Three months after the Chinese AI company DeepSeek shocked global markets with a highly capable reasoning model, another China-linked company made a splash with a capable agentic AI system. Did Manus, released in March 2025, portend Chinese leadership in AI systems that go beyond chatbots to take action on the user’s behalf? Victor Mustar, head of product at Hugging Face described Manus’ capabilities as “mind-blowing, redefining what’s possible.” A journalist’s comparison with ChatGPT DeepResearch found that Manus provided better results, despite speed and stability issues.
Manus had been released by a Singapore-based firm but developed by a startup in Wuhan with backing from the Chinese tech giant Tencent. It wasn’t China’s only foray into the emerging field. The same month, the Beijing-based firm Zhipu AI launched AutoGLM-Rumination, an open-source agentic system the company said achieved “state-of-the-art” scores on benchmarks such as AgentBench. (Zhipu also announced an “international alliance” for autonomous AI models, to include 10 countries associated with the Belt and Road Initiative and from ASEAN.) Earlier in January, Alibaba released the Qwen-Agent framework for building agentic systems with its Qwen models. ByteDance followed with its Coze Studio platform in July. Last month, Tencent open-sourced Youtu-Agent agentic framework, which was reportedly built atop a DeepSeek model.
With so much action this year in Chinese “agentic” AI efforts, it’s worth pausing to ask what Chinese developers mean when they talk about agentic AI. Moreover, what does the proliferation of such systems in China mean for AI safety and governance in the country? — Read More
Daily Archives: October 18, 2025
Stanford RNA 3D Folding: 1st Place Solution
My approach was clear from the outset. Without GPUs, training a model from scratch or fine-tuning was not viable. My early research – drawing on CASP results, literature, and conference talks, including one by host @rhijudas – showed that Template-Based Modeling approaches consistently dominated. Based on this, I committed to TBM from day one and spent the next 90 days refining my method.
Next, I focused on the evaluation metric, since understanding it determines the exploration path. TM-score has two key properties: it is normalized by structure length (so 50nt and 200nt RNAs are compared on the same 0-1 scale), and it is robust to local errors – a small number of misplaced nucleotides does not disproportionately lower the score. This insight allowed me to prioritize getting the overall fold correct over achieving atomic-level precision. — Read More