Colin Angle, one of the co-founders of the Oomba robotic vacuum, is raising funds for his new home robotics project.

Image Source Note: Image generated by AI, image licensed from service provider Midjourney

Colin Angle, one of the co-founders of the Oomba robotic vacuum, is raising funds for his new home robotics project.

Image Source Note: Image generated by AI, image licensed from service provider Midjourney
Colin Angle, co-founder and former CEO of iRobot, has announced his return to the robotics field by founding a new startup, Familiar Machines & Magic, focused on developing an innovative home health and wellness robot. According to the Boston Globe, this robot may take the form of an animal or 'pet', aiming to provide companionship to family members while assisting in health management. Familiar Machines & Magic
Colin Angle, co-founder of the renowned Roomba vacuum maker iRobot, is raising funds for his new home robotics startup Familiar Machines. According to documents filed with the SEC, Familiar Machines is aiming to raise $30 million and has already secured $15 million from eight investors. Image source note: The image is AI-generated and licensed from service provider Midjo.
Meta Intelligence OS is a startup founded by Bloomberg. It has developed a series of large models based on the open-source model RWKV and aims to become the Android in the era of large models. The RWKV model has superior performance and low cost in inference tasks, thus attracting customers from industries such as finance, law firms, and smart hardware. The business model of Meta Intelligence OS is model customization based on private data and internal AI Agent development. The company hopes to solve the problems of API call latency and data security by deploying large models on terminal devices. Currently, RWKV versions are available on Windows, Mac, and Linux computers, and Android and iOS versions are also in development. Meta Intelligence OS is raising funds and collaborating with chip companies and computing power platforms to create benchmark customers. Luo Xuan said that the decisive battlefield for large models is on hardware, and both terminal devices and the cloud require dedicated chips.
In the field of video analysis, the persistence of objects is an important cue for humans to understand that objects still exist even when completely occluded. However, current object segmentation methods mainly focus on visible (modal) objects, lacking the capability to handle non-modal (visible + invisible) objects. To address this issue, researchers proposed a two-stage method based on diffusion priors, Diffusion-Vas, aimed at improving the effectiveness of non-modal segmentation and content completion in videos, enabling the tracking of specified targets within the video and using diffusion models to complete the occluded parts.
Recently, Meta officially launched an open-source AI virtual try-on framework named Leffa. This framework aims to enhance the user dressing experience by generating new images, allowing users to switch between different clothing and poses based on a reference image. Compared to previous methods, Leffa excels in detail retention and reducing image distortion. The launch of Leffa brings new possibilities for online shopping and virtual try-ons. Users only need to upload a reference image, and the system can generate an entirely new outfit effect based on that image.