Showing 4 open source projects for "config editor"

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    Thinc

    Thinc

    A refreshing functional take on deep learning

    Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models. Previous versions of Thinc have been running quietly in production in thousands of companies, via both spaCy and Prodigy. We wrote the new version to let users compose,...
    Downloads: 211 This Week
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  • 2
    MCP Language Server

    MCP Language Server

    mcp-language-server gives MCP enabled clients access semantic tools

    mcp-language-server gives MCP-enabled clients semantic code-navigation powers—go-to-definition, find references, rename, and diagnostics—by brokering requests to language servers. It is not “a language server for MCP,” but an MCP server that exposes language-server–style capabilities to agents and chat IDEs through typed tools. The README demonstrates a streamlined setup: install the Go server, plug in one or more language servers per language, and the MCP client gains editor-grade...
    Downloads: 0 This Week
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  • 3
    Laravel Boost

    Laravel Boost

    Laravel-focused MCP server for augmenting AI powered local development

    ...It’s designed to fit naturally into existing projects, supporting current Laravel releases and modern PHP runtimes with minimal setup. Rather than trying to replace your editor or framework, Boost acts like an intelligent layer that understands Laravel’s conventions and reduces the “explain my app to the AI” friction.
    Downloads: 0 This Week
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  • 4
    AIConfig

    AIConfig

    AIConfig is a config-based framework to build generative AI apps

    AIConfig is an open-source framework designed to simplify the development and management of generative AI applications by separating AI logic from application code. The framework allows prompts, model configurations, and parameters to be stored as structured configuration files that can be version controlled and managed independently from the rest of the software system. This approach improves collaboration between developers, prompt engineers, and machine learning practitioners by turning...
    Downloads: 0 This Week
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