nikhilunni
Joined 12 years ago
Karma
16
Submissions
12
Member since
Mar 15
Recent submissions
Comment · 1 month ago
Comment · 1 month ago
Been working on this same idea for Super Smash Bros. Melee on the GameCube! Also runnable in the browser: https://github.com/nikhilunni/melee
Port is about 80% done, but completely mechanical with AI and an oracle (the Dolphin emulator)
Comment · 8 months ago
Checkout the README on GitHub! It has an example split.
Honestly the WASM part was pretty straightforward, other than some shader performance issues with Burn + CubeCL , but nothing this project can really change.
The hardest part was the weight porting honestly, since I had to perfectly recreate all of the digital signal processing and model, any deviations from the original PyTorch implementation, and you ended up with subtle loss of signal in the outputs. Added that folder `bench/` to step through the two implementations piece by piece to help debug where they were diverging -- and it's still not a perfect replication of the original after all the tuning!
Comment · 5 years ago
This is awesome!
Comment · 6 years ago
Cortex (YC W20, Sequoia) | Full Time | Full Stack SWE | SF Bay Area
https://getcortexapp.com/ Contact: [email protected]
Cortex helps engineering teams understand and improve their services. By aggregating data from tools like Datadog and Okta, we help teams understand their architecture at a glance – everything from ownership to runbooks. Using this data, we enable engineers to build report cards that score services against initiatives and service quality standards, helping teams build high quality software and instill a culture of quality. The tools teams use today (excel sheets and confluence) are cumbersome and rudimentary - we’re on a mission to create a new category of engineering tooling, to solve the organizational complexity of microservices
Comment · 6 years ago
Cortex (YC W20, Sequoia) | Full Time | Full Stack SWE | SF Bay Area
https://getcortexapp.com/ Contact: [email protected]
Cortex helps engineering teams understand and improve their services. By aggregating data from tools like Datadog and Okta, we help teams understand their architecture at a glance – everything from ownership to runbooks. Using this data, we enable engineers to build report cards that score services against initiatives and service quality standards, helping teams build high quality software and instill a culture of quality. The tools teams use today (excel sheets and confluence) are cumbersome and rudimentary - we’re on a mission to create a new category of engineering tooling, to solve the organizational complexity of microservices
Or if there are some features of an AI SRE tool that make it better than Claude + some MCPs, should those be captured in this same benchmark?