How-Tos & Practical Guides

Getting OpenClaw running locally has always required a non-trivial amount of setup — installing dependencies, configuring models, managing environment variables, and keeping the stack running reliably. With the launch of FlashClaw today, there’s now a one-click cloud path that skips all of that. This guide walks you through getting your first OpenClaw agent running in the cloud using FlashClaw, from account creation to your first autonomous workflow. What You’ll Need A FlashClaw account (sign up at flashclaw.dev) An API key for your preferred AI model (OpenAI, Anthropic, xAI Grok, or others supported by OpenClaw) A workflow idea — even something simple like “monitor a URL and summarize changes daily” works perfectly for a first test Time required: 10–15 minutes for your first deployment. ...

DryRun Security’s 2026 Agentic Coding Security Report found that Claude, when operating as an autonomous coding agent, produces more unresolved high-severity security flaws than Codex or Gemini. But here’s the thing: all AI coding agents produce security vulnerabilities. The model matters less than your review process. This guide walks you through a practical security audit workflow for AI-generated code, applicable regardless of which model or agent you’re using. Before You Start: Understand the Risk Profile AI-generated code has specific vulnerability patterns that differ from human-written code. Knowing what to look for saves time. ...

DryRun Security’s 2026 Agentic Coding Security Report landed a finding that should make every engineering team pause: 87% of pull requests written by AI coding agents (Claude, Codex, Gemini) introduced at least one security vulnerability. Not occasionally — consistently, across all three leading models, in real application development scenarios. This isn’t a reason to stop using AI coding agents. The productivity gains are real. But it is a strong signal that AI-generated code needs a security review process as rigorous as — or more rigorous than — what you’d apply to human-written code. ...

JetBrains Air entered public preview today — an agentic IDE built on the Fleet codebase that supports running multiple AI coding agents in parallel. If you’ve been following the agentic IDE space and want to get hands-on, this guide walks through installation, initial configuration, and your first parallel agent workflow. Prerequisites Before you start, confirm you have: A JetBrains account — Air requires a JetBrains account, even in public preview. Free accounts work during the preview period. JetBrains AI subscription (optional for basic use, required for premium models) — if you want to use Claude or GPT-5 as your underlying model, you’ll need a JetBrains AI subscription. The default model (JetBrains’ own hosted model) is available free during preview. System requirements: macOS 13+, Windows 11, or Ubuntu 22.04+. M1/M2/M3 Mac or Windows ARM is recommended for performance. 16GB RAM minimum, 32GB recommended for larger codebases. Step 1: Download and Install Air Go to jetbrains.com/air and click Download Public Preview You’ll be prompted to log in to your JetBrains account Download the installer for your platform Run the installer — Air installs independently from other JetBrains IDEs and does not replace or affect IntelliJ IDEA, PyCharm, or other products The install is straightforward. Air does not use the JetBrains Toolbox App for management during the preview period — it’s a standalone installer. ...

GitHub Copilot CLI went generally available today with full agent mode — and it’s the most significant upgrade to terminal-native AI development since GitHub CLI launched. This guide gets you set up and running productive agent workflows from the shell without touching a browser or IDE. Prerequisites GitHub account with an active Copilot Individual, Team, or Enterprise subscription Node.js 18+ (for the npm package) GitHub CLI (gh) version 2.40 or later A Unix-like terminal (macOS Terminal, iTerm2, Windows Terminal with WSL, or any Linux terminal) Step 1: Install the Copilot CLI Extension Copilot CLI installs as a gh extension. If you have gh installed and authenticated, run: ...

Datadog just shipped an MCP (Model Context Protocol) Server that pipes live telemetry — metrics, logs, traces, and dashboards — directly into AI agents and IDE-integrated coding assistants. The result: your AI agent can query production observability data in real time without you switching to a separate monitoring tab. This is a significant practical capability. Debugging a production incident while your AI assistant has read access to the actual traces and error logs is meaningfully different from asking it to hypothesize based on a description you type. ...

A malicious npm package is actively targeting OpenClaw developers right now. Named @openclaw-ai/openclawai, the package — internally called GhostLoader but tracked publicly as GhostClaw — was uploaded to npm on March 3, 2026. Security researchers at JFrog confirmed it was still live as of March 8. If you work with OpenClaw or any tools in the OpenClaw ecosystem, you need to read this. What GhostClaw Actually Does GhostClaw doesn’t just steal one thing — it steals everything. Once you run npm install @openclaw-ai/openclawai, the package quietly re-installs itself globally via a postinstall hook, embedding itself on your system PATH without any visible prompt. ...

A developer recently watched Claude Code autonomously execute a destructive database migration that deleted 1.9 million rows from a school platform. The post-mortem was honest: “I over-relied on AI.” The data was unrecoverable. The platform was down. This will happen again. It will happen to someone using Claude Code, and to someone using another coding agent, and to someone who thought they had safeguards in place. AI agents are fast, confident, and not always right about what “cleaning up” a database means. ...

If your OpenClaw agent feels like a generic chatbot that happens to have shell access, the problem is almost certainly in your configuration files — or the lack of them. Two files, SOUL.md and HEARTBEAT.md, are the difference between a passive assistant that waits for commands and a proactive agent that knows who it’s helping, how to help them, and what to check on while you’re not looking. This guide walks through both. ...

Indirect Prompt Injection (IDPI) is now confirmed in-the-wild by Palo Alto Unit 42. Adversaries are embedding hidden instructions in web pages and documents to hijack AI agents — and OpenClaw’s browser and research agents are high-value targets. This guide walks through concrete hardening steps you can apply to your OpenClaw deployments today. Prerequisites OpenClaw installed and configured (any recent version) At least one agent with web browsing or document processing capability Basic familiarity with OpenClaw’s skill and session configuration Step 1: Audit Your Agent Attack Surface Before hardening anything, map your exposure. For each agent you run: ...