Editorial illustration for ShipAI Project Pages Feature Video Walkthroughs from Creators
ShipAI: Video Walkthroughs Prove AI Projects Work
ShipAI Project Pages Feature Video Walkthroughs from Creators
Towards Data Science launched ShipAI today, a video showcase built around a simple test: an AI project counts as real once someone can watch it actually run. The format is straightforward. Practitioners record a screen-share walkthrough, four to fifteen minutes long, covering what pushed them to build the thing, how they built it, and what broke along the way. TDS reviews each submission before it goes live.
Every project page carries more than the video. There are AI-generated takeaways for a ten-second scan, a cleaned-up searchable transcript, and stack notes listing the models and infrastructure behind the build. Links point out to GitHub, Hugging Face, or a live demo, since TDS is showcasing the work rather than hosting it. Builders also get a profile page that collects everything they've shipped in one place.
The site opens with more than 30 walkthroughs recorded over the summer by a group of founding builders, many of them familiar TDS authors. The lineup ranges from a RAG chunk-size experiment to a warehouse damage-report generator built for AI use, with one contributor turning a CV into a playable video game.
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.
Why this matters
For readers tired of AI demos that are really just slide decks with a voiceover, ShipAI's screen-share format is a useful filter. Watching someone actually run their agent or pipeline for four to fifteen minutes tells you more than a polished announcement post ever will. It's a low bar, but it's one a lot of "AI projects" quietly fail to clear.
For developers and founders, this is a chance to get a walkthrough in front of a Towards Data Science audience without needing a marketing budget or a launch thread. That's worth paying attention to if you've built something and have nowhere good to show it running. For researchers, the AI-generated takeaways are a smaller thing but a practical one: a ten-second way to decide if a fifteen-minute video is worth your time.
The real test is what TDS lets through. A showcase is only as credible as its editorial bar, and "we review and publish it" doesn't say much about what gets rejected. Worth watching whether ShipAI stays a place for working demos or drifts into another venue for polished pitches.
Related Reading
Business & Startups Nordic pilot adds Gemini for Education, NotebookLM to boost AI literacy
Business & Startups Kling launches Video O1, all-in-one model with MVL bridge using transformer
Business & Startups DeepSeek Seeks More Capital Weeks After USD 7B Funding Round
From Archives Anthropic's USD 517 Billion Compute Deals Trail OpenAI's 2030 Goal
From Archives Perplexity Details GPU Embedding Stack: Ivy, Tulip, ROSE Serve pplx-embed
Common Questions Answered
What is the core requirement for an AI project to be featured on ShipAI?
An AI project must be demonstrated actually running through a screen-share walkthrough video to qualify for ShipAI. This means the project needs to go beyond theoretical presentations or slide decks and show real, functional execution of the AI system in action.
What length and content should creators include in their ShipAI video walkthroughs?
Creators should record screen-share walkthroughs between four to fifteen minutes long that cover what inspired them to build the project, how they built it, how it works, and what they learned along the way. Each video should demonstrate the actual functioning of their AI project while explaining their development process and challenges encountered.
How does ShipAI differentiate itself from typical AI project announcements?
ShipAI filters out polished but non-functional AI demos by requiring actual screen-share demonstrations rather than slide decks with voiceovers. This format provides viewers with a more authentic understanding of whether an AI project genuinely works, setting a practical standard that many announced AI projects fail to meet.
What additional content accompanies the video walkthroughs on ShipAI project pages?
Beyond the video walkthrough, each ShipAI project page includes AI-generated takeaways to provide supplementary insights about the project. This additional content helps readers quickly understand key learnings and technical details from the creator's demonstration.
What review process does Towards Data Science apply to ShipAI submissions?
Towards Data Science reviews each ShipAI submission before it goes live on the platform to ensure quality and accuracy. This editorial oversight helps maintain the credibility and reliability of projects featured in the showcase.
