Updated:

Gian Segato

The Data Team @ Replit

Data privacy and data security is one of the most stringent constraints in the design of our information architecture. As already mentioned in past blog posts , we only use public Repls for analytics and AI training: any user code that's not public - including all enterprise accounts - is not reviewed. And even for public Repls, when training and running analyses, all user code is anonymized, and all PII removed.

For any company making creative tools, being able to tell what its most engaged users are building on the platform is critical. When the bound of what's possible to create is effectively limitless, like with code, sophisticated data is needed to answer this deceivingly simple question. For this reason, at Replit we built an infrastructure that leverages some of the richest coding data in the industry.

Replit data is unique. We store over 300 million software repositories, in the same ballpark of the world's largest coding hosts like GitHub and Bitbucket. We have a deeply granular understanding of the timeline of each project, thanks to a protocol called Operational Transformation (OT), and to the execution data logged when developers run their programs. The timeline is also enriched with error stack traces and LSP diagnostic logs, so we have debugging trajectories. On top of all that, we also have data describing the developing environment of each project and deployment data of their production systems.

It's the world's most complete representation of how software is made, at a massive scale, and constitutes a core strategic advantage. Knowing what our users are interested in creating allows us to offer them focused tools that make their lives easier. We can streamline certain frameworks or apps. Knowing, for instance, that many of our users are comfortable with relational databases persuaded us to improve our Postgres offering; knowing that a significant portion of our most advanced users are building API wrappers pushed us to develop Replit ModelFarm . We can also discover untapped potential in certain external integrations with third-party tools, like with LLM providers. It informs our growth and sales strategy while supporting our anti-abuse efforts. And, of course, it allows us to train powerful AI models.