<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LLM Systems on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/llm-systems/</link><description>Recent content in LLM Systems on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Wed, 06 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/llm-systems/index.xml" rel="self" type="application/rss+xml"/><item><title>Implementing On-Device Speech-to-Text with Whisper.cpp</title><link>https://ai-blog.noorshomelab.dev/on-device-ai-agents-tiny-llms-guide-2026/on-device-stt-whisper-cpp/</link><pubDate>Wed, 06 May 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/on-device-ai-agents-tiny-llms-guide-2026/on-device-stt-whisper-cpp/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;Building truly intelligent on-device AI agents starts with their ability to perceive and understand the world around them. For human interaction, this often means processing spoken language directly on the device. In this chapter, we&amp;rsquo;ll lay the groundwork for our edge AI system by implementing robust, low-latency Speech-to-Text (STT) capabilities.&lt;/p&gt;
&lt;p&gt;We will leverage &lt;code&gt;whisper.cpp&lt;/code&gt;, a high-performance C++ port of OpenAI&amp;rsquo;s Whisper model, to perform transcription entirely on the device. This choice is critical for privacy, reducing reliance on cloud services, and achieving minimal latency—all hallmarks of a production-ready edge AI system. By the end of this chapter, you will have a standalone command-line application that can transcribe audio files with impressive accuracy, forming a core component for any voice-enabled agent.&lt;/p&gt;</description></item></channel></rss>