AI

You can automate your Gmail inbox on your own machine. The Gmail API feeds messages into a private Python script. A local LLM then handles summaries, sorting, and draft replies. You get the smart inbox features that tools like Google’s Gemini sidebar or Microsoft Copilot for Outlook offer. None of your email content ever leaves your computer.

This guide walks through the full build. You’ll set up the Gmail API with minimal OAuth scopes. You’ll fetch and parse raw email data, then mask any PII with Microsoft Presidio before the model sees it. You’ll build a daily summarizer that ranks mail by urgency. You’ll also build a smart draft writer that learns from your sent mail, and you’ll wire the whole pipeline up with cron. By the end, you’ll have a working local email agent that runs on any mid-range Linux or macOS box with Ollama installed.

Software teams keep adding AI coding agents to their workflow. One popular trend: drop a repo-level context file, often named AGENTS.md or CLAUDE.md, to guide the agent. The idea sounds clean. Give the AI a map of the codebase and a few rules, and it should solve tasks faster.

But does it work? A new paper, “Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?” , says no. The results push back hard on the default advice.

You can run FLUX 2 locally on a single consumer GPU in 2026. The open-weight FLUX 2 dev is a 32B model from Black Forest Labs that fits a 24GB card when quantized, while the smaller Klein builds run on 8GB. This guide picks the right variant for your card, installs it in ComfyUI, and covers what it costs to run.

Small Language Models, sub-4B parameter models built to run on local hardware, now handle most of the edge AI work that used to need the cloud. Phi-4 , Gemma 3 , and Llama 3.2-1B run offline on Raspberry Pi boards, phones, and industrial PLCs. The economics, latency, and privacy story all point the same way: edge first.

What Counts as a Small Language Model

In 2023, “small” meant under 13B parameters. Today, three tiers matter for edge work.

The best way to fine-tune Stable Diffusion XL 2.0 is with Low-Rank Adaptation (LoRA) : a small adapter that injects your style or subject without touching the base weights. Instead of retraining the full model, LoRA trains a tiny side network next to the frozen base. The result is a 50 to 300 MB file you can load, swap, and stack at inference, trained on a 12 GB GPU in an afternoon.

To build a private Retrieval-Augmented Generation (RAG) system, pair a local vector database like Qdrant with an embedding model like BGE-M3 . Add a local LLM through Ollama , and you can index hundreds of documents and ask questions about them. Your data stays on your machine.

Why RAG? The Problem With Pure LLM Memory

Large language models sound smart, but they are poor knowledge stores. They learn from old training data and know nothing about files you created later or keep private. Ask about your own data, and the model will often guess. Even strong open weight models like Llama 4.0 can invent plausible but wrong answers about content they never saw. For a deeper breakdown of why LLM hallucinations happen and how to measure them, the issue goes beyond missing context.