
ReadMe
GitBook
Mintlify
Docusaurus
Archbee.io
Postman
Swagger UI
Document360
Deepnote
Apache Zeppelin
Saturn Cloud
Amazon SageMaker
Databricks Unified Analytics Platform
Azure Synapse Analytics
Google BigQuery
GeoSpock
ReadMeReadMe is recommended for tech companies, API developers, software development teams, product managers, and any organization that needs to create, maintain, and improve the usability of their API documentation. It is particularly beneficial for teams that prioritize collaborative documentation processes and wish to offer users a modern documentation interface.
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Deepnote might be a bit more popular than ReadMe. We know about 34 links to it since March 2021 and only 28 links to ReadMe. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
ReadMe specializes in creating stunning developer experiences. If your APIโs success depends on attracting external developers, ReadMeโs polish and developer-centric features deserve consideration. - Source: dev.to / 9 months ago
In this comparison, we examine four leading platforms: Theneo's AI-first approach with complete developer portals, Redocly's spec-governance excellence, ReadMe's content-centric hubs, and Mintlify's beautiful Git-native design. We'll evaluate each across critical dimensionsโautomation capabilities, collaboration workflows, agent discoverability, and pricing valueโto help you find the perfect fit for your team's... - Source: dev.to / 8 months ago
ReadMe is fantastic for API documentation specifically. The interactive API explorer is genuinely impressive. But if you need more than API docs; tutorials, conceptual guides, getting started content, it starts to feel like you're fighting the platform. - Source: dev.to / 8 months ago
ReadMe delivers story-like docs with changelogs, feedback loops, and embeddable in-app guidance. - Source: dev.to / 11 months ago
Readme.com make your API look good enough to care about. - Source: dev.to / about 1 year ago
Thank you for the list - I think I've come across all of these in my research! I'll try highlight the differences for each. - https://noteable.io/ - as you say, it doesn't exist anymore - https://deepnote.com - I actually mentioned this in the post but in my experience, the UX and features far behind what we've built already. I'd love to hear from anyone who's tried jupyter-ai to give us a shot and let me know... - Source: Hacker News / about 2 years ago
- https://deepnote.com -- also extensive AI integration and realtime collaboration. - Source: Hacker News / about 2 years ago
Deepnote - A new data science notebook. Jupyter is compatible with real-time collaboration and running in the cloud. The free tier includes unlimited personal projects, up to 750 hours of standard hardware, and teams with up to 3 editors. - Source: dev.to / over 2 years ago
We looked into many of these issues with Deepnote (YC S19) [https://deepnote.com/]. What we found is that these are not necessarily problems of the underlying medium (a notebook), but more of the specific implementation (Jupyter). We've seen a lot of progress in the Jupyter ecosystem, but unfortunately almost none in the areas you mentioned. - Source: Hacker News / about 3 years ago
Upload your ipynb to Deepnote and publish as an app. That simple. https://deepnote.com. - Source: Hacker News / over 3 years ago
GitBook - Modern Publishing, Simply taking your books from ideas to finished, polished books.
Apache Zeppelin - A web-based notebook that enables interactive data analytics.
Mintlify - The AI-powered documentation writer. It's documentation that just appears as you build
Saturn Cloud - ML in the cloud. Loved by Data Scientists, Control for IT. Advance your business's ML capabilities through the entire experiment tracking lifecycle. Available on multiple clouds: AWS, Azure, GCP, and OCI.
Docusaurus - Easy to maintain open source documentation websites
Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.