Mitico Pilots Community-Based Livestreaming at World of Dance Hong Kong

On January 22, 2026, World of Dance Hong Kong became the first global event to pilot Mitico’s community-based livestreaming model. The idea is simple: rethink how live competitions are shared in a digital-first world.

Instead of relying on a single official broadcast, the event was produced as one centralised live feed. It was then distributed across multiple creators and influencers, each hosting the stream for their own audience.

This gave creators room to add their own commentary, adapt the language and bring in cultural context that suited their communities, while the production remained consistent behind the scenes.

“Dance is a universal language”, said David Gonzalez, President of World of Dance. “Our collaboration with Mitico to produce an international, creator-led livestream in Hong Kong allowed a regional competition to reach a global audience. With personalised commentary from hosts in different languages, we can begin to see how regional events may connect through global communities”. This approach points to a shift away from traditional broadcaster-led distribution and toward creator-led amplification.

A dance crew performs on stage as the audience watches. PHOTO: WORLD OF DANCE HONG KONG

Mitico’s approach begins with a familiar industry challenge: the high cost of production and licensing, which often makes it difficult to livestream cultural and sports events at scale.

“Many cultural and sports competitions are never livestreamed because traditional broadcasting is too costly and complex”, said Chengcheng Li, Founder of Mitico. “By distributing a centralised production feed through creators and community hosts, regional events can reach global audiences while maintaining a unified production workflow”.

World of Dance (WOD) offered a natural test environment. It started as a global dance competition platform before entering a television partnership with NBC, which later produced four seasons of the World of Dance reality series. While the television programme concluded in 2021, the competition business has continued to expand through an international network of partners. Today, World of Dance competitions are represented in more than 72 countries, producing nearly 100 events each year, with a digital audience of more than 34 million followers across platforms

Despite that scale, many competitions are not livestreamed due to the high production costs and technical demands associated with traditional broadcasting. The Hong Kong event was selected to assess whether a community-led distribution model could offer a more scalable alternative for live coverage.

While no changes to World of Dance’s broader distribution strategy have been announced, the Hong Kong pilot offers an early indication of how global competitions may rethink livestreaming in an increasingly creator-driven media environment.

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Artificial Intelligence

AgiBot Brings Real‐World Reinforcement Learning to Factory Floors

Robots that learn on the job: AgiBot tests reinforcement learning in real-world manufacturing.

Ventureport Media Team

Updated

January 8, 2026 6:34 PM

A humanoid robot works on a factory line, showcasing advanced automation in real-world production. PHOTO: AGIBOT

Shanghai-based robotics firm AgiBot has taken a major step toward bringing artificial intelligence into real manufacturing. The company announced that its Real-World Reinforcement Learning (RW-RL) system has been successfully deployed on a pilot production line run in partnership with Longcheer Technology. It marks one of the first real applications of reinforcement learning in industrial robotics.

The project represents a key shift in factory automation. For years, precision manufacturing has relied on rigid setups: robots that need custom fixtures, intricate programming and long calibration cycles. Even newer systems combining vision and force control often struggle with slow deployment and complex maintenance. AgiBot’s system aims to change that by letting robots learn and adapt on the job, reducing the need for extensive tuning or manual reconfiguration.

The RW-RL setup allows a robot to pick up new tasks within minutes rather than weeks. Once trained, the system can automatically adjust to variations, such as changes in part placement or size tolerance, maintaining steady performance throughout long operations. When production lines switch models or products, only minor hardware tweaks are needed. This flexibility could significantly cut downtime and setup costs in industries where rapid product turnover is common.

The system’s main strengths lie in faster deployment, high adaptability and easier reconfiguration. In practice, robots can be retrained quickly for new tasks without needing new fixtures or tools — a long-standing obstacle in consumer electronics production. The platform also works reliably across different factory layouts, showing potential for broader use in complex or varied manufacturing environments.

Beyond its technical claims, the milestone demonstrates a deeper convergence between algorithmic intelligence and mechanical motion.Instead of being tested only in the lab, AgiBot’s system was tried in real factory settings, showing it can perform reliably outside research conditions.

This progress builds on years of reinforcement learning research, which has gradually pushed AI toward greater stability and real-world usability. AgiBot’s Chief Scientist Dr. Jianlan Luo and his team have been at the forefront of that effort, refining algorithms capable of reliable performance on physical machines. Their work now underpins a production-ready platform that blends adaptive learning with precision motion control — turning what was once a research goal into a working industrial solution.

Looking forward, the two companies plan to extend the approach to other manufacturing areas, including consumer electronics and automotive components. They also aim to develop modular robot systems that can integrate smoothly with existing production setups.