🌟 Robbyant/lingbot-map caught my eye on GitHub Trending today.

🔗 https://github.com/Robbyant/lingbot-map
📝 A feed-forward 3D foundation model for reconstructing scenes from streaming data
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LingBot-Map is a cutting-edge repo that introduces a geometric context transformer for streaming 3D reconstruction. This innovative approach unifies coordinate grounding, dense geometric cues, and long-range drift correction within a single streaming framework. The key features of LingBot-Map include its high-efficiency streaming inference, achieving stable performance at ~20 FPS on 518×378 resolution over long sequences exceeding 10,000 frames, and its state-of-the-art reconstruction capabilities, outperforming existing streaming and iterative optimization-based approaches.

To get started with LingBot-Map, users can follow the installation instructions, which involve creating a conda environment, installing PyTorch, and installing the lingbot-map package. The repo also provides a quick start guide, which enables users to run their first scene with a single command. Additionally, the interactive demo allows users to visualize 3D scenes interactively, while the offline rendering pipeline enables batch rendering for long sequences.

The target audience for LingBot-Map includes researchers and developers in the field of 3D reconstruction, as well as professionals working with 3D modeling and computer vision. The repo's technical highlights, such as its feed-forward architecture and paged KV cache attention, make it an attractive solution for applications requiring efficient and accurate 3D reconstruction.

In a nutshell, LingBot-Map is a game-changer for streaming 3D reconstruction, and its efficiency, accuracy, and ease of use make it a must-try for anyone working in the field: Reconstruct your world in 3D, effortlessly!

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🧠 Channel: https://t.iss.one/GithubRe

🔍 Deep-diving into every-app/open-seo — fresh off the trending list.

🔗 https://github.com/every-app/open-seo
📝 Open source alternative to Semrush and Ahrefs
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OpenSEO is an open-source alternative to expensive SEO tools like Semrush and Ahrefs. It's an all-in-one SEO tool that you can control, with a modern and simple UI focused on workflows like keyword research, rank tracking, and competitor insights.

You can use OpenSEO with any AI agent, and it even offers pre-built skills for popular agents like Claude Code, OpenClaw, or Hermes.

The tool is pay-as-you-go, so you only pay for what you use, and you can self-host it using Docker or Cloudflare.

OpenSEO is perfect for anyone looking for an affordable and customizable SEO solution.

Take control of your SEO with OpenSEO - it's the people's SEO tool.

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🧠 Channel: https://t.iss.one/GithubRe

🚀 Meet MoonshotAI/kimi-cli: a gem from today's GitHub trending list.

🔗 https://github.com/MoonshotAI/kimi-cli
📝 Kimi Code CLI is your next CLI agent.
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Kimi CLI is an AI agent that runs in your terminal, designed to assist with software development tasks and terminal operations. It can read and edit code, execute shell commands, search and fetch web pages, and even plan and adjust actions during execution.

The key features of Kimi CLI include a shell command mode that allows you to run shell commands without leaving the interface, a VS Code extension for integration with Visual Studio Code, and support for IDE integration via ACP (Agent Client Protocol) for compatibility with various editors and IDEs. Additionally, it offers Zsh integration and MCP support for Model Context Protocol tools.

To get started with Kimi CLI, you can refer to the Getting Started guide, which provides installation and usage instructions. For development, you can clone the repository, prepare the development environment, and use various make commands to run, format, and test the code.

The target audience for Kimi CLI appears to be developers who want to leverage AI capabilities to enhance their productivity and workflow. However, note that Kimi CLI is evolving into Kimi Code CLI, and this project will be gradually wound down.

One-liner takeaway: Kimi CLI is a terminal AI agent that's evolving into Kimi Code CLI, so try the next-generation terminal AI agent for a more enhanced experience!

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🧠 Channel: https://t.iss.one/GithubRe

🌟 AstrBotDevs/AstrBot caught my eye on GitHub Trending today.

🔗 https://github.com/AstrBotDevs/AstrBot
📝 AI Agent Assistant & development framework that integrates lots of IM platforms, LLMs, plugins and AI feature, and can be your openclaw alternative. ✨
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AstrBot is an open-source, all-in-one chatbot platform that integrates with mainstream instant messaging apps, providing reliable and scalable conversational AI infrastructure. Its key features include AI LLM conversations, multimodal support, and plugin extensions with over 1000 plugins available. astrbot supports multiple platforms, including QQ, WeChat, Telegram, and Slack, and offers various deployment methods, such as one-click deployment, Docker, and desktop application deployment. The platform is suitable for individuals, developers, and teams looking to build AI applications within their workflows. With its internationalization support and web chat UI, AstrBot enables users to create production-ready AI applications quickly. Whether you're building a personal AI companion or an enterprise knowledge base, AstrBot has got you covered - revolutionize your conversations with AstrBot today!

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🧠 Channel: https://t.iss.one/GithubRe

💡 PrefectHQ/fastmcp just hit the trending charts — here's why it matters.

🔗 https://github.com/PrefectHQ/fastmcp
📝 🚀 The fast, Pythonic way to build MCP servers and clients.
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The FastMCP framework simplifies building applications with the Model Context Protocol (MCP), connecting large language models to tools and data. Key features include automatic schema generation, validation, and documentation for declared tools, as well as transport negotiation, authentication, and protocol lifecycle management for servers.

To use FastMCP, you can declare a tool with a Python function, such as:

from fastmcp import FastMCP

mcp = FastMCP("Demo ")
 
@mcp.tool
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b

if __name__ == "__main__":
    mcp.run()

Technical highlights of FastMCP include its three pillars: Servers, which expose tools and resources to LLMs, Apps, which provide interactive UIs, and Clients, which connect to any MCP server.

FastMCP is designed for developers and enterprises looking to build and deploy MCP applications efficiently. The framework is the standard for working with MCP and powers 70% of MCP servers.

Get started with FastMCP and scale your applications with Prefect Horizon. With FastMCP, best practices are built in, so you can focus on your logic and let the framework handle the rest. FastMCP: build fast, scale faster.

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🧠 Channel: https://t.iss.one/GithubRe