Press Releases

Bitget Releases January 2026 Proof of Reserves, Demonstrating Strength Through Market Volatility Leave a reply Bitget Releases January 2026 Proof of Reserves, Demonstrating Strength Through Market Volatility VICTORIA, Seychelles, Jan. 28, 2026 (GLOBE NEWSWIRE) — Bitget, the world’s largest Universal Exchange (UEX), has published its January 2026 Proof of Reserves (PoR), reaffirming full backing of user assets during a month marked by heightened market volatility and shifting investor sentiment. The snapshot covers BTC, ETH, USDT, and USDC, with user balances and reserve coverage published on the PoR transparency page alongside wallet attestations and a self-check tool that lets account holders verify inclusion using anonymized identifiers. Despite turbulence across global crypto markets over the past several weeks, Bitget continues to maintain a total reserve ratio well above the 1:1 benchmark, ensuring that all user balances across core assets are fully covered. The January snapshot shows user assets of 14,189 BTC, 179,941 ETH, 1,682,952,107 USDT and 133,804,760 USDC. The reserve ratios are 254% for BTC, 100% for USDT, 161% for ETH, and 113% for USDC, each marked well-above sufficient reserves, with an average reserve ratio of 163%. “Transparency matters most when markets are unsettled,” said Gracy Chen, CEO of Bitget. “January tested the industry with volatility and fast-moving sentiment. What stands out is that Bitget remained fully backed while users continued to engage, rebalance, and grow their holdings. Proof of Reserves is not a marketing moment, it is our operational standard that has to hold up when conditions are unpredictable.” Even as markets reacted to macro uncertainty and rapid price swings, Bitget’s reserve structure remained resilient, ensuring uninterrupted access and asset security for its global user base. Bitget’s Proof of Reserves operates alongside its Protection Fund and monthly disclosures as part of a broader security framework. Through Merkle root verification, users can confirm their individual balances without

WeRide GENESIS Unites Physical and Generative AI to Redefine Autonomous Driving Simulation Leave a reply WeRide GENESIS WeRide GENESIS, WeRide’s proprietary general-purpose simulation model, links the real and virtual worlds to accelerate the development, training, and validation of AVs at scale GUANGZHOU, China, Jan. 27, 2026 (GLOBE NEWSWIRE) — WeRide (NASDAQ: WRD, HKEX: 0800), a global leader in autonomous driving technology, today announced the launch of WeRide GENESIS1, its proprietary general-purpose simulation model. WeRide GENESIS bridges physical AI and generative AI, connecting the real and simulated worlds to accelerate the development, training, and validation of autonomous vehicles (AVs) at scale. Leveraging generative AI, WeRide GENESIS can rapidly generate highly realistic virtual worlds, building simulated cities within minutes and accurately reproducing rare, extreme real-world driving scenarios that are difficult to capture on actual roads. This enables AVs to undergo large-scale training and validation in simulation. Insights generated are continuously fed back into real-world operations – significantly enhancing AV performance in complex scenarios, speeding up iteration cycles, and reducing time and cost compared to traditional road testing. WeRide GENESIS was developed to address the challenges of commercializing autonomous driving on a global scale. Cities differ greatly in road networks, traffic behaviors, infrastructure, weather conditions, and regulatory environments, placing greater demands on the generalization capabilities of autonomous driving technology. However, real-world testing is costly, slow, and limited in scenario coverage, making large-scale validation across multiple cities and conditions extremely difficult. A high-fidelity simulation platform capable of fully replicating real-world environments has therefore become indispensable. Such simulation platforms allow AVs to “drive” in virtual cities, exposing AI drivers to diverse road, weather, and traffic scenarios – including rare and high-risk events. This overcomes the cost and coverage constraints of real-world testing, improves training efficiency, and ensures AVs are safer and more reliable on real roads. As one of the