sohaibt

sohaibt / product-mode

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The most expensive bug in AI-assisted building is shipping the wrong thing, well. A CLAUDE.md for PM + eng teams using Claude Code- 7 principles for problem framing, scope, tradeoffs, and outcome measurement. Built on @karpathy's observations.

www.masteringproducthq.com ai-coding ai-product-management claude claude-ai claude-code
10
0
100% credibility
Found Apr 17, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A markdown template providing product management principles and checklists for teams using AI coding assistants to ensure they build the right features.

How It Works

1
🔍 Discover the guide

While searching for ways to build better products with AI helpers, you find this simple guide for product teams.

2
📖 Understand the pitfalls

You read how teams often rush to build fancy features that solve the wrong problems using AI tools.

3
💡 See the seven principles

The guide reveals seven clear principles and a quick checklist to frame problems right and avoid waste.

4
Add the guide to your project
🆕
Start fresh

Save the full guide as a new notes file for your project.

➕
Update existing

Blend the guide into your current project notes file.

5
✏️ Make it yours

Tweak the guide with details about your users, goals, and what you avoid.

6
✅ Use before building

Before any big change, run through the checklist to plan smartly and log decisions.

🚀 Ship what matters

Your team now builds features that delight users and drive real success, feeling confident and efficient.

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AI-Generated Review

What is product-mode?

product-mode delivers a Markdown-based guide with seven principles, a pre-flight checklist, and a skip-rigor table to frame problems, scope changes, tradeoffs, and outcomes before coding with AI tools like Claude Code or Cursor. It tackles the most expensive bug in AI-assisted building—shipping the wrong thing well—by adding product discipline to engineering prompts, much like avoiding the most expensive car in the world if it solves the wrong problem. Install it via a simple curl command into new or existing projects for instant team alignment.

Why is it gaining traction?

Unlike pure engineering guides like Karpathy's, it targets product failures in mixed PM-eng teams, surfacing assumptions, naming tradeoffs, and measuring outcomes upfront—features devs notice in fewer reworks. The hook is its bash-friendly install and customization for project context, standing out amid github most popular repos by biasing rigor without slowing trivial fixes. It resonates in github most starred discussions on AI pitfalls, echoing most github stars repo patterns for lightweight process tools.

Who should use this?

PMs pairing with Claude Code or Cursor who want AI to act like a product partner, not just a coder. Engineering teams in SaaS startups tired of gold-plating irrelevant features. Solo founders battling scope creep on side projects, especially those tracking most github contributions but shipping blindly.

Verdict

Grab it if you're in early AI-product workflows—low 1.0% credibility score and 10 stars reflect its raw, single-doc maturity, but solid docs and MIT license make forking easy. Test on a non-trivial change; skip if you're purely in github most used languages like JS without PM overhead.

(178 words)

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