وهاب میررکنی از مهندسای ارشد گوگل خبر داده که تیم‌های پردازش گراف با کمک مدل Gemini 4 Argon و ایجنت‌های Antigravity کاری کردن کارستان و کارهای سنگین چند ماهه در حوزه شبکه و کدهای اختصاصی TPU رو توی چند ساعت جمع کردن

Today, we shared how Google’s Graph Mining and Graph Learning teams used Gemini 4 Argon with Teamwork agent in @GoogleAntigravity to solve a different class of internal engineering bottleneck: compressing months of low-level RDMA networking and custom TPU kernel development into hours. Specifically, improvements include accelerated graph algorithms with FarArray (up to 180x faster, 22x less RAM) and custom TPU kernels for Graph Neural Networks that run 8–15x faster than native JAX with massive HBM savings. For me, this update is a full-circle experience: Graph Mining contributed deeply to Gemini Data via deduping and data curation over time, and now Gemini is giving back by scaling and improving Graph Mining library! This is also a close collaboration amongst our Graph Mining and Teamwork teams, together with the Antigravity and Gemini teams. Congratulations! Read the full blog: https://antigravity.google/blog/gemini-4-argon-trillion-edge-graphs-and-tpu-kernels