Hello, Shipset
Why we are building a practice ground for shipping real AI systems, not just learning the API surface.
The AI engineering job market exploded over the last 18 months, but most of the learning material still stops where the real work begins. You can find a thousand notebooks that ingest a PDF, embed it, and answer one question. You can find about three that explain how to keep that pipeline running at 3 AM on a Tuesday when the embedding API rate-limits you, the user uploads a 400-page contract, and your retrieval recall drops below 60%.
Shipset is a bet on the second category.
What we are building
A practice ground for AI engineering challenges that look like real product requirements. Each challenge gives you a brief — “build a RAG system over our internal docs, here's a deployment URL we'll ping, ship something we can demo to a customer” — plus a structured checklist of stages and tests. You build, you deploy, you submit a public URL plus a GitHub repo, and your work joins the community feed.
The point isn't to memorize the OpenAI SDK. The point is to wire together model calls, retrieval, evaluation, and observability into systems that survive contact with real input.
What you can do today
- Sign up for a free account
- Browse the challenge catalogue (we ship the first batch in the next sprint)
- Enable 2FA on your account (we already do this properly)
- Read our Privacy and Terms — they're drafts pending a counsel review, but they describe how we actually run the platform today
What's next
A lot. The challenge catalogue is the obvious one. Submissions, the community feed, Discord integration, Pro credits, and a much nicer blog (this post is plain prose; future posts will have proper code blocks once we wire up syntax highlighting).
If you are reading this in pre-launch and you have feedback, email me — I read every message.
— Julio