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Towards Data Science launches ShipAI for video-based AI project showcases

Towards Data Science has launched ShipAI, a new platform designed to validate AI projects through video evidence. The service allows practitioners to submit screen-share walkthroughs of their AI applications, agents, or pipelines, which are then reviewed and published by the TDS editorial team. Each entry features a 4-15 minute video explaining the project's inspiration, architecture, and…

1 source

Key points

  • ShipAI is a new TDS platform featuring video walkthroughs of AI projects to demonstrate real-world functionality.
  • The launch includes over 30 initial entries covering various AI stages, from weekend experiments to production systems.
  • Each project page includes AI-generated takeaways, transcripts, and links to code repositories like GitHub or Hugging Face.

The platform launched with over 30 pre-recorded walkthroughs from founding builders, including examples ranging from RAG experiments to production-ready warehouse tools. ShipAI does not host the code itself but links to repositories on GitHub or Hugging Face. The initiative aims to shift focus from written claims to demonstrable functionality, helping the community distinguish between theoretical concepts and working software. Submissions are accepted via a simple form where users upload links to their screen recordings.

Full story from Towards Data Science · by TDS Editors Open source ↗

Introducing ShipAI

Towards Data Science · 8 September 2026

How do you know an AI project is real? You know it's real when you watch it run.

That's the idea behind ShipAI, which is live on TDS today. It's a video showcase for AI work: practitioners record a screen-share walkthrough of something they've built (an app, an agent, a pipeline, an experiment), and we review and publish it.

What's on a project page

Every entry is a video walkthrough (4-15 minutes long) in which the creator explains what inspired their project, how they built it, how it works, and what they've learned along the way. Around the video you'll find:

  • AI-generated key takeaways , so you can decide in ten seconds whether to press play.
  • A searchable transcript , lightly cleaned, so you can jump straight to the part you need.
  • Stack notes , including the models, infrastructure, and tools in the build, linked out.
  • Links to where the project lives , whether on GitHub, Hugging Face, or a live demo. We showcase the work; we don't host it.

Every builder also gets a profile page collecting everything they've shipped.

What you'll find on day one

ShipAI opens with more than 30 published walkthroughs recorded over the summer by our founding builders, most of them TDS authors whose work you may already know. We already published a retrieval experiment on RAG chunk sizes, an AI damage-report generator for warehouse operations, and a CV turned into a playable video game.

Some are weekend experiments, while others are already running in production: we showcase projects across a wide range of development stages.

An editor reviews every submission

The same editorial team that reviews TDS articles also runs ShipAI. We don’t prioritize production value and flashy visuals. Instead, we choose videos that help other builders understand what you made, how, and why.

Show us what you built

Have you shipped something real recently? We want to see it! Record a screen walkthrough (using Loom, OBS, QuickTime, or any other tool that captures your screen and your voice) and send us the link through the submission form.

ShipAI is now live at towardsdatascience.com/shipai. Go watch what people are building. And if one of the walkthroughs leaves you thinking "I should share my project, too," that's just what we're after.

Questions about a submission, or anything else? Email us at shipai@towardsdatascience.com.

This text was published by Towards Data Science and written by TDS Editors. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗

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