Thank you for taking the time to look through the repository. To all those who are calling it to be generated by AI. Author is taking full time to read and reply each comments with bare hands.
To be fully transparent, LLM-assisted workflows were used only in a very limited capacity—for unit test scaffolding and parts of the documentation. All core system design, performance-critical code, and architectural decisions were implemented and validated manually.
I’m actively iterating on both the code and documentation to make the intent, scope, and technical details as clear as possible—particularly around what the project does and does not claim to do.
For additional context, you can review my related research work (currently under peer review):
what do you think you will get out of this? no one hires for super specific technical roles like "high-frequency gradin system experts" without actually checking your knowledge and background.
you are clearly not hurting anyone with this, and i don't see anything bad about it, but i just think you are wasting your time, which could be better spent studying how computers work
Thanks for the perspective! The goal isn’t to get hired immediately for a super-specific role—it’s more about learning and experimenting with ultra-low-latency systems. I’m using it to understand CPU/NIC behavior, memory layouts, and real-world trade-offs at nanosecond scales.
Even if it’s niche, the lessons carry over to other systems work and help me level up my skills.
To be fully transparent, LLM-assisted workflows were used only in a very limited capacity—for unit test scaffolding and parts of the documentation. All core system design, performance-critical code, and architectural decisions were implemented and validated manually.
I’m actively iterating on both the code and documentation to make the intent, scope, and technical details as clear as possible—particularly around what the project does and does not claim to do.
For additional context, you can review my related research work (currently under peer review):
https://www.preprints.org/manuscript/202512.2293
https://www.preprints.org/manuscript/202512.2270
Thanks again for your attention.