Docs: Can we add FlagGems under the session of "Triton Kernels / Examples"

[lindylin1817]

Issue Description:

Dear GPU MODE team,

I'm writing to suggest adding FlagGems to the "Triton Kernels / Examples" section in your README. Thanks so much for your consideration!

About FlagGems:

FlagGems is a high-performance general operator library implemented in Triton language, designed to provide a comprehensive suite of kernel functions to accelerate large model training and inference. We have developed more than 230 Triton operators with kernels which have been well tested on Nvidia and other AI chips. https://github.com/flagos-ai/FlagGems/. It has become the Pytorch ecosystem project in 2025 https://landscape.pytorch.org/?item=optimizations--compilers-runtimes--flaggems. It offers:

• A wide range of optimized Triton kernels for common ML operations
• Focus on LLM acceleration with performance optimizations
• Clean, well-documented Triton implementations that serve as excellent learning references
• Active development and community support

Why Include FlagGems:

Given GPU MODE's mission to make GPU programming more accessible and your excellent collection of Triton resources, FlagGems would be a valuable addition because:

• It provides real-world, production-quality Triton kernel implementations that complement your existing examples
• Practical Applications: The library focuses on operations commonly used in modern LLM workloads. It has fully covered Triton operators required by DeepSeek, Qwen2/3, GLM, etc. And it has been used to support lots of language models, multimodal models, and VLA, etc.
• Like GPU MODE, FlagGems aims to democratize high-performance GPU computing
• It would expand the range of Triton examples available to your community

Proposed Addition:

Could you consider adding a link to FlagGems in your README's "Triton Kernels / Examples" section? Something like:

• FlagGems - High-performance general operator library in Triton for large model acceleration
  This would help your community discover another valuable resource for learning Triton and accessing production-ready kernels.
  Thank you for maintaining such an excellent resource for the GPU programming community!

Best regards,

Yonghua Lin