EveryInc

EveryInc / after-automation-agent-mode

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Agent Mode companion kit for After Automation

10
0
100% credibility
Found May 24, 2026 at 12 stars 3x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

This is a companion guide for Dan Shipper's essay 'After Automation,' published by Every.to. The essay argues that AI makes yesterday's competence cheap, which paradoxically creates more human work—not less—because cheap competence leads to more attempts, more sameness, and greater demand for human judgment and framing. This repository provides a structured way to understand, examine, and most importantly apply the essay's ideas to your own work. It includes a claims map, starter prompts, objection responses, and real-world workflow examples from Every's own AI-native practices. Users paste a simple instruction into their AI assistant to work through the material and develop practical human-AI collaboration workflows.

How It Works

1
📖 You discover the essay

You find Dan Shipper's essay 'After Automation' and read about his surprising discovery: automating everything actually created more work for humans, not less.

2
💡 The idea sticks with you

The argument resonates—you've noticed AI makes things easier, yet somehow there's always more to do. You want to understand why and what to do about it.

3
🗺️ You find the companion guide

You discover there's a free companion guide on GitHub that helps you actually use the essay's ideas in your own work, not just read about them.

4
🤖 You bring in your AI assistant

You paste a simple instruction into your AI coding assistant, and it learns about the essay and how to help you apply it.

5
You choose your path
📚
Understand the argument

Get a clear explanation of the core claim and why it matters

🔍
Inspect the evidence

Dive into the claims and sources to see what's really behind the argument

🛠️
Apply it to your work

Have your assistant learn about your situation and suggest practical workflows to try

⚔️
Work through objections

Test the essay against the strongest counterarguments you can think of

6
🎯 You take action

Following the prompts and examples, you build a real human-AI workflow where you frame the work, your assistant executes, and you review the results.

✨ You have a workflow that works

You walk away with a practical approach to collaborate with AI that plays to human strengths—framing, judgment, and specificity.

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AI-Generated Review

What is after-automation-agent-mode?

This is a companion kit for Dan Shipper's essay "After Automation" -- not a traditional codebase but a curated set of prompts and workflows designed to be fed to AI coding agents like Claude Code, Codex, or OpenClaw. It helps you extract practical value from the essay by giving your agent a structured way to understand the argument, inspect evidence, and apply the concepts to your own work. The repo contains starter prompts, objection-handling guides, claims maps, and real Every workflow examples you can adapt.

Why is it gaining traction?

The core thesis -- that AI makes competence cheap, which creates more attempts, more sameness, and more demand for human judgment -- resonates with developers feeling anxious about their role in an AI-powered future. This repo monetizes that anxiety into actionable workflows: instead of debating whether AI will replace you, it provides specific human-agent collaboration patterns. The hook is simple: copy a prompt, paste it into your coding agent, and get a personalized workflow for your team by end of day.

Who should use this?

Product teams exploring AI-native workflows will find the most here -- the case studies cover Slack mining, code review pipelines, and support-to-product routing. Individual developers skeptical about AI hype will appreciate the objections-and-responses section, which addresses common counterarguments head-on. If you just want to read the essay and think about it, skip this. If you want your agent to help you operationalize it, this is the on-ramp.

Verdict

At 10 stars and 1.0% credibility, this is an early-stage thought experiment, not production-grade infrastructure. The ideas are genuinely useful, but the repo is a set of markdown files with no code, tests, or active maintenance. Try it if you're experimenting with agent mode workflows and want a structured way to think through human-AI collaboration. Do not adopt this as a team standard -- it's a starting point, not a solution.

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