SVT-AV1-PSY is an AV1 software video encoder born from the SVT-AV1 project. Our encoder is designed for optimal perceptual fidelity; we have optimized heavily around pleasing the human visual system, and we aim to provide the best perceptual fidelity available in any AV1 video encoder across a wide quality spectrum.
As one of the main contributors of this project, I added features that improve quality of life, and encode quality and consistency.
SVT-AV1
SVT-AV1 is an open-source software AV1 encoder that is architected to yield excellent quality-speed-latency tradeoffs on CPU platforms for a wide range of video coding applications.
One of my goals is to incrementally incorporate SVT-AV1-PSY features back into the SVT-AV1 project, so more people can enjoy the benefits of our improvements.
Azure DevOps
At Azure DevOps, I worked with various teams and products. The most notable features I contributed were:
A new Code Review Service, written from the ground up in ASP.NET and T-SQL, by leveraging the in-house framework to ensure scalability and reliability. This to enable our "Version 2" of the Pull Request experience, which supports features like code updates, comment tracking, and rich REST API integration.
Automated test tooling for the initial Git Server implementation, which include a command-line tool that massively uploaded popular open-source repositories of various sizes and complexities.
Real-time monitoring and alerting of critical services, by using Azure Data Explorer to gather telemetry, and an internal monitoring service that triggered alerts on any detected anomalies.
GitHub
At GitHub, I worked for the GitHub Actions team on these things (among others):
Helped refactor Azure Pipelines Agent codebase into the initial version of the Actions Runner, which included renaming and cleaning up legacy components.
Assessed the best Azure database solution for an upcoming "private cloud" product (never saw the light :slightlyfrowningface:)
Leveraged my knowledge from Azure DevOps to develop essential "Live Site Incident" infrastructure. I'm proud of a query that detected anomalies in stored procedure performance (total execution time and number of rows read/written) that managed to capture bad regressions just minutes after deploying database updates.
