Favorite In this first post in a two-part series, we examine how retailers can implement a virtual try-on to improve customer experience. In part 2, we will further explore real-world applications and benefits of this innovative technology. Every fourth piece of clothing bought online is returned to the retailer, feeding
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Shared by AWS Machine Learning March 4, 2026
Favorite Learn more about Google DeepMind’s Project Genie and how to write prompts to create your own worlds. View Original Source (blog.google/technology/ai/) Here.
Favorite Gemini 3.1 Flash-Lite is our fastest and most cost-efficient Gemini 3 series model yet. View Original Source (blog.google/technology/ai/) Here.
Favorite The Open Source Initiative (OSI) is proud to highlight Google Summer of Code (GSoC) 2026, a global program that continues to strengthen Open Source communities by pairing new contributors with experienced mentors. For more than two decades, GSoC has helped thousands of developers make meaningful, sustained contributions to free
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Shared by voicesofopensource March 3, 2026
Favorite Are you struggling to balance generative AI safety with accuracy, performance, and costs? Many organizations face this challenge when deploying generative AI applications to production. A guardrail that’s too strict blocks legitimate user requests, which frustrates customers. One that’s too lenient exposes your application to harmful content, prompt attacks,
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Shared by AWS Machine Learning March 3, 2026
Favorite Customer service teams face a persistent challenge. Existing chat-based assistants frustrate users with rigid responses, while direct large language model (LLM) implementations lack the structure needed for reliable business operations. When customers need help with order inquiries, cancellations, or status updates, traditional approaches either fail to understand natural language
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Shared by AWS Machine Learning March 3, 2026
Favorite Large language models (LLMs) perform well on general tasks but struggle with specialized work that requires understanding proprietary data, internal processes, and industry-specific terminology. Supervised fine-tuning (SFT) adapts LLMs to these organizational contexts. SFT can be implemented through two distinct methodologies: Parameter-Efficient Fine-Tuning (PEFT), which updates only a subset
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Shared by AWS Machine Learning March 3, 2026
Favorite Modern large language model (LLM) deployments face an escalating cost and performance challenge driven by token count growth. Token count, which is directly related to word count, image size, and other input factors, determines both computational requirements and costs. Longer contexts translate to higher expenses per inference request. This
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Shared by AWS Machine Learning February 27, 2026
Favorite Foundation models deliver impressive out-of-the-box performance for general tasks, but many organizations need models to consume their business knowledge. Model customization helps you bridge the gap between general-purpose AI and your specific business needs when building applications that require domain-specific expertise, enforcing communication styles, optimizing for specialized tasks like
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Shared by AWS Machine Learning February 27, 2026
Favorite There’s a lot of excitement right now about AI enabling mainframe application modernization. Boards are paying attention. CIOs are getting asked for a plan. AI is a genuine accelerator for COBOL modernization but to get results, AI needs additional context that source code alone can’t provide.Here’s what we’ve learned
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Shared by AWS Machine Learning February 27, 2026