2 projects for "umbrella-cli" with 2 filters applied:

  • Ship AI Apps Faster with Vertex AI Icon
    Ship AI Apps Faster with Vertex AI

    Go from idea to deployed AI app without managing infrastructure. Vertex AI offers one platform for the entire AI development lifecycle.

    Ship AI apps and features faster with Vertex AI—your end-to-end AI platform. Access Gemini 3 and 200+ foundation models, fine-tune for your needs, and deploy with enterprise-grade MLOps. Build chatbots, agents, or custom models. New customers get $300 in free credit.
    Try Vertex AI Free
  • Cut Data Warehouse Costs up to 54% with BigQuery Icon
    Cut Data Warehouse Costs up to 54% with BigQuery

    Migrate from Snowflake, Databricks, or Redshift with free migration tools. Exabyte scale without the Exabyte price.

    BigQuery delivers up to 54% lower TCO than cloud alternatives. Migrate from legacy or competing warehouses using free BigQuery Migration Service with automated SQL translation. Get serverless scale with no infrastructure to manage, compressed storage, and flexible pricing—pay per query or commit for deeper discounts. New customers get $300 in free credit.
    Try BigQuery Free
  • 1
    Architecture as a code

    Architecture as a code

    Visualize, collaborate, and evolve the software architecture

    Architecture as a code is an open-source modeling language and toolkit that enables software teams to describe, visualize, collaborate on, and maintain software architecture as code. Inspired by the C4 Model and other architectural DSLs, LikeC4 lets you define your system’s structure in a textual DSL and then automatically generate consistent diagrams that reflect that design, ensuring that architecture documentation stays in sync with source code changes. The project includes command-line...
    Downloads: 3 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: 3 This Week
    Last Update:
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