How-Tos & Practical Guides

A developer recently published an account of running 10 Claude Code agents simultaneously on their codebase — and the performance difference was not subtle. Analysis that previously took 10 minutes dropped to 3. If you’ve been running Claude Code agents serially, this guide covers exactly how to replicate that setup. Why Parallel Agents Work Claude Code’s Agent Teams architecture is built for parallelism. Each agent operates in its own context window with its own task scope, writing results to shared inboxes rather than competing for a single context. The bottleneck in serial workflows isn’t usually the model — it’s the sequential handoff pattern. ...

For most of OpenClaw’s history, backing up your configuration meant manually copying files, writing shell scripts, or hoping your dotfiles repo was up to date. With v2026.3.8, that changes: OpenClaw now ships with built-in backup CLI tools that make protecting your setup first-class behavior, not an afterthought. This guide covers the new openclaw backup commands, how to use config-only mode, how to verify your backups, and how to restore if things go wrong. ...

⚠️ Time-sensitive: Free API access for MiMo-V2-Pro expires approximately March 25, 2026. Xiaomi’s MiMo-V2-Pro is now live on OpenRouter, and for the next few days, you can run it for free. This is a frontier-class agentic model (1T parameters, sparse 42B active) that benchmarks close to Anthropic’s Opus 4.6 — and it was purpose-built for the kinds of autonomous, multi-step tasks that OpenClaw agents perform. Here’s how to hook it up in under 10 minutes. ...

Every AI agent pipeline eventually hits the same wall: documents. PDFs, Word files, scanned images, slide decks — agents need to read them all. Most solutions are either painfully slow, require an external API (and cloud costs), or demand a GPU just to process a 40-page report. LlamaIndex founder Jerry Liu announced LiteParse on X on March 19th, calling it “unglamorous but critical” infrastructure. He wasn’t wrong. LiteParse processes 500 pages in 2 seconds on CPU. No GPU. No API key. No cloud. ...

CVE-2026-33017 (CVSS 9.3) is a critical unauthenticated remote code execution vulnerability in Langflow that was actively exploited within 20 hours of public disclosure. If your Langflow instance is running version 1.8.1 or earlier and is network-accessible, treat this as an emergency. This guide walks you through patching, verification, and hardening steps to protect your deployment. Step 1: Confirm Your Current Version Check your installed Langflow version: pip show langflow | grep Version # or if running in Docker: docker exec <container_name> pip show langflow | grep Version If the output shows 1.8.1 or earlier, you are vulnerable and must patch immediately. ...

Here’s a number that should worry you if you’re shipping AI agents to production: 0.85¹⁰ = 0.197. That’s the success rate of a 10-step agentic task when each individual step has an 85% accuracy rate. Not 85% success overall — 19.7%. Your highly accurate agent fails 4 out of every 5 tasks it attempts. This is the compound probability problem, and it’s the hidden failure mode of most production AI agent deployments. ...

Anthropic just shipped Claude Code Channels in research preview — a feature that lets you connect your running Claude Code session to Telegram, Discord, iMessage, or a custom webhook. Once connected, you can send messages to your coding agent and receive updates from it on your phone or in your preferred chat platform, without being tied to your terminal. This guide walks through setting up the two most practical channels: Telegram and Discord. ...

If you’ve ever watched an AI coding agent ignore your project’s conventions — using the wrong naming scheme, adding unnecessary dependencies, writing tests that don’t match your test runner — there’s a simple fix that most developers haven’t heard of yet. It’s called AGENTS.md, and it’s now natively supported by over 25 AI coding tools. What Is AGENTS.md? AGENTS.md is a plain Markdown file you place in the root of your project repository. AI coding agents read it automatically when they start working in your codebase. It tells them everything they need to know about your project’s specific conventions, constraints, and preferences — before they write a single line of code. ...

If you’ve used GitHub Copilot, Claude Code, Cursor, or OpenAI Codex and wondered what’s actually happening under the hood when the agent “thinks,” plans a multi-step fix, and edits three files at once — Simon Willison just published the definitive practitioner answer. Willison’s new chapter of Agentic Engineering Patterns — titled “How Coding Agents Work” — is the clearest technical breakdown yet of what separates a coding agent from a coding assistant, and why that distinction matters enormously for how you use and build with these tools. ...

If you’re running AI agents in production and they have access to real tools — file systems, APIs, databases, external services — you have a security problem you may not have fully reckoned with yet. The problem: agents are not sandboxed by default. An agent that gets fed a malicious prompt (prompt injection), hallucinates a destructive command, or malfunctions can do real damage to your host system, your connected services, or your data. And most agent frameworks, even the good ones, don’t enforce OS-level isolation between the agent process and the machine it’s running on. ...