Best Mini PCs and Single-Board Computers for AI Projects

Complete 2026 guide to mini PCs and single-board computers for AI — Raspberry Pi, Jetson, Beelink, GEEKOM compared with real performance numbers for local LLMs and edge AI.

On-Device Speech Recognition with Whisper.cpp

Complete guide to whisper.cpp — build, run, and optimize OpenAI's Whisper model for offline speech recognition on Raspberry Pi, Apple Silicon, and desktop hardware.

Federated Learning Explained: Train Models Without Sharing Data

Complete guide to federated learning — how decentralized training works, privacy techniques like differential privacy and secure aggregation, and real production deployment patterns.

Building a Local RAG Chatbot with Ollama and LangChain

Build a fully local RAG chatbot with Ollama and LangChain — no API keys, no cloud dependency. Complete pipeline with document loading, embeddings, retrieval, and generation.

Jetson Nano Tutorial: Deep Learning on NVIDIA's Edge Device

Complete Jetson Nano and Orin Nano tutorial — JetPack setup, TensorRT optimization, YOLO object detection, and running generative AI locally on NVIDIA edge hardware.

Knowledge Distillation: Train Small Models from Large Ones

Complete guide to knowledge distillation — teacher-student training, soft labels, dark knowledge, and practical pipelines for compressing large models into deployable ones.

Edge Object Detection with YOLO on Raspberry Pi

Complete guide to running YOLO26 real-time object detection on Raspberry Pi — NCNN export optimization, camera inference, and practical edge deployment tips.

Running Stable Diffusion Locally: Complete Setup Guide

Complete guide to running Stable Diffusion locally in 2026 — ComfyUI vs Automatic1111, SDXL and Flux setup, VRAM optimization, ControlNet, and LoRA workflows.

GGUF Format Explained: Understanding Quantized LLM Files

Complete guide to GGUF format — understand quantization tags like Q4_K_M, internal file structure, and how local LLM tools use GGUF for quantized model deployment.

Pruning Neural Networks: Reduce Model Size for Edge Deployment

Learn how pruning removes unnecessary weights from neural networks for edge deployment. Covers structured vs unstructured pruning, selection criteria, and practical PyTorch workflows.