AI-Assisted Development: Vibe Coding Bridges the Gap for Stalled GraphQL Project

The developer behind xeiaso.net recently shared an illuminating case study in AI-assisted software development, detailing how they finally shipped a long-stalled sponsor panel project using what they call 'vibe coding.' The project had languished for months due to difficulties with GraphQL integration in Go, but was completed in a weekend using AI code generation tools with carefully prepared context.

The GraphQL Challenge in Go

The author's experience reflects a common sentiment among Go developers working with GraphQL. 'Go and GraphQL are oil and water,' they write, noting that the library ecosystem presents significant challenges. Popular tools like shurcooL/graphql require 'abusive struct tags' for reflection-based query generation, while code generation tools produce extensive boilerplate.

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Previous attempts to build the sponsor panel consistently failed at the same point: when queries required four levels of nested connections and the necessary struct tags became unwieldy. The removal of GitHub's GraphQL explorer further complicated the process, leaving developers to rely on documentation and guesswork.

Vibe Coding: A Methodical Approach

Rather than treating AI code generation as a 'type a prompt and pray' scenario, the author implemented a structured approach. They prepared four specific skills to load into the context window:

1. templ-syntax: The actual syntax for Templ, the Go HTML templating library
2. templ-components: Reusable component patterns and composition techniques
3. templ-htmx: Specific guidance on combining Templ with HTMX
4. templ-http: Proper integration with net/http handlers

These documents provided the model with authoritative references rather than requiring it to invent syntax from limited training data. The result was Templ code that compiled on the first try, a significant improvement over the author's manual attempts.

Agent Teamwork and Parallel Development

The author employed an agent team approach, with one team splitting the written specification into tasks and building components, while a second team reviewed output and flagged issues. Meanwhile, the author handled infrastructure provisioning—OAuth credentials, database setup, and storage configuration—creating a parallel workflow between human operations and AI code generation.

The resulting GraphQL code, while functional, is what the author describes as 'ugly'—raw query strings with manual JSON parsing that would fail standard linting checks. However, it works reliably, which the author prioritizes over theoretical cleanliness.