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​[[{“value”:” Table of contents Introduction What is Tokenization? What is Chunking? The Key Differences That Matter Why This Matters for Real Applications Where You’ll Use Each Approach Current Best Practices (What Actually Works) Summary Introduction When you’re working with AI and natural language processing, you’ll… Read More »Chunking vs. Tokenization: Key Differences in AI Text Processing Michal Sutter Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Table of contents The Problem with “Thinking Longer” The Agentic Approach Infrastructure Challenges and Solutions GRPO-RoC: Learning from High-Quality Examples Training Strategy: From Simple to Complex Breakthrough Results Understanding the Mechanisms Summary The Problem with “Thinking Longer” Large language models have made impressive strides… Read More »Microsoft AI Introduces rStar2-Agent: A 14B Math Reasoning Model Trained with Agentic Reinforcement Learning to Achieve Frontier-Level Performance Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Modern large language models (LLMs) have moved far beyond simple text generation. Many of the most promising real-world applications now require these models to use external tools—like APIs, databases, and software libraries—to solve complex tasks. But how do we truly know if an AI… Read More »Accenture Research Introduce MCP-Bench: A Large-Scale Benchmark that Evaluates LLM Agents in Complex Real-World Tasks via MCP Servers Michal Sutter Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Microsoft AI lab officially launched MAI-Voice-1 and MAI-1-preview, marking a new phase for the company’s artificial intelligence research and development efforts. The announcement explains how Microsoft AI Lab is getting involved in AI research without any third party involvement. MAI-Voice-1 and MAI-1-preview models supports… Read More »Microsoft AI Lab Unveils MAI-Voice-1 and MAI-1-Preview: New In-House Models for Voice AI Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” This post was co-written with Nick Frichette and Vijay George from Datadog.  As organizations increasingly adopt Amazon Bedrock for generative AI applications, protecting against misconfigurations that could lead to data leaks or unauthorized model access becomes critical. The AWS Generative AI Adoption Index, which… Read More »Detect Amazon Bedrock misconfigurations with Datadog Cloud Security Nina Chen Artificial Intelligence

​[[{“value”:” When deploying AI agents to Amazon Bedrock AgentCore Runtime (currently in preview), customers often want to use custom domain names to create a professional and seamless experience. By default, AgentCore Runtime agents use endpoints like https://bedrock-agentcore.{region}.amazonaws.com/runtimes/{EncodedAgentARN}/invocations. In this post, we discuss how to transform… Read More »Set up custom domain names for Amazon Bedrock AgentCore Runtime agents Rahmat Fedayizada Artificial Intelligence

​[[{“value”:” Today, we’re excited to announce that Amazon SageMaker HyperPod now supports managed node automatic scaling with Karpenter, so you can efficiently scale your SageMaker HyperPod clusters to meet your inference and training demands. Real-time inference workloads require automatic scaling to address unpredictable traffic patterns… Read More »Introducing auto scaling on Amazon SageMaker HyperPod Vivek Gangasani Artificial Intelligence