Work just for fun π
Tavily AI
tavily-ai
Tavily Search API is a search engine optimized for LLMs and RAG, aimed at efficient, quick and persistent search results
United States of America
THUKEG
THUDM
ChatGLM, GLM-4, CogVLM, CodeGeeX, CogView, ImageReward, CogVideoX | CogDL, GraphMAE, AMiner | Zhipu.ai (Z.ai) & Knowledge Engineering Group (KEG)
FIT Building, Tsinghua University
Keyu Tian
keyu-tian
Master's student @ pku. Incoming Ph.D. student. Self-supervised learning & generative models &
reinforcement learning.
Peking University Lyoko
Austin Silveria
austinsilveria
communication bandwidth/latency.
independent researcher. previously amazon.
Dickson Neoh
dnth
Full stack AI engineer specializing in deploying computer vision models on edge devices for real-time inference.
Kuala Lumpur, Malaysia
Ce Gao
gaocegege
AI Infrastructure / MLSys | Co-founder & CEO @tensorchord | Co-chair @kubeflow | SJTU
@TensorChord
AmadeusChan
Bachelor in Computer Science and Technology at Tsinghua ---> Ph.D. student in Computer Engineering at USC => CS Ph.D. candidate at Purdue
Less Wright
lessw2020
PyTorch Partner Engineer - Meta AI,
Principal Software Engineer - Audere
Software Architect - X10 Wireless
Dev/PM - Microsoft
Meta Seattle, WA USA
Neural Magic
neuralmagic
Neural Magic (Acquired by Red Hat) empowers developers to optimize & deploy LLMs at scale. Our model compression & acceleration enable top performance with vLLM
Boston
Qizhen WENG
qzweng
Ph.D. from HKUST: AI Infra, MLSys, Cloud Computing
Institute of AI Shanghai, China
IPADS-DuVisor
IPADS-DuVisor
This organization is affiliated with IPADS (at SJTU) and is dedicated to maintaining the development of DuVisor, a user-level virtual machine monitor.
China
OpenXLA
openxla
A community-driven, open source ML compiler ecosystem, using the best of XLA & MLIR.
KServe
kserve
Standardized Distributed Generative and Predictive AI Inference Platform for Scalable, Multi-Framework Deployment on Kubernetes
Knative
knative
Kubernetes-based platform to build, deploy, and manage modern serverless workloads
PreviousNext