Over the past few days, I’ve been using reranker models through cloud APIs as part of my RAG experiments. After successfully running several LLMs locally with llama.cpp, I started wondering: Can I also run a reranker model locally? The answer is yes. It turns out that llama-server can host embedding models, reranker models, and chat models behind the same OpenAI-compatible API. This makes it easy to build a completely local RAG pipeline without relying on external services.

Automatic Speech Recognition (ASR) has become an essential capability for many AI-powered applications. Whether it is transcribing meetings, generating subtitles, or enabling voice-based interactions, ASR is increasingly becoming a core feature of modern AI systems. As part of my ongoing learning journey with AI technologies, I decided to start exploring the ASR capabilities available today. My first stop is Qwen3-ASR, one of the latest speech recognition models from the Qwen family.

Serving LLMs with Dev Containers: A New Rabbit Hole One of the interesting things about working with AI is that you often discover new ideas when you’re exploring something completely different. I never imagined I would write about serving Large Language Models (LLMs) using Dev Containers. Although I’ve been using the vLLM container for quite some time, I always thought of it simply as another containerized application running in Kubernetes or Docker.