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2 projects for "obsidian" with 2 filters applied:

  • Gen AI apps are built with MongoDB Atlas Icon
    Gen AI apps are built with MongoDB Atlas

    The database for AI-powered applications.

    MongoDB Atlas is the developer-friendly database used to build, scale, and run gen AI and LLM-powered apps—without needing a separate vector database. Atlas offers built-in vector search, global availability across 115+ regions, and flexible document modeling. Start building AI apps faster, all in one place.
    Start Free
  • Transforming NetOps Through No-Code Network Automation - NetBrain Icon
    Transforming NetOps Through No-Code Network Automation - NetBrain

    For anyone searching for a complete no-code automation platform for hybrid network observability and AIOps

    NetBrain, founded in 2004, provides a powerful no-code automation platform for hybrid network observability, allowing organizations to enhance their operational efficiency through automated workflows. The platform applies automation across three key workflows: troubleshooting, change management, and assessment.
    Learn More
  • 1
    Obsidian Skills

    Obsidian Skills

    Agent skills for Obsidian

    Obsidian-Skills is a repository of agent skills tailored for use with Obsidian and any Claude-compatible agent that follows the standard Agent Skills specification, enabling AI assistants to better understand and interact with Obsidian content. These skills are markdown-driven specifications that teach Claude Code (or similar agents) how to perform context-aware tasks within Obsidian’s unique environment, such as interpreting different file types and workflows, automating workflows tied to notes, or enhancing agent responses with structured knowledge. ...
    Downloads: 4 This Week
    Last Update:
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  • 2
    Obsidian Visual Skills Pack

    Obsidian Visual Skills Pack

    Generate Canvas, Excalidraw, and Mermaid diagrams from text

    LLM-TLDR is a Python-based tool designed to dramatically reduce the amount of code a large language model needs to read by extracting the essential structure and context from a codebase and presenting only the most relevant parts to the model. Traditional approaches often dump entire files into a model’s context, which quickly exceeds token limits; LLM-TLDR instead indexes project structure, traces dependencies, and summarizes code in a way that preserves semantic relevance while shrinking...
    Downloads: 2 This Week
    Last Update:
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