vue-objccn

project

Use Vue.js to develop a cross-platform full stack application of Objc china.

  • ✅ Desktop application available for three platforms, Mac, Linux and Windows
  • ✅ Web application supports desktop browsers and mobile browsers
  • ✅ Mobile App which uses the Cordova framework, supports iOS, Android, Windows Phone and BlackBerry platforms
  • ❌ Native Mobile App, which can use Weex framework to support both iOS and Android

Note: This project is just a bit of fun and purely for learning purpose, please support 喵神(@onevcat) and Objc china.

The download link for the runnable and complete packaged software is here.

Dezhi Yu
Authors
Senior ML Engineer

I am a research-oriented machine learning systems engineer working on foundation model infrastructure, closed-loop evaluation and optimization systems, and scalable AI platforms. My work focuses on building reliable Model-as-a-Service and Harness-as-a-Service platforms that connect data, training, inference, evaluation, and feedback loops into measurable, continuously improving AI products.

My recent work centers on Model-as-a-Service platforms and high-performance LLM inference. I develop serving infrastructure with vLLM and SGLang across model runtime integration, scheduling and continuous batching, KV-cache and memory management, distributed execution, observability, and reliability. This systems work is closely connected to my research on distributed disaggregated inference, preference optimization, instruction-tuning data selection, multimodal evaluation.

My broader research centers on reinforcement learning infrastructure and reinforcement learning optimization algorithms for scalable AI systems. I am interested in how policy optimization, reward modeling, preference learning, offline RL, simulation environments, distributed rollout systems, and automated evaluation harnesses can be engineered together to improve model behavior. My goal is to build frontier AI systems that learn from feedback efficiently, evaluate progress rigorously, and remain dependable when deployed at scale.