MAROKO133 Hot ai: China tests world’s largest power-generating kite to turn upper winds in

📌 MAROKO133 Update ai: China tests world’s largest power-generating kite to turn u

China has successfully completed flight tests of the world’s largest power-generating kite, marking a breakthrough in high-altitude wind energy technology.

The test took place in Alxa Left Banner in the Inner Mongolia Autonomous Region, according to China Media Group (CMG).

The kite, measuring 5,000 square meters (53,800 square feet), is part of China’s first national R&D project focused on harnessing wind energy from high altitudes.

A milestone for China’s clean energy ambitions

Developed by China Energy Engineering Corporation under a key national program, the system completed full in-air deployment and retraction during the test. CMG reported that this success represents a major step toward engineering and commercial use of high-altitude wind power in the country.

At the test site, a helium balloon lifted the massive kite to about 300 meters (984 feet) above ground. Once airborne, the kite unfolded and pulled traction cables connected to a ground-based generator, converting wind energy into electricity.

Unlike traditional turbines that rely on towers, nacelles, and blades, this ladder-type system consists of airborne components, traction cables, and ground equipment. It functions like a large kite, designed to capture stronger and steadier winds found at higher altitudes.

How high-altitude wind systems work

High-altitude wind energy is often called a new frontier in renewable power because of its higher wind speeds, stable flow, and greater energy density. Two main approaches exist worldwide: airborne systems and ground-based systems.

Airborne systems use lightweight turbines mounted on flying platforms, while ground-based systems, such as China’s model, rely on kites or canopies to drive generators on the ground.

During the recent test, the team also evaluated smaller kites measuring 1,200 square meters (12,900 square feet) each. The tests successfully demonstrated deployment, retraction, and stable energy conversion.

“We have completed the first deployment test of the world’s largest [power-generation] kite and successfully finished data collection, providing a scientific basis for the design of the kite assembly, which will lay the foundation for deploying a complete set of equipment and for setting standards,” revealed Cao Lun, chief director of the kite-opening test.

Promising efficiency and environmental benefits

Compared to conventional onshore wind farms, high-altitude kite systems can save up to 95 percent of land area, reduce steel use by 90 percent, and lower the cost of electricity generation by about 30 percent.

A 10-megawatt kite-based system could generate around 20 million kilowatt-hours of electricity per year, enough to power about 10,000 homes.

This method not only cuts costs but also minimizes environmental impact by reducing the need for heavy construction materials and large land areas.

At high altitudes, wind flows are faster and more consistent, making them ideal for continuous power generation. “We will conduct multi-kite flight tests and plan to launch power generation trials at the end of next year,” said Huo Shaolei, senior technical expert at China Power Engineering Consulting Group Limited.

🔗 Sumber: interestingengineering.com

📌 MAROKO133 Update ai: Weibo's new open source AI model VibeThinker-1.5B outp

Another day in late 2025, another impressive result from a Chinese company in open source artificial intelligence.

Chinese social networking company Weibo's AI division recently released its open source VibeThinker-1.5B—a 1.5 billion parameter large language model (LLM) that is a fine-tuned variant of rival Chinese tech firm Alibaba's Qwen2.5-Math-1.5B.

It's available now for free download and usage by researchers and enterprise developers—even for commercial purposes—under a permissive MIT License on Hugging Face, GitHub and ModelScope, with a technical report on open access science publishing site arxiv.org.

And yet, despite its compact size, VibeThinker-1.5B achieves benchmark-topping reasoning performance on math and code tasks, rivaling or surpassing models hundreds of times its size, even outperforming Chinese rival DeepSeek's famed R1 that went viral at the start of this year—a 671-billion parameter model—on formal reasoning benchmark.

It further eclipses Mistral AI's Magistral Medium and holds its own against Anthropic's Claude Opus 4 and OpenAI's gpt-oss-20B Medium, all while requiring a fraction of the infrastructure and investment.

It also does so having been post-trained on a budget of merely $7800 USD for compute resources (3900 GPU hours on Nvidia H800s) — far less than the tens, or even hundreds, of thousands of dollars typically required to fine-tune models of similar or larger scale.

Recall this is not the total cost of the model's development, however: LLMs are trained in stages. First comes pre-training, when the model learns basic language structure and general knowledge by predicting the next word across enormous amounts of text from the internet, books, and articles. This gives it fluency but not much sense of how to follow instructions or hold a conversation

Post-training comes next, using much smaller, higher-quality datasets—typically collections of example questions, prompts, and expert-written answers—to teach the model how to respond helpfully, reason through problems, and align with human expectations. Still, Weibo's post-training cost effectiveness on VibeThinker-1.5B is noteworthy and should be commended.

The open-source release upends assumptions about parameter scale, compute intensity, and the minimum viable size for high-performance LLMs.

A Different Training Approach: Spectrum-to-Signal

VibeThinker-1.5B owes its performance not to scale, but to the training framework behind it: the Spectrum-to-Signal Principle (SSP).

Instead of optimizing a model purely for single-answer correctness (Pass@1), the SSP framework decouples supervised fine-tuning (SFT) and reinforcement learning (RL) into two distinct phases with different goals:

• SFT (“Spectrum Phase”): The model is trained to maximize diversity across potential correct answers, improving its Pass@K score. This builds a wide range of plausible solution paths.

• RL (“Signal Phase”): A second-stage reinforcement learning system (called MaxEnt-Guided Policy Optimization, or MGPO) is used to identify and amplify the most correct paths from this diverse solution pool. MGPO prioritizes problems where the model is most uncertain, using entropy-based weighting to focus learning.

The authors argue this separation allows small models to explore reasoning space more effectively—achieving signal amplification without relying on massive parameter counts.

VibeThinker-1.5B makes a compelling case that the industry’s reliance on parameter scaling as the only route to better reasoning performance may be outdated.

By adopting a diversity-first training pipeline, WeiboAI has shown that smaller, more accessible models can match and even outperform billion-dollar systems in logic-heavy tasks.

The low resource footprint is among the most significant aspects of VibeThinker-1.5B. At under $8,000, the post-training cost is 30–60x lower than models like DeepSeek R1 and MiniMax-M1, which cost between $294K and $535K to train.

Performance Across Domains

Despite its small size, VibeThinker-1.5B delivers cross-domain reasoning that outpaces many larger open-source and commercial models:

VibeThinker was benchmarked against both reasoning-centric models (Magistral, Claude, OpenAI o3-mini) and non-reasoning LLMs (GPT-4.1, Kimi K2, DeepSeek V3). Across structured reasoning benchmarks, the model consistently outperformed non-reasoning models, regardless of size:

• On AIME24 (math), it beat Kimi K2 (1.09T) by over 10 points (80.3 vs. 69.6).

• On LiveCodeBench v6, it surpassed Claude Opus 4 (51.1 vs. 47.4).

• On GPQA, it scored below GPT-4.1 and Claude, but still doubled its base model (from 16.4 to 46.7).

This supports the authors’ claim that size is not the only path to reasoning capability—with proper training design, smaller models can reach or even exceed the performance of far larger systems in targeted tasks.

Notably, it achieves parity with models hundreds of times larger on math and code, though it lags behind in general knowledge reasoning (GPQA), where larger models maintain an edge.

This suggests a potential specialization trade-off: while VibeThinker excels at structured logical tasks, it has less capacity for wide-ranging encyclopedic recall, a known limitation of smaller architectures.

Guidance for Enterprise Adoption

The release includes recommended inference settings (temperature = 0.6, top_p = 0.95, max tokens = 40960).

The model is small enough to be deployed on edge devices, including mobile phones and vehicle-embedded systems, while inference costs are estimated to be 20–70x cheaper than with large models.

This positions VibeThinker-1.5B not just as a research achievement, but as a potential foundation for cost-efficient, locally deployable reasoning systems.

Weibo’s Strategy and Market Position

Weibo, launched by Sina Corporation in 2009, remains a cornerstone of China’s social media ecosystem. Often described as China’s version of X (formerly Twitter), the platform blends microblogging, multimedia content, and trending-topic features with a regulatory environment shaped by tight government oversight.

Despite counting 600 million monthly active users (more than twice that of X), investors are not optimistic about its advertising revenue growth potential in the near term, and Weibo is navigating intensifying competition from video-first platforms like Douyin, which are drawing younger users and increasin…

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🔗 Sumber: venturebeat.com