Sergio Prada is a Co-Founder and CTO in New York with nine years of experience building backend and serverless systems. He combines startup leadership at Metal (and YC founder training) with hands-on engineering roles at Meta, Datadog, and Kustomer focused on observability and customer-platform infrastructure. An active open-source contributor, he implemented a Motorhead-backed memory adapter for LangChainJS and improved Datadog’s serverless log forwarder, showing strength integrating LLM context, external retrievers, and production-grade testing. A self-described hackathon aficionado, he pairs rapid prototyping instincts with disciplined DevOps and backend engineering to ship scalable, reliable services.
Repo of AWS Lambda and Azure Functions functions that process streams and send data to Datadog
Role in this project:
Backend & DevOps Engineer
Contributions:6 releases, 13 reviews, 17 commits in 4 months
Contributions summary:Sergio primarily contributed to the Datadog serverless functions repository by implementing enhancements to the AWS log forwarder and integration tests. They introduced the ability to forward logs to additional target lambdas and added associated integration tests to validate this functionality. Furthermore, the user updated the versioning and addressed an issue related to stripping digits from tag values, along with various bug fixes and enhancements. They also contributed to infrastructure-related tasks by modifying and testing the integration tests.
Contributions:1 review, 5 PRs, 5 comments in 3 months
Contributions summary:Sergio primarily contributed to the development of a memory adapter utilizing the Motorhead service within the LangchainJS framework. Their work involved implementing the `MotorheadMemory` class, integrating with the Motorhead API, and incorporating functionalities for saving and loading chat history. Additionally, they added comprehensive tests to ensure the adapter's functionality and compatibility. The commits also showcase the user's involvement in the integration with a Metal retriever, indicating the user's experience with external data sources.
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