I'm Jakub and I'm the founder of Deepnote (https://deepnote.com/). We're building a better data science notebook.
Notebooks as we know them today have many pain points (versioning, reproducibility, collaboration). They don't work well with other tools. They don't exactly encourage best practices. But none of these are fundamental flaws of the notebook paradigm. They are signs of a new computational medium. Much like spreadsheets in the 1980s.
Two years ago, my co-founders and I started to think about a better data science notebook. Deepnote is built on top of the Jupyter ecosystem. We are using the same format, and we intend to remain fully compatible in both directions. But to solve the above problems, we've introduced significant changes.
First, we made collaboration a first-class citizen. To allow for this, Deepnote runs in the cloud by default. Every Deepnote notebook is easily shareable (like Google Docs) and easy to understand even by non-technical users.
Second, we completely redesigned the interface to encourage best practices, write clean code, define dependencies, and create reproducible notebooks. We also built a really good autocomplete system, and added a variable explorer.
Third, we made Deepnote easy to integrate with other services. We didn't want to build another data science platform where people work with an iframed notebook. We want to build an amazing notebook that plays well with other services, databases, ML platforms, and the Jupyter ecosystem.
Check out a 2-min demo here: https://www.loom.com/share/b7e05ecca78047c2a2f687d77be8ecea
Building a new computational medium is hard. It takes time. Today, we're launching a public beta of Deepnote. Not everything works yet. Some pieces are missing. But we also have a lot in store, including versioning, code reviews, visualizations. We still have a lot to learn too, so I'd love to hear your thoughts and feedback.