Blog

Insights on AI, research workflows, and building with Medley.

Sakana Fugu Not Available in Your Region? Here's What to Use Instead

A practical guide for developers blocked by the EU/EEA restriction or beta waitlist

Sakana AI's Fugu has generated real excitement in the multi-model orchestration space — but a hard EU/EEA access block, an application-gated beta, and a Japan-first rollout have left a large share of developers locked out. This guide explains why the restrictions exist, what you actually need from a multi-model orchestration tool, and how Medley delivers it today with no waitlist, no regional wall, and no vendor lock-in.

·7 min read

Sakana Fugu vs. Medley: Two Approaches to Multi-Model Orchestration

A fair, detailed comparison of black-box API routing vs. transparent local orchestration

Sakana Fugu and Medley both route tasks across multiple AI models — but they represent fundamentally different architectural philosophies. Fugu is a managed cloud API that handles model selection invisibly. Medley is a local, transparent orchestration layer that shows you the plan, learns from your decisions, and runs on your own keys. This comparison covers five dimensions to help you choose.

·9 min read

The Problem With Black-Box Orchestration (And What Transparent Routing Actually Looks Like)

Why orchestration you can't see, audit, or steer is just a fancier single model

Black-box AI orchestration feels like magic at first — one API call, multiple models, better results. But when you can't see which model handled which sub-task, why it was chosen, or what it cost, you lose the ability to debug, control costs, and build trust in the output. This article makes the case for transparent orchestration and explains what it actually requires in practice.

·10 min read

How to Run Claude Code and Codex Together Without Losing Your Mind

A Practical Guide to Parallel AI Coding Agents in 2026

Running Claude Code and Codex in parallel can dramatically accelerate your development workflow — but without the right coordination patterns, you end up with conflicting edits, lost context, and more overhead than you saved. This guide covers the real pain points of multi-agent coding and the patterns that actually work in 2026.

·8 min read

Medley vs. Conductor: Which AI Agent Orchestrator Is Right for You?

A Honest Comparison for Builders Running Parallel Coding Agents

Conductor (by Melty Labs) and Medley both help developers run multiple AI coding agents on Mac — but they solve different problems. This comparison breaks down where each tool wins so you can pick the right one for your workflow.

·7 min read

Medley vs. Devin Desktop: Managing Agents Without Vendor Lock-In

Why Model-Agnostic Orchestration Beats Vendor Lock-In for Serious Builders

Devin Desktop is a powerful enterprise-grade agent environment, but it’s built around Cognition’s own model and cloud. This comparison explores why model-agnostic orchestration — and owning your agent stack — matters for serious builders and technical teams.

·9 min read

Missions vs. Sessions: Why the Unit of AI Work Is Changing

The Shift From Ephemeral Sessions to Persistent, Decomposable Missions

The session has been the default unit of AI work since the first chat interface launched — but it was never the right unit. This article makes the case that the shift from ephemeral, stateless sessions to persistent, decomposable missions is the defining productivity unlock of the current AI era, and explains what that shift means for how builders think about and manage their work.

·9 min read

What Is AI Agent Orchestration? A Plain-English Guide for Builders in 2026

Everything Builders Need to Know About Multi-Agent Coordination

AI agent orchestration is the practice of coordinating multiple AI agents across a shared goal — decomposing work, routing tasks, managing context, and surfacing decisions that need a human. This plain-English guide explains what orchestration means in 2026, why it matters now more than ever, and what a practical orchestration layer actually looks like for builders.

·8 min read

The Problem With Managing AI Agents in Terminal Tabs

Why Your Terminal Tab Habit Is Costing You More Than You Think

If you’re running Claude Code, Codex, or other AI coding agents across four or more terminal tabs, you already know the pain: lost context, silent failures, and the constant overhead of re-explaining your project from scratch. This article breaks down why the terminal tab habit is quietly destroying your productivity — and what the missing layer above your agents actually looks like.

·7 min read

The Attention Queue: The Missing Layer in Every AI Agent Setup

Why Human-in-the-Loop Needs a Better Interface Than a Kanban Board

As AI agents multiply, the bottleneck isn’t agent capability — it’s human attention. Most developers manage their agents through a patchwork of chat transcripts, status columns, and notification pings that fragment focus rather than protect it. This article introduces the attention queue as the missing primitive in every multi-agent setup.

·9 min read

When Your AI Agent Gets Shut Down

The Case for Open Source and Local Models

Cloud-only AI agent setups carry risks most developers underestimate — from sudden shutdowns to pricing changes to geopolitical friction. This article uses a real deactivation event as a news hook to make the practical case for open source and local models, and explains why a BYOK, model-agnostic architecture is the right structural response.

·9 min read

Why Smaller Models Win

The Case for Multi-Model Ensembling in AI Agent Workflows

The default assumption — bigger model, better results — breaks down for complex, multi-step missions. This article makes the case for multi-model ensembling: decomposing work and routing each sub-task to the right-sized model consistently outperforms monolithic frontier execution on cost, latency, error isolation, and often quality.

·9 min read