<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ai on AiSlackin</title><link>https://aislackin.com/tags/ai/</link><description>Recent content in Ai on AiSlackin</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 06 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://aislackin.com/tags/ai/index.xml" rel="self" type="application/rss+xml"/><item><title>slug-tts-stt: giving a mini-PC ears and a mouth, on two different chips</title><link>https://aislackin.com/posts/2026-08-06-slug-tts-stt-voice-service/</link><pubDate>Thu, 06 Aug 2026 00:00:00 +0000</pubDate><guid>https://aislackin.com/posts/2026-08-06-slug-tts-stt-voice-service/</guid><description>&lt;p&gt;A voice assistant needs two things from its hardware: it must hear you and it must answer you. On a single-box setup those workloads fight for the same chip — unless you deliberately split them. That&amp;rsquo;s the whole idea here: STT runs on the &lt;strong&gt;XDNA2 NPU&lt;/strong&gt; (Whisper large-v3-turbo via FastFlowLM), TTS runs on the &lt;strong&gt;Radeon 840M iGPU&lt;/strong&gt; (Qwen3-TTS 1.7B via qwentts.cpp/Vulkan). Both exposed as OpenAI-compatible HTTP endpoints on your LAN. The reasoning brain lives elsewhere; this box is only ever the audio edge.&lt;/p&gt;</description></item><item><title>quill-of-hermes: LAN OCR that eats PDFs and multi-page TIFFs</title><link>https://aislackin.com/posts/2026-07-30-quill-of-hermes-lan-ocr/</link><pubDate>Thu, 30 Jul 2026 00:00:00 +0000</pubDate><guid>https://aislackin.com/posts/2026-07-30-quill-of-hermes-lan-ocr/</guid><description>&lt;p&gt;OCR as a service usually means cloud APIs, per-page billing, and your documents on someone else&amp;rsquo;s disk. This is the local counterargument: a FastAPI wrapper around &lt;a href="https://github.com/01-ai"&gt;OvisOCR2&lt;/a&gt; running GGUF via llama.cpp&amp;rsquo;s &lt;code&gt;llama-mtmd-cli&lt;/code&gt;, serving your LAN. POST an image, a PDF, or a multi-page TIFF; get back clean Markdown.&lt;/p&gt;&#10;&lt;p&gt;The design decisions worth noting: each page is sent to the model independently and results are joined with a horizontal rule, so a 50-page PDF degrades gracefully instead of failing whole. Jobs are serialized to keep the GPU sane, there&amp;rsquo;s a five-minute timeout per page so one bad scan can&amp;rsquo;t wedge the queue, and temp files are cleaned unconditionally. The service deliberately stays separate from llama-swap because multimodal &lt;code&gt;llama-server&lt;/code&gt; support for this model wasn&amp;rsquo;t proven — &lt;code&gt;llama-mtmd-cli&lt;/code&gt; was.&lt;/p&gt;</description></item></channel></rss>