Google Unveils Gemini 3.5: A Leap in AI Capabilities for Agents and Coding Google DeepMind has launched Gemini 3.5, a new family of AI models designed to enhance complex workflows and coding tasks. The release includes 3.5 Flash, a model optimized for speed and performance, and 3.5 Pro, which is already being tested internally and will be rolled out next month. The announcement highlights advancements in agentic intelligence, multimodal understanding, and real-world applications across industries. Gemini 3.5 Flash is now available globally through the Gemini app, Google Search’s AI Mode, and developer platforms like Google Antigravity and the Gemini API. It is positioned as a powerful tool for developers and enterprises, offering capabilities that rival large flagship models in performance while maintaining exceptional speed. The model excels in coding, long-horizon tasks, and multimodal reasoning, achieving notable benchmarks such as 76.2% on Terminal-Bench 2.1, 1656 Elo on GDPval-AA, and 83.6% on MCP Atlas. Its output token speed is four times faster than other frontier models, balancing quality and latency. The release emphasizes the model’s ability to handle complex, real-world problems. For instance, 3.5 Flash can automate tasks like renaming and categorizing unstructured assets, synthesizing academic papers into playable games, and transforming legacy codebases into modern frameworks like Next.js. It also supports collaborative subagents through the Antigravity platform, enabling scalable solutions for tasks such as financial document preparation, data analysis, and application development. Industry partners are already leveraging Gemini 3.5 Flash for transformative applications.#google #google_deepmind #shopify #gemini_35 #google_antigravity
Fashion Retailers Embrace AI Virtual Try-On Technology to Cut Returns Fashion retailers are increasingly integrating AI-powered virtual try-on tools into their digital strategies to address the persistent issue of high product return rates, according to a report by CNBC published on April 5, 2026. The shift is driven by the need to reduce the financial burden of returns, which have long been a significant challenge for the industry. Startups such as Catches, along with major platforms like Shopify and Google, are leveraging generative AI and advanced fabric physics simulations to create realistic virtual fitting experiences for customers. These tools aim to bridge the gap between online shopping and in-store try-ons by allowing shoppers to visualize how garments would fit and look on their bodies without physically purchasing the item. The National Retail Federation (NRF) reported that in 2025, approximately 15.8% of retail sales—amounting to $849.9 billion—were attributed to returns. This figure highlights the scale of the problem, as returns not only incur direct costs for retailers but also strain supply chains and logistics operations. By offering virtual try-on capabilities, retailers hope to mitigate these issues by increasing customer confidence in their purchases. The technology simulates how clothing items would drape, stretch, and move on a virtual avatar, taking into account factors such as body shape, posture, and fabric properties. This level of realism is achieved through generative AI models trained on vast datasets of human body measurements and material behaviors. The adoption of these tools is part of a broader trend toward digital innovation in retail.#national_retail_federation #cnbc #shopify #fashion_retailers #ai_virtual_try_on
Retailers Bet on AI Fitting Rooms to Slash Costly Returns Online shopping has revolutionized the way consumers buy clothing, offering convenience and ease of return. However, for retailers, the process of managing returns has become a significant financial burden. Returns require retailers to bear the costs of retrieving garments that do not fit, inspecting them, and deciding their fate, often at a loss. To address this challenge, virtual try-ons powered by generative artificial intelligence are emerging as a promising solution. According to the U.S. National Retail Federation, 15.8% of annual retail sales were returned in 2025, totaling $849.9 billion, as reported by CNBC. For online sales, the return rate climbed to 19.3%, with Gen Z shoppers aged 18 to 30 averaging nearly eight online returns per person last year, according to the same NRF data. This trend highlights the growing problem of fit uncertainty, which drives both returns and abandoned shopping carts. Ed Voyce, founder and CEO of AI startup Catches, told CNBC that uncertainty over fit is the primary driver of these issues. Most returned items rarely make it back to shelves, and the cost of processing them often exceeds the value of the refund. The NRF data further reveals that 82% of consumers consider free returns essential, yet the financial strain of providing them is becoming unsustainable for many brands. Simeon Siegel, senior managing director at Guggenheim, emphasized that proactively minimizing returns can significantly boost profitability. Siegel noted that AI-generated visuals, now deliverable in the cloud at low costs, offer brands a viable return on investment. Retailers are increasingly integrating generative AI virtual try-on technology into their platforms.#google #shopify #zara #asos #nrf
