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Featured2

The piece we would hand you first.

The Harness Is the Capability Multiplier

Medley turns ambitious work into a contract-driven execution graph, then makes the harness self-improving through an unsupervised, grade-blind trace loop.

Medley turns ambitious work into a contract-driven execution graph, then makes the harness self-improving through an unsupervised, grade-blind trace loop. The resulting systems set new reported highs across terminal work, clinician conversations, full-stack applications, and drug discovery, while the learned procedures transfer across closed and open-weight models without changing model weights.

July 27, 202622 min read

Featured

The Mission Is the Unit of AI Work

People are engineering larger jobs out of agent sessions. Missions make those jobs explicit enough to govern, route, resume, and account for.

As agents take on work that spans sessions, tools, and models, the missing layer is not another loop — it is the mission contract. This article explains why larger AI work needs explicit contracts, curated context, tool policy, receipts, and durable state above any single harness or session.

July 1, 202616 min read

Essays4

Points of view on how agent work should work.

Essays

Local-First AI Coding Agents: Why Your Code Shouldn't Have to Leave Your Machine

Most agent tools route your codebase through someone else's cloud. There's a better default.

Most AI coding agents are cloud-first by default — your code, your context, and your execution all run on someone else's infrastructure. This article explains what local-first actually means for AI agents, why it matters for privacy, control, and trust, and what you give up when your codebase has to leave your machine.

June 25, 20266 min read

Essays

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.

June 22, 202610 min read

Essays

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.

June 15, 20269 min read

Essays

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.

June 15, 202610 min read

Research4

Data, benchmarks, and what the numbers actually say.

Open research hub →

ResearchPinned

The Harness Is the Capability Multiplier

Medley turns ambitious work into a contract-driven execution graph, then makes the harness self-improving through an unsupervised, grade-blind trace loop.

July 27, 202622 min read

Research

AI Coding Agent ROI: How to Measure Developer Productivity Gains

Stop guessing whether AI agents are saving you time. Here is the exact framework to calculate net value per mission.

A concrete framework for calculating the real ROI of AI coding agents — moving beyond token counts to actual developer hours saved per task.

July 8, 202610 min read

Research

Cost Per Task, Not Per Token: How to Actually Budget for AI Coding Agents

Token meters tell you what you spent. They don't tell you what it was worth.

Token-based billing makes AI coding agents impossible to budget for — you can't tell which task drove the cost, whether it was worth it, or where to cut. This article makes the case for cost-per-task accounting and explains what it takes to actually see, attribute, and control what your agents spend.

June 25, 20267 min read

Research

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.

June 15, 20269 min read

Field Notes14

Hands-on fixes and playbooks from running agents daily.

Field Notes

AI Code Review Agent Workflow: How to Review More Without Slowing Engineers Down

A practical system for routing code review work to AI agents while keeping humans in control of architecture, risk, and final approval.

Build an AI code review agent workflow that catches routine issues, routes deeper reviews to stronger models, and keeps human engineers in final control.

July 11, 202610 min read

Field Notes

Why Your AI Coding Agent Is Stuck in an Infinite Loop (And How to Fix It)

OS-level process tracking is the only reliable way to stop runaway agents and silent billing surprises.

AI agents hang for hours and burn your API budget in silent infinite loops. Learn why text-based completion fails and how OS exit codes fix it.

July 8, 20269 min read

Field Notes

Claude Code Quota Exhausted Mid-Task? Here's What to Do

A practical rescue guide for developers who hit Claude Code's rate limit wall — and how to make sure it never stops a mission again.

Hit Claude Code's quota mid-task? Learn why it happens, how to recover fast, and how Medley.sh's multi-runtime routing prevents it entirely.

July 7, 20269 min read

Field Notes

The 3-Layer Isolation Stack: How to Run Multiple AI Agents in Parallel Without Losing Work

Git worktree isolation, copy-on-write snapshots, and port namespacing — the complete technical guide to safe parallel agent execution

Learn the 3-layer isolation stack — Git worktrees, CoW DB snapshots, and port namespacing — to run multiple AI agents in parallel without conflicts.

July 5, 20269 min read

Field Notes

Why Your AI Agent Fleet Keeps Hitting 429s — And How Multi-Runtime Routing Fixes It

Exponential backoff is a band-aid. The real fix is routing around rate limits across multiple AI runtimes.

Parallel AI agents inevitably trigger 429 rate limit errors. Learn why backoff fails at scale and how multi-runtime routing solves it structurally.

July 4, 202610 min read

Field Notes

The Missing Layer in Multi-Agent Development: Solving Handoff Between Runtimes

Why developers are drowning in context switching, and how Medley.sh's Attention Queue orchestrates Claude Code, Cursor, and Gemini natively on macOS.

Developers today juggle multiple AI coding agents, leading to fragmented workflows and wasted tokens. Learn how Medley.sh solves agent handoff with a unified…

July 3, 20268 min read

Field Notes

AI Agent Task Queuing Strategies: How to Stop Being Your Own Message Bus

A practical framework for developers running multiple AI agents who need to manage a real mission backlog — without becoming the bottleneck themselves.

Learn practical AI agent task queuing strategies: prioritization, dependency ordering, parallel vs sequential execution, and the Attention Queue model for…

July 2, 202610 min read

Field Notes

Claude Code vs. Codex vs. Gemini vs. Cursor: Choosing the Right Agent for Each Task

The best AI coding agent isn't a single tool — it's the right one for the job in front of you.

There is no single best AI coding agent — Claude Code, Gemini, Cursor, and Kimi each have different strengths. This guide breaks down what each is good at, why picking one tool for everything leaves quality and money on the table, and how to route each task to the agent that fits it.

June 25, 20266 min read

Field Notes

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 approaches it — local-first, bring-your-own-keys, with no regional wall and no vendor lock-in.

June 22, 20267 min read

Field Notes

How to Run Claude Code Together Without Losing Your Mind

A Practical Guide to Parallel AI Coding Agents in 2026

Running Claude Code 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.

June 15, 20268 min read

Field Notes

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.

June 15, 20267 min read

Field Notes

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.

June 15, 20269 min read

Field Notes

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.

June 15, 20268 min read

Field Notes

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, 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.

June 15, 20267 min read