Feishin it is a robust, specialized client-side interface designed to exercise digital sovereignty over your personal media library. This open-source desktop application acts as a modern, functional frontend for established, self-hosted media servers (like Jellyfin or Navidrome). By decoupling the playback client from a centralized, proprietary cloud API (e.g., Spotify), Feishin allows users to maintain complete control over metadata, access patterns, and the underlying data.
In the rapidly evolving landscape of locally hosted Large Language Models (LLMs), performance often remains the primary bottleneck. While quantization and efficient kernel implementations have made running powerful models on consumer hardware a reality, a significant efficiency gap remains. This gap is bridged by speculative decoding, an advanced inference technique that allows local LLMs to operate with significantly greater throughput.
This post provides a technical summary of how speculative decoding works, the core mechanism, best practices for choosing a draft model, and the setup required for various tooling environments.
I could ask my local LLM to write a script, troubleshoot a Docker container, or explain how to organize a folder full of files, and it would usually give me a sensible answer. That answer was also where its job ended. My local LLM could not handle an entire task on its own.
Actually - I did not even like them as a child; prefered to create some of my first programs on my C16. But at the moment they are a big source of frustration. I was used to use AI in Visual Studio Code - and it was a well integrated environment/experience. It is possible to use it with BYOK to connect it to your local AI ...
openHAB has rolled out version 5.2, and it's a massive testament to the power of its global community! This release focuses heavily on extending user-facing features while maintaining stability for smooth upgrades from previous major versions.
