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.

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.