Python

Automating Gmail with Local AI Agents and Python

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.

Reverse Engineer USB Devices with Wireshark and Python

Reverse engineering an unknown USB device means working out the protocol it uses to talk: the byte sequence that makes it do things. The good news is that most USB devices don’t encrypt their traffic. Everything they send and get back travels in plain sight on the USB bus, and Linux gives you the tools to watch it. Once you know the protocol, a Python script using pyusb can drive the device directly and skip the vendor software.

Building Multi-Step AI Agents with LangGraph

AI agents built on LangGraph run as stateful graphs, not linear prompts. The graph can loop, branch on tool output, retry after a failure, and save its progress. That structure is what lets one agent handle long, multi-step tasks reliably.

Key Takeaways

LangGraph models an agent as a stateful graph, so it can loop, retry, and recover.
The state schema you design up front decides how stable the agent turns out.
Built-in checkpointing lets an agent crash, pause for approval, and resume without lost work.
Conditional edges turn failures into retries instead of dead ends.
One agent task can fire dozens of LLM calls, so plan for cost before you deploy.

Prerequisites

You should know Python 3.11+ and the LangChain basics: LLMs, tools, prompts. The code below uses these versions: