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DeepSeek Expands Open-Source AI Strategy With New Code Release

Chinese AI startup DeepSeek is doubling down on open-source innovation, announcing plans to publicly release five new code repositories next week. In a post on social media platform X, the company described the move as “small but sincere progress” toward greater transparency in AI development.

“These humble building blocks in our online service have been documented, deployed, and battle-tested in production,” the company stated.

DeepSeek made waves last month when it unveiled its open-source R1 reasoning model, a system that rivaled Western AI models in performance but was developed at a fraction of the cost. Unlike many AI firms in China and the U.S. that guard their proprietary models, DeepSeek has positioned itself as a leader in open-source AI.

The company’s elusive founder, Liang Wenfeng, reinforced this philosophy in a rare interview last July, emphasizing that commercialization was not DeepSeek’s primary focus. Instead, he framed open-source development as a cultural movement with strategic advantages.

“Having others follow your innovation gives a great sense of accomplishment,” Liang said. “In fact, open source is more of a cultural behavior than a commercial one, and contributing to it earns us respect.”

The newly released repositories will provide infrastructure support for DeepSeek’s existing open-source models, enhancing their capabilities and accessibility. This follows the company’s Tuesday launch of Native Sparse Attention (NSA), a new algorithm designed to optimize long-context training and inference.

DeepSeek’s influence is growing rapidly. Since last month, its user base has surged, making it China’s most popular chatbot service. As of January 11, the platform had 22.2 million daily active users, surpassing Douban’s 16.95 million, according to Aicpb.com, a Chinese analytics site.

With its latest commitment to transparency and collaboration, DeepSeek continues to challenge the AI industry’s dominant closed-source model, reshaping the future of artificial intelligence on a global scale.

Mirendil Signs $100 Million Google Cloud Deal To Advance Self-Improving AI

AI startup Mirendil has signed a multi-year agreement worth more than $100 million with Google Cloud to secure computing infrastructure for its self-improving AI research.

The partnership reflects growing competition among AI companies to lock in access to high-performance computing, while cloud providers race to attract promising startups developing next-generation AI models.

Backing The Next Stage Of AI Research

Mirendil plans to use Google’s Tensor Processing Units (TPUs), Nvidia GPUs and managed training infrastructure to develop AI systems capable of improving their own performance over time.

Known as recursive self-improvement, the concept focuses on building AI that can refine its knowledge and capabilities with minimal human intervention. The technology is attracting growing interest across the industry, with several startups and leading AI labs exploring similar approaches.

According to co-founder and Chief Executive Behnam Neyshabur, the long-term goal is to develop AI that can automate scientific research and accelerate discoveries in fields such as medicine, biology and materials science.

Compute Capacity Becomes A Strategic Asset

Training increasingly advanced AI models requires enormous computing resources, making long-term infrastructure agreements a critical competitive advantage.

Mirendil said Google’s combination of TPUs and GPUs allows workloads to be matched with the most suitable hardware, improving efficiency while reducing costs for customers.

For Google Cloud, the agreement strengthens its position in the race to provide infrastructure for frontier AI developers, while giving the company exposure to one of the industry’s emerging approaches to next-generation artificial intelligence.

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