Description

Users sometimes wonder what files or directories they find on their local PC are good for. If they can't determine from the filename or metadata, there should an easy way to quickly analyze the content and at least guess the meaning. An LLM could help with that, through the use of a filesystem MCP and to-text-converters for typical file types. Ideally this is integrated into the desktop environment but works as well from a console. All data is processed locally or "on premise", no artifacts remain or leave the system.

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Port release tool from Python to Go to learn language features and industry best practices. The project prioritizes proper Go architecture (concurrency, interfaces, error handling) over direct translation, concluding with a performance benchmark comparison between the two implementations.

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Introduction to AI Workflows

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MCP Server for Futmondo Fantasy Football in Elixir

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SUSE Observability pre-processes topology data during ingestion and stores it in its custom graph database.

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The core of the system is the Home Assistant Green Hub, which will serve as the dedicated, localized controller for all smart devices. The primary goal is to establish a secure, private, and highly customizable local home automation ecosystem, moving away from cloud-dependent solutions.

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I am trying to create a smart-lighting project with a Raspberry Pi Pico that reacts to a movie's visuals and audio that involves combining two distinct functions: