AI

You can clone your own voice with Coqui TTS using just 5 minutes of recorded audio, all on your own hardware. The steps are simple. Record clean audio. Turn it into a training set. Fine-tune an XTTS v2 or VITS model. Export the result for real-time use. On a modern GPU like the RTX 5070 with 12 GB of VRAM, fine-tuning takes 2 to 4 hours. The output sounds natural and keeps the target voice’s timbre, pacing, and accent.

The Model Context Protocol gives LLMs a standard way to call external tools, read files, and query databases. You skip the rewrite each time you switch models. You can build a working MCP server in Python with the official mcp SDK in under 100 lines. It runs with Claude Desktop or Claude Code in minutes. This guide walks the full path, from a tiny first server to production.

What MCP Is and Why It Changes Tool Use

MCP is a JSON-RPC 2.0 protocol. It lets an LLM client (like Claude Desktop , Claude Code, or Cursor) find and call tools exposed by a server process. The big shift from older function-calling is the discovery step. Instead of hard-coding tool defs into every prompt, the client sends a tools/list request when it connects. It gets back the full schema for everything the server exposes. Add a new tool, restart the server, and any client sees it on the next connect.

Five open source repositories dropped in March 2026 that expand what Claude Code can do. Karpathy’s AutoResearch runs overnight ML experiments without you. OpenSpace makes agent skills fix and improve themselves. CLI-Anything turns GUI software into agent-ready command-line tools. Claude Peers MCP lets many Claude Code sessions coordinate on one machine. And Google Workspace CLI opens Gmail, Drive, Calendar, and Sheets to agents. All five are free, open source, and plug right into Claude Code.

ControlNet lets you steer Stable Diffusion with spatial inputs: hand-drawn sketches, Canny edge maps, depth images, or OpenPose skeletons. The output then follows your layout, not your prompt alone. You feed a control image next to your text prompt. The model builds artwork that matches the structure of your input. It then fills in texture, lighting, and detail from the prompt. You get pixel-level control that no prompt tweak can match.

Fixing LLM hallucinations in production needs a layered defense. Use Chain-of-Verification at inference time. Ground the model in trusted data. Build eval suites that give you a hallucination rate you can track and gate in CI . No single trick fixes this. But pair prompt rules with retrieval-augmented grounding , self-checking, and validation layers, and you turn it into a problem you can measure and ship against.

What Is Hallucination? A Taxonomy for Developers

“Hallucination” has become an umbrella label for almost any unexpected LLM output. That fuzziness is dangerous in production. Each failure mode has a distinct cause and a distinct fix. Lump them together and you’ll apply the wrong remedy to the wrong problem. You’ll spend cycles on prompt tuning when the real issue is retrieval quality, or add RAG when the failure is instruction-following. Before you can fix hallucinations, you need a precise vocabulary for what you’re seeing.

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.