Blog
Insights on agentic development, multi-agent workflows, and building developer tools.
What Is Agentic Development? A Beginner's Guide
Agentic development means using AI agents that autonomously write, test, and review code. Here's what it is, how it works, and how to get started.
How to Automate Code Review with AI Agents
Set up an automated AI code review pipeline where one agent writes code and another reviews it — with practical examples and configuration.
How to Run Multiple AI Agents at the Same Time
A step-by-step guide to running Claude Code, Codex, and other AI coding agents simultaneously for faster development.
Claude Code Tips: 10 Ways to Be More Productive in 2026
Practical tips for getting more out of Claude Code — from keyboard shortcuts to multi-agent workflows. Updated for 2026.
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Claude Code vs Cursor vs Copilot: Choosing the Right AI Coding Tool in 2026
An honest comparison of Claude Code, Cursor, and GitHub Copilot — pricing, features, strengths, and when to use each one.
From Single Agent to Multi-Agent: A Practical Migration Guide
Most developers start with one AI agent. Here's how to scale to multiple agents working in parallel — and why it changes everything.
5 Multi-Agent Patterns That Actually Work
Battle-tested patterns for orchestrating multiple AI coding agents, from simple pipelines to self-correcting review loops.
Spatial Thinking for Software Development
Why arranging your work on a canvas isn't just prettier — it's cognitively better. The case for spatial interfaces in developer tools.
Why Canvas-Based Agent Orchestration Matters
Traditional chat interfaces force linear thinking. Canvas-based orchestration lets you see, connect, and control multiple AI agents simultaneously.
Under the Hood: How JSONL Session Resolution Powers Agent Routing
A deep dive into how MadoHub reads Claude Code's session logs to extract clean, structured responses for intelligent routing.
Building Multi-Agent Workflows with MadoHub
A practical guide to composing multiple AI agents into reliable workflows using the MadoHub canvas.
Intelligent Routing: How AI Agents Talk to Each Other
Routing is the hidden infrastructure of multi-agent systems. Here's how MadoHub makes agent-to-agent communication explicit, inspectable, and configurable.
Why We Built MadoHub with Tauri Instead of Electron
The technical reasoning behind choosing Tauri v2 for a desktop development tool — performance, binary size, and Rust's advantages for system integration.
Why We Built an Orchestrator, Not More Routing Rules
Static trigger/transform rules scale badly once your canvas grows past a couple of agents. Here's why MadoHub now routes through a real tool-calling loop instead — and when you should still use rules.
Best-of-N Coding: Ensemble Exploration and Auto-Critique
One task, N divergent forks, a critic that scores them, and a human who picks the winner. When best-of-N earns its token cost, and when it doesn't.
Mission Control: Never Lose Track of Which Agent Needs You
Running six agents means the canvas stops answering the one question that matters — who needs you right now. Here's how Fleet's state grouping and the pending-question tail fix that.
Teaching MadoHub to Remember
Inside MadoHub's memory system: six kinds of memory, namespacing by persona and project, and why nothing an agent learns becomes permanent without a human looking at it first.
Local, Private Voice Input with On-Device Whisper
Why MadoHub runs speech recognition on your machine instead of calling a cloud API, what that means for privacy and cost, and how to get the most out of dictation.
Claude Code's Completion Signals Keep Changing. Here's How We Keep Up
Why keyword-matching on Claude Code's completion banner kept breaking, and what MadoHub actually trusts to know when an agent's turn is over.