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Google launched a new experimental app called Doppl on Thursday in the US for iOS and Android platforms, aiming to let users see how different clothes look on them through artificial intelligence technology. The app uses AI to generate virtual images of users wearing clothes, even converting static images into dynamic videos, providing an immersive try-on experience. The core feature of Doppl allows users to upload full-body photos, then import photos or screenshots of clothing to try them on their digital version.

Recently, Google announced that the upcoming Gemini feature will replace Google Assistant on Android devices. According to an internal email obtained by Android Police, the Gemini update will start rolling out on July 7th. This update will allow users to still control phone calls, messages, WhatsApp, and other apps through this AI assistant even when the Gemini app is closed. This change aims to enhance user experience.

Recently, Google officially launched an AI fitting room app called Doppl, aimed at providing users with a new virtual try-on experience. With this app, users can simply upload a full-body photo and then choose their favorite clothes to try on. Whether these clothes come from thrift stores, friends' outfits, or images on social media, they can be easily achieved. The operation process of Doppl is very simple. Users first need to upload their full-body photos to the app. Then, they can upload photos or screenshots of other clothes to...

Welcome to the AIbase [AI Daily Report] section! Spend three minutes a day to learn about the latest AI events, helping you understand AI industry trends and innovative AI product applications. For more AI news, visit: https://www.aibase.com/zh1. Doubao AI Programming Launches Major Upgrade! No-code beginners can easily create their own web pages, with real-time editing that is very convenient! Doubao AI Programming has been upgraded to Application Creation 1.0, featuring visual editing, real-time preview, and multi-version management functions, lowering the barrier to web and application development for beginners.

Recently, an intelligent academic research tool called ScholAI has attracted widespread attention. This innovative tool, based on the Multi-Cloud Platform (MCP), integrates functions such as paper search, analysis, management, CCF ranking query, and semantic query analysis, providing researchers with an efficient and intelligent academic research solution. The AIbase editorial team has compiled the latest information from the web to provide you with a comprehensive overview of ScholAI's unique advantages and future potential. Core Features of ScholAI: One

Recently, a media outlet revealed that the user growth team at ByteDance developed an AI product called 'Tante Fan'. This product uses the large model

On June 18th, the artificial intelligence research institute OpenAI officially open-sourced its customer service agent framework, marking an important step in the company's layout of enterprise-level AI solutions. This framework aims to promote the practical commercial application of intelligent agent systems from laboratory environments to real-world scenarios by providing transparent toolchains and clear implementation examples.

The plan's advancement has triggered dissatisfaction among developers. According to VentureBeat, OpenAI's current API deprecation involves the GPT-4.5 Preview version, which is an intermediary model launched between GPT-4 and GPT-5. Multiple developers have stated that this sudden change has disrupted their development plans, forcing significant adjustments to applications already built based on this API. A developer who wished to remain anonymous commented: We understand the necessity of technological iteration, but the lack of sufficient buffer time in the schedule has brought additional pressure to development teams.

This comprehensive review of the Solar AI development tool examines how it generates complete applications via natural language input, reducing 83% of development time. Explore Solar's core features, use cases, and comparisons with mainstream tools to determine if it can truly deliver on its 'light-speed development' promise.

An in-depth review of the pros and cons of the VIBESEC AI code security scan platform, analyzing how it addresses developers' pain points such as high false positive rates and integration difficulties, and whether it's worth adopting in the development process. Learn about the unique value and user experience of this AI-driven security tool.

This article provides an in-depth analysis of the innovative architecture and technical advantages of the Kimi-Dev-72B open source large model, exploring how it addresses efficiency pain points in software development through its three-stage training paradigm and environment-integrated reinforcement learning mechanism, featuring core functions such as automated issue localization and precise code repair.