Use Claude code without spending time on setup - The PM version

Why the first hour with any AI dev tool is broken for most product people -- and what I built to fix it.

A personal story, of how I'd rather build whatever I need to keep myself unblocked. A story of how I'm becoming a manager of AI Agents in my daily workflow. Follow this newsletter and I'll keep posting more insights on which workflows could automate your everyday tasks and cater to your aspirations.

But first let me quickly demo what it is so that I have your attention:

You have watched the demos. Your mind is already three steps ahead — prototype that feature idea, spin up that internal tool, wire your marketing stack into a live data pipeline. You open your laptop and you are ready to actually make something. Then someone tells you to "just set up your environment first."

Four hours later, you have not built anything. You have survived configuration.

That gap — between "ready to build" and "actually building" — is what I call the Setup Tax. And it is the real reason most non-developer product people never end up prototyping their ideas. Not because the tools are too complex, and not because the ideas are too ambitious. Because the front door is broken.

Setting up should not be the hardest part

In practical terms, "environment setup" means getting your laptop to a state where a collection of tools can run, find each other, and talk to the right credentials. Think of it like this: before your team can use a new SaaS platform, someone has to create the workspace, invite people, connect the integrations, and set the permissions. Developer environment setup is that same process — except the SaaS platform is five tools built by different companies, each documented separately, and none of them know the others exist.

The modern AI development stack has made this considerably worse. To use something like Claude Code with a locally running AI model — the setup that lets you build without routing your code through third-party servers — you now need four or five tools running in coordination. A model runner that keeps the AI available on your machine. A bridge layer that translates how one tool talks to another. An IDE plugin that actually surfaces the AI in your editor. And a set of credentials and system configurations that all need to point at each other correctly. Each of these tools was built by a separate team, assumes the others are already installed, and offers you a Stack Overflow thread when something goes wrong.

These numbers are for professional software engineers. The situation is considerably worse for product people and marketers approaching these tools for the first time, often without a senior engineer on call to debug with them.

"The bottleneck between wanting to build and actually building is almost never skill. It is the absence of a designed front door."

Why This Matters Now

The leverage is real. But it has a cover charge.

The most significant shift in product management over the past 18 months is not AI — it is access. Tools like Claude Code, Cursor, and Codex have made it genuinely possible for a non-engineer with a clear product hypothesis to prototype something working in an afternoon. A PM who once needed a two-sprint engineering cycle to validate a feature idea can now do it in a weekend. That leverage is real, and it is compounding fast for those using it.

But that leverage is conditional. The condition is: you have to get past step one.

The same stakes apply to digital marketing managers navigating technical integrations. Meta and Google's ad platforms increasingly reward teams that move fast on server-side events, custom attribution models, and real-time conversion data pipelines. Teams that can build those quickly compound their campaign advantage over teams that wait in the engineering ticket queue. That queue is not usually a prioritization problem. It is a setup and access problem — the people with the ideas cannot move without the people who know how to configure the tools.

💡Cue is built specifically for that gap.

What I Built

I switched to Linux, and setup broke immediately

Earlier this year I migrated from Windows to Pop!_OS Linux — largely because I was tired of an operating system that felt like it was selling me something every time I opened it. Copilot nudges in the taskbar. Ads embedded in the Start menu. I wanted a clean environment that worked for me, not around me.

What I found on the other side was that getting my developer tools running was not a solved problem. Every tool I needed had its own documentation, its own set of prerequisites, and its own assumption about what I had already installed. To set up Claude Code with a locally running AI model, I needed a model runner, a bridge connecting it to my editor, a set of credentials pointing in the right directions, and the patience to discover each failure through its error message. There was no guide that covered all of it. There was no tool that orchestrated any of it.

What I needed did not exist. So I built it.

Cue is a zero-dependency command-line tool that orchestrates your entire developer environment setup. Single binary. No complex prerequisites. It works on Mac, Linux, and Windows. It handles the coordination problem that currently falls on you — queuing installs that would otherwise conflict with each other, automatically pausing and resuming large downloads when your network drops, and guiding you through credential setup with an interactive, step-by-step wizard. You do not need to know any of this is happening. You just need to run three commands.

The Fix

Three commands. Fifteen minutes. Working AI environment.

The typical path from a fresh laptop to a working Claude Code setup — the kind where you can actually start building something — takes anywhere from three to eight hours if you are doing it manually. Here is how Cue compresses that into fifteen minutes.

The diagram below shows both paths. On the left: the traditional experience — five distinct failure points, manual restarts, no guidance at any stage. On the right: the Cue path — three commands, invisible failure handling, and a guided finish line.

The Bigger Picture

Nobody owns step one

Every major AI coding tool assumes you already have a working environment. Every tutorial begins at step two. Cursor assumes Node.js is installed. Claude Code's documentation assumes you have already configured your shell. Codex assumes your API keys are set. Nobody has taken ownership of the moment before any of that — and for the growing number of PMs, operators, and marketers who want to use these tools, that gap is often the difference between building and not building.

Cloud-based alternatives try to solve this by moving everything to a remote server. That works for some workflows. It does not work for privacy-sensitive projects, for offline scenarios, or for the growing number of people who want to run AI models locally so that their code and data never leave their own machine. Cue is built for that ecosystem — local, private, and with no subscription required.

It is open source, free, and opinionated in exactly the way a product manager would be opinionated: it has a clear point of view, it makes sensible decisions on your behalf, and it optimizes for the outcome — a working environment — rather than giving you full manual control over every intermediate step.

If you install it and find friction I missed — in the flow, the instructions, the error messages — I want to hear it. The best product feedback always comes from the first people to find the gap the builder could not see.

The Setup Tax is real. It is optional. Let us stop paying it.

Check out cue: https://github.com/GyaneshSamanta/cue

If this resonated, share it with a friend who has been meaning to try Claude Code but has not gotten past the setup. The most useful thing this newsletter can do is save someone else the three hours.