AI insights, industry analysis, and technical guides
Learn how to wire together open-source classifiers and LLMs into a production-ready content moderation pipeline that catches harmful text, images, and edge cases. This hands-on guide walks you through every step, from model selection to deployment considerations.
Learn how to build a fully local document Q&A system using open-weights embedding models, a vector store, and a retrieval-augmented generation pattern. This hands-on tutorial takes you from raw PDFs to accurate, cited answers in under an hour.
Model quantisation can slash your inference costs by up to 4x while preserving most of your model's accuracy. This hands-on tutorial walks you through INT8 and INT4 quantisation using Hugging Face and bitsandbytes, covering real pitfalls and how to sidestep them.
Learn how to compile llama.cpp, load a quantized model, and expose it through a local REST API endpoint — all without a GPU. A practical walkthrough for developers who need cost-effective, self-hosted language model inference.
Learn how to build a fully functional semantic search engine using free, open-source embedding models and a lightweight vector store — no expensive APIs required. This hands-on tutorial walks you through every step, from encoding documents to querying results in milliseconds.
Learn how to compile llama.cpp, download a quantized model, and expose it through a local REST API — all without a GPU. This tutorial walks you through every step so you can run production-grade language model inference on any Linux or macOS machine.
Learn how to build a fully functional Retrieval-Augmented Generation pipeline using open-source models that run entirely on CPU. This step-by-step guide covers everything from document ingestion to query serving without a single GPU in sight.
Learn how to build a production-ready AI content moderation pipeline using open-source models like Llama Guard and Detoxify. This step-by-step guide walks developers through setup, inference, and deployment considerations.
Learn how to build a fully local, CPU-based LLM inference server using llama.cpp and a lightweight REST API wrapper. This tutorial walks you through every step, from model download to serving real HTTP requests.