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AI-native software engineering may be closer than developers think

CIO

Developers unimpressed by the early returns of generative AI for coding take note: Software development is headed toward a new era, when most code will be written by AI agents and reviewed by experienced developers, Gartner predicts. That’s what we call an AI software engineering agent.

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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

As systems scale, conducting thorough AWS Well-Architected Framework Reviews (WAFRs) becomes even more crucial, offering deeper insights and strategic value to help organizations optimize their growing cloud environments. This time efficiency translates to significant cost savings and optimized resource allocation in the review process.

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Digital transformation 2025: What’s in, what’s out

CIO

In 2025, AI will continue driving productivity improvements in coding, content generation, and workflow orchestration, impacting the staffing and skill levels required on agile innovation teams. CIOs must also drive knowledge management, training, and change management programs to help employees adapt to AI-enabled workflows.

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John Snow Labs Releases Generative AI Lab 7.0 to Help Domain Experts Evaluate and Improve LLM Applications and Conduct HCC Coding Reviews

John Snow Labs

New capabilities include no-code features to streamline the process of auditing and tuning AI models. While the Generative AI Lab already exists as a tool for testing, tuning, and deploying state-of-the-art (SOTA) language models, this upgrade enhances the quality of evaluation workflows.

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LLM benchmarking: How to find the right AI model

CIO

These are standardized tests that have been specifically developed to evaluate the performance of language models. They not only test whether a model works, but also how well it performs its tasks. Platforms like Hugging Face or Papers with Code are good places to start. They define the challenges that a model has to overcome.

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GitHub Copilot – Change the Narrative

Xebia

Currently there is a lot of focus on the engineers that can produce code easier and faster using GitHub Copilot. Eventually this path leads to disappointment: either the code does not work as hoped, or there was crucial information missing and the AI took a wrong turn somewhere. Use what works for your application.

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Efficiently train models with large sequence lengths using Amazon SageMaker model parallel

AWS Machine Learning - AI

Across diverse industries—including healthcare, finance, and marketing—organizations are now engaged in pre-training and fine-tuning these increasingly larger LLMs, which often boast billions of parameters and larger input sequence length. This approach reduces memory pressure and enables efficient training of large models.

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