instead of relying on flat vector piles.

When integrated with OpenClaw, it can cut token usage by up to 61.38% and improve the pass rate by 51.52%. The system is designed with heterogeneous storage and progressive disclosure in mind, allowing for a dual-layer storage strategy that balances robust full-text retrieval with high information density and white-box inspection.

To get started, users can follow the Quick Start guide, which provides steps for installing the plugin, enabling it, and configuring it for use with OpenClaw or Hermes. The plugin is suitable for developers and researchers looking to enhance the memory capabilities of their AI agents.

In a nutshell, TencentDB Agent Memory is all about empowering agents to remember what should be remembered, so people can focus on judgment, creation, and work that truly matters - let the machines handle the memorization, and humans handle the innovation.

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🧠 Channel: https://t.iss.one/GithubRe

🌟 mvanhorn/last30days-skill caught my eye on GitHub Trending today.

🔗 https://github.com/mvanhorn/last30days-skill
📝 AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary
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Introduction to last30days-skill: This GitHub repository is home to a game-changing AI agent-led search engine that scores results based on upvotes, likes, and real money, not editors.

The key features of this tool include its ability to search multiple platforms like Reddit, X, YouTube, TikTok, and GitHub in parallel, and provide a synthesized brief of the most relevant information. It's zero-config, meaning you can start using it right away, and it works immediately with Reddit, HN, Polymarket, and GitHub.

To use this tool, you can install it using Claude Code, Codex, Cursor, Copilot, Gemini CLI, or any other Agent Skills host. There are also more install options available, including a manual setup.

From a technical perspective, this tool is built to bridge the gap between multiple disconnected platforms, using an AI agent to search and score information from various sources. It's a constantly evolving project, with new features and updates being added regularly, such as the recent integration with OpenAI Codex, arXiv, Techmeme, and Digg.

This tool is perfect for anyone looking to stay up-to-date with the latest information on a particular topic or person. Whether you're a researcher, a journalist, or simply someone who wants to stay informed, last30days-skill is an invaluable resource.

In short, last30days-skill is a powerful search engine that uses AI to provide you with the most relevant and up-to-date information on any topic, and its best feature is that it synthesizes information from multiple sources into one brief, making it easy to stay informed in a fast-changing world.
So, try it out and experience the power of AI-led search for yourself: last30days-skill is the ultimate tool for anyone who wants to stay ahead of the curve!

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🧠 Channel: https://t.iss.one/GithubRe

⚡ NomaDamas/k-skill is making waves. Here's the full picture.

🔗 https://github.com/NomaDamas/k-skill
📝 한국인을 위한 스킬 모음집 - 에이전트를 한국인으로
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k-skill is a collection of AI agent skills that can automate various tasks for Koreans, from booking train tickets to searching for real estate information. Key features include support for multiple coding agents like Claude Code and Codex, and the ability to download and use the skills without needing an additional client API layer. Node.js 18+ and npx are required for installation.

To use, you can install all skills with npx --yes skills add NomaDamas/k-skill --all -g or install specific skills like npx --yes skills add NomaDamas/k-skill --skill srt-booking -g. The skills cater to a wide range of tasks, including transportation, real estate, education, finance, and more.

The audience for this repository includes anyone looking to automate tasks in Korea, from individuals to businesses. With k-skill, you can simplify your life by letting AI handle the mundane tasks. Here's a sneak peek at what you can do: query Korean stock information, search for real estate listings, or even find the cheapest gas station near you. This toolkit is perfect for those looking to streamline their daily routines.

In a nutshell, k-skill is your one-stop-shop for automating tasks in Korea - it's like having your own personal AI assistant!

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🧠 Channel: https://t.iss.one/GithubRe

🌟 HarbourMasters/Lighthouse caught my eye on GitHub Trending today.

🔗 https://github.com/HarbourMasters/Lighthouse
📝 No description.
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The HarbourMasters/Lighthouse GitHub repository is a project focused on decompiling and recompiling the classic game Banjo-Kazooie. The purpose of this project is to provide a comprehensive understanding of the game's inner workings and to allow for modifications and improvements.

Key features of the repository include the ability to build the game using different baserom versions, such as us.v10, us.v11, jp, and pal, as well as the option to use Docker for building on various platforms, including Linux and macOS.

Usage involves installing dependencies, adding the baserom file, and running the build command using make. The repository also provides instructions for building on cloud platforms using GitLab CI.

From a technical standpoint, the project uses a combination of bash scripts, python, and Rust to manage the building process. The repository is well-organized, with clear instructions and a detailed README file.

The target audience for this repository appears to be developers and gamers interested in game development, reverse engineering, and modding.

In summary, HarbourMasters/Lighthouse is a valuable resource for anyone looking to dive into the world of game decompilation and modification - and with great power comes great banjos.

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🧠 Channel: https://t.iss.one/GithubRe

💡 antirez/ds4 just hit the trending charts — here's why it matters.

🔗 https://github.com/antirez/ds4
📝 DeepSeek 4 Flash and PRO local inference engine for Metal, CUDA and ROCm
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DwarfStar is a native inference engine optimized for DeepSeek V4 Flash and other select models, offering high performance on various hardware backends, including Metal, NVIDIA CUDA, and ROCm. Key features include SSD streaming for smaller machines, multi-GPU support, and experimental speculative decoding. The project is self-contained and focused on a narrow set of models, allowing for efficient and specialized implementation.

You can use ds4 to run capable models on consumer hardware, create a multi-user LLM server with older CUDA cards, or leverage pipeline parallelism to combine systems and run larger models. The project is designed for high-end personal machines, leveraging compressed KV caches and fast local SSDs to make long contexts practical.

Technical highlights include the use of asymmetrical quantization, where only routed MoE experts are quantized, and the implementation of DSpark speculative decoding, which can accelerate generation speed. The engine is optimized for DeepSeek V4 Flash and GLM 5.2 models, with support for various quantization formats and tensor layouts.

Audience: The project is geared towards developers and users who want to run high-performance models on their local machines or servers, particularly those interested in DeepSeek V4 Flash and GLM 5.2.

One-liner takeaway: With DwarfStar, you can unlock the full potential of your hardware and run high-performance models like DeepSeek V4 Flash at incredible speeds, making it an exciting project for anyone interested in AI and machine learning.

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🧠 Channel: https://t.iss.one/GithubRe