I took this as a sign to get my priorities straight and made a decision to rely on my own tech stack.
Sarvam 105B is optimized for agentic workloads involving tool use, long-horizon reasoning, and environment interaction. This is reflected in strong results on benchmarks designed to approximate real-world workflows. On BrowseComp, the model achieves 49.5, outperforming several competitors on web-search-driven tasks. On Tau2 (avg.), a benchmark measuring long-horizon agentic reasoning and task completion, it achieves 68.3, the highest score among the compared models. These results indicate that the model can effectively plan, retrieve information, and maintain coherent reasoning across extended multi-step interactions.，推荐阅读搜狗输入法下载获取更多信息
arstechnica.com，更多细节参见体育直播
1月13日，广州城市可信数据空间面向社会全面开通互联网访问，旨在打破技术壁垒，降低数据接入门槛，让各类主体平等共享数据要素发展红利。