“Vibecoding” doesn’t lend itself to easy translation. For now, Chinese speakers call it 氛围编程 fènwéi biānchéng, 氛围 being “atmosphere”/”vibes” and 编程 being coding. This is an awkward expression because 氛围 usually refers to the atmosphere of a space or environment, and doesn’t have the connotation of care-free DIY that “vibe” does in colloquial American English. 氛围编程 sounds nonsensical as a phrase — something like “coding up an atmosphere.”

But we make do, and oftentimes writers simply use the English word. Developers, creatives, and entrepreneurs in China have been creating many interesting coding projects with AI tools over the past year, utilizing not only popular tools by Silicon Valley giants like Cursor and Claude Code, but also domestic models as Chinese AI companies increasingly compete in the coding-agent market.

Tinkering culture has no borders, and companies are cashing in. This is a roundup of reports from Chinese media on how vibecoding is changing the landscape of technology in China. — Read More

This is the third and final blog in a three-part series on China’s open source community’s historical advancements since January 2025’s “DeepSeek Moment.” The first blog on strategic changes and open artifact growth is available here, and the second blog on architectural and hardware shifts is available here.

In this third article, we examine paths and trajectories of prominent Chinese AI organizations, and posit future directions for open source.

For AI researchers and developers contributing to and relying on the open source ecosystem and for policymakers understanding the rapidly changing environment, due to intraorganizational and global community gains, open source is the dominant and popular approach for Chinese AI organizations for the near future. Openly sharing artifacts from models to papers to deployment infrastructure maps to a strategy with the goal of large-scale deployment and integration.  — Read More

Observers often fixate on the most visible layer of China’s tech stack: consumer-facing conveniences like mobile payments, fifteen-minute food delivery, and dockless bikes. These can make for good investments — we regularly cover them at Tech Buzz China — but they are primarily business model innovations, increasingly familiar, and replicable with modest effort. In my opinion, they do not represent China’s true advantages, the ones that resist replication.

What proves far harder to replicate, and far more consequential, is the invisible layer: China’s manufacturing base. This is the part of the ecosystem that actually reshapes global supply chains, yet it remains the part most visitors never see and, in many cases, never think to see. — Read More

On January 23, China’s National University of Defense Technology demonstrated something that’s reshaping how autonomous weapons work: a single operator supervising over 200 drones simultaneously during urban combat exercises. The swarm operated with minimal human input, relying on what the People’s Liberation Army calls “effect-based control,” designed to function even when communication signals are jammed.

The technology didn’t emerge from traditional programming. It came from watching hawks hunt.

Engineers at Beihang University, a military-linked institution, observed how hawks select vulnerable prey and trained defensive drones to replicate that behaviour, according to The Wall Street Journal. In parallel tests, attack drones mimicked pigeons to evade threats. The result: in a five-versus-five combat simulation, the hawk-trained drones eliminated all opponents in 5.3 seconds, according to a patent filed in April 2024. — Read More

Chinese outfit Zhipu AI claims it trained a new model entirely using Huawei hardware, and that it’s the first company to build an advanced model entirely on Chinese hardware.

Zhipu, which styles itself Z.ai and runs a chatbot at that address, offers several models named General Language Model (GLM). On Wednesday the company announced GLM-Image, that it says employs “an independently developed ‘autoregressive + diffusion decoder’ hybrid architecture, which enables the joint generation of image and language models.” represents an important advance on the Nano Banana Pro image-generating AI. — Read More

Starting 2026, most people are aware that a handful of Chinese companies are making strong, open AI models that are applying increasing pressure on the American AI economy.

While many Chinese labs are making models, the adoption metrics are dominated by Qwen (with a little help from DeepSeek). Adoption of the new entrants in the open model scene in 2025, from Z.ai, MiniMax, Kimi Moonshot, and others is actually quite limited. This sets up the position where dethroning Qwen in adoption in 2026 looks impossible overall, but there are areas for opportunity. In fact, the strength of GPT-OSS shows that the U.S. could very well have the smartest open models again in 2026, even if they’re used far less across the ecosystem. — Read More

Recently, studies exemplified by Hyper-Connections (HC) have extended the ubiquitous residual connection paradigm established over the past decade by expanding the residual stream width and diversifying connectivity patterns. While yielding substantial performance gains, this diversification fundamentally compromises the identity mapping property intrinsic to the residual connection, which causes severe training instability and restricted scalability, and additionally incurs notable memory access overhead. To address these challenges, we propose Manifold-Constrained Hyper-Connections (mHC), a general framework that projects the residual connection space of HC onto a specific manifold to restore the identity mapping property, while incorporating rigorous infrastructure optimization to ensure efficiency. Empirical experiments demonstrate that mHC is effective for training at scale, offering tangible performance improvements and superior scalability. We anticipate that mHC, as a flexible and practical extension of HC, will contribute to a deeper understanding of topological architecture design and suggest promising directions for the evolution of foundational models. — Read More

#china-ai

Or: The West banned the machines. China hired the machinists. Sometimes plans just do not go how you planned them. Ironically, I had been writing this article for a month now, and all research pointed at China being way too far behind. Well…

December 2025. Reuters reveals that China completed an operational EUV lithography prototype in a high-security Shenzhen facility. Not through reverse engineering captured ASML machines. Not through some breakthrough in domestic optics manufacturing. Through something far simpler.

They recruited the humans who knew how to build them. — Read More

In this wave of artificial intelligence, it’s easy to be swept up in grand narratives: computing power, models, the scale of parameters, disruption, and replacement. But the changes truly worth documenting often happen out of sight: how AI is used, how it enters daily life, and how it changes the way people work.

Last week, The Intellectual and Doubao jointly launched: “A Story Collection | How Were You ‘Amazed’ by AI This Year?”, not asking “how powerful is AI,” but a more specific question: When AI enters your work and life, what exactly does it change?

…Perhaps what is truly worth documenting is not what AI can do, but how researchers, after its intervention, re-understand their work, judgments, and responsibilities—what tasks can be automated, and what problems must still be decided by humans.

These scattered and specific experiences constitute the first batch of “field notes” of AI entering the scientific field. They may not be complete, but they are sufficiently honest. — Read More