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Arya Mehta
Arya Mehta

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Building a multi-step AI pipeline workspace + 2 OSS fixes to RocketRide server

I’ve been learning RocketRide (open-source AI pipeline platform + VS Code extension) by building a small project and contributing fixes upstream.

What RocketRide is (quick)

RocketRide lets you build AI workflows as .pipe graphs inside VS Code (visual canvas), run them against a local/hosted engine (default settings often point to http://127.0.0.1:5565), and inspect step-level execution/traces.

What I built: a pipeline workspace (Next.js)

I shipped a product-shaped demo that mirrors the “runs → steps → traces → artifact” experience:

  • /: landing page + quick explanation
  • /app: enter a goal/prompt → run a multi-step pipeline → see:
    • step-by-step execution trace (detail + timing)
    • a final markdown artifact (structured handoff doc)
    • recent runs + seeded demo rows (useful in fresh Docker/CI environments)

Repo: https://github.com/aryaMehta26/multi-step-ai-pipelines

Reproducibility (important for reviewers)

  • Docker: docker compose up --buildhttp://localhost:3000
  • CI: GitHub Actions runs npm ci → lint → build

“Built on RocketRide” evidence

Alongside the web app, I included an actual RocketRide pipeline file + setup guide:

  • .pipe: pipelines/goal-planning-agent.pipe
  • docs: docs/ROCKETRIDE.md

So the submission shows both:
1) RocketRide-native .pipe workflow in the extension
2) a web UI showing how I’d present pipeline execution/traces outside the IDE

OSS contributions (2 upstream PRs)

While building, I fixed a couple small-but-real edge cases upstream:

Fork: https://github.com/aryaMehta26/rocketride-server

Feedback welcome

If you’ve built workflow UIs / agentic pipelines before, I’d love feedback on:

  • what makes traces actually useful (beyond “logs everywhere”)
  • what you’d want in a pipeline runner UI for non-IDE users

Thanks for reading!

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