WHEE Mobile App Launched by Meitu, Offering AI Art and Image Generation Services


The domestic large model Skywork launches the mobile app version 5.0, supporting iOS and Android. The core highlight of the new version is the implementation of the "multi-agent parallel collaboration" feature. Users can quickly generate structured points, action lists, and mind maps through a single voice note, and simultaneously launch multiple agents to create PPTs, social media copy, podcast scripts, and promotional posters with one click, significantly enhancing the mobile AI productivity experience.
The Nano Banana2 AI image model has achieved a major breakthrough, overcoming the challenges of complex detail reproduction. By simulating the human multi-stage creative process, it enables image generation to move from random output to controllable refinement, thoroughly solving issues with details such as text, time, and lighting, leading the industry into a new phase of precise generation.
Recently, the Qwen VLo multimodal large model was officially released, achieving significant advancements in image content understanding and generation, offering users a brand-new visual creation experience. According to the introduction, Qwen VLo has been comprehensively upgraded based on the advantages of the original Qwen-VL series models. The model not only can accurately understand the "world", but also can perform high-quality re-creation based on understanding, truly achieving the transition from perception to generation. Users can now access Qwen Chat (chat.qwen.ai)
In the recent Super Bowl event, the AI search engine Perplexity adopted a unique marketing strategy by increasing its mobile app downloads through a tweet (now known as X), rather than opting for traditional expensive advertising. Perplexity's CEO Aravind Srinivas released a tweet on Friday introducing an exciting contest that encouraged users to download their app and submit at least five questions during the contest.
At the intersection of science and technology, graphs are increasingly attracting the attention of researchers as important tools for expressing complex relationships. From chemical molecule design to social network analysis, graphs play an indispensable role in numerous fields. However, generating graphics efficiently and flexibly has always been a challenging problem. Recently, research teams from Tufts University, Northeastern University, and Cornell University have collaborated to launch a project called Graph Generative Pre-trained Tran.