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I Vibe Coded a GitHub Action, Then Turned It Into a Real Product: Inside Proof of Shipping

Hamza says he vibe coded a GitHub Action showing delivered work, not just activity, then found shipping it took real engineering. Here is his account and what it does and doesn't prove.
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A developer named Hamza says he built a GitHub Action with AI-assisted “vibe coding,” then found the project demanded real software engineering before it could become a product. The Action is called Proof of Shipping. It puts delivered work on a GitHub profile, such as releases and merged pull requests, in place of raw activity counts. Everything below is the author’s own account, from a DEV Community post dated Sep 27 (a search listing indicates 2025). Adoption, revenue and code quality have not been independently established.

The problem: activity is not delivery

Hamza’s starting point is a distinction. Most profile widgets answer questions like “How active is this developer?” and “How often do they commit?” He argues that the more useful question is the one he poses in the post: “What has this developer actually delivered?” (DEV Community).

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This is his framing. It doesn’t claim that contribution graphs or language statistics are worthless. It claims they measure a different thing.

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Kind of signal Examples named in the post What it mostly answers
Activity Contribution graphs, streaks, language stats, commit counters How often and how much someone participates
Delivery Published releases, merged pull requests, latest delivery, a configurable time window What was actually shipped in a period

Each has blind spots. Activity counts can’t tell a trivial commit from a shipped feature. Delivery counts miss work that is real but unreleased, such as research, review, and maintenance without a tagged release. The post doesn’t show that Proof of Shipping is more accurate than other widgets, and it names no competitors.

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What Proof of Shipping does

According to the author, it is a GitHub Action that generates a visual card from public GitHub activity. It runs inside GitHub Actions and updates an SVG stored in the user’s profile repository. The card can show:

  • published releases
  • merged pull requests
  • the latest delivery
  • activity over a configurable period

The post links a project repository, the author’s profile and a v1.0.0 release. The post’s text doesn’t disclose the implementation language, the permissions the workflow needs, or how the integration is designed. Check the repository’s workflow file and token scopes before adding any third-party Action to your profile repo.

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From vibe coding to a product

The author lays out this path: idea, AI-assisted coding, tests, real integration, release, GitHub Marketplace. His central line is: “Because somewhere along the way, it stopped feeling like simple vibe coding and started requiring actual software engineering.”

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The takeaway is about the gap between a prototype and something others install. Tests, integration with the real GitHub environment, a versioned release and a Marketplace listing are all work beyond generating code. The post doesn’t name the AI tool, describe the test suite, or cover support burden. It would be a mistake to read the story as AI producing a production-ready product on its own.

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The author’s example output

The post demonstrates the card with one snapshot: zero releases in the selected period, seven merged pull requests, and a latest delivery from a repository called petmingle. It’s a single self-reported illustration. It isn’t a benchmark, and it says nothing about how other users’ cards look.

It does show a useful property of the delivery lens. A developer can have zero releases and still show real merged work, so the card separates “released” from “merged.” Whether that reads as productive depends on the project’s release practice.

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What “real product” does and doesn’t mean here

The post reports a public release and a Marketplace listing. It gives no sales, user counts, pricing or independent reviews. A Marketplace listing shows distribution, not commercial success. Current listing status and repository maintenance weren’t independently verified, so check the repository directly if you plan to depend on it.

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Lessons for anyone shipping an AI-built tool

  • Define the question your tool answers before generating code. Here it is “what was delivered?”, not “how busy was I?”
  • Add tests early, since generated code that appears to work can fail against the real GitHub environment.
  • Test against real accounts and repositories, not mocked data alone.
  • Cut a versioned release so users can pin to it instead of tracking a moving branch.
  • Treat a marketplace listing as the start of maintenance, not the finish.

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