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This post was co-written with Daniel Han and Michael Han from Unsloth. Deploying large foundation models (FMs) stored at their original 16-bit floating-point precision (BF16 or FP16) is expensive. They need large GPU instances, driving up serving costs, and slowing down iteration cycles. Quantization addresses this by reducing the numerical precision of a model’s weights […]

When Model Context Protocol (MCP) tools underperform, the cause is rarely the protocol itself but the tool design. Many teams start by exposing an existing API as-is and trusting the agent to figure out the rest. It is a natural way to extend APIs to agentic systems and generative AI coding tools. For straightforward use

As enterprises scale their generative AI workloads, the demand for faster, more observable, and more flexible inference infrastructure continues to grow. Amazon SageMaker HyperPod is rising to meet that challenge with a set of new capabilities designed to streamline how organizations deploy and operate large models in production. Teams can now record inputs and outputs

Enterprises deploying Claude Code and Claude Desktop across development teams need centralized control over access, cost, and policy. At scale, this is hard to manage: each developer needs an individual credential, settings must be distributed manually, and spend is difficult to track or cap. Without a centralized control point, governance is left to whatever tooling

In pharmaceutical research, scientists face a fundamental challenge: accessing and connecting the vast amount of scientific knowledge scattered across disparate systems. From published literature and internal lab notes to genomics databases, critical insights remain trapped in silos, making it difficult for researchers to form comprehensive connections and generate promising hypotheses. This fragmentation slows down the

AI-powered email management can transform how organizations in the public sector handle constituent communications. By implementing intelligent email routing and prioritization systems, organizations can automatically classify and direct incoming messages based on urgency and departmental relevance. This technology is particularly useful in local government settings, where councillors receive diverse communications across multiple service areas. AI

When ecommerce teams need faster time-to-market for AI-powered customer experiences, they face weeks of custom integration work that delays launches and increases security risks. Building and connecting a production-ready AI assistant typically requires custom API code for each client, container infrastructure management, and complex authentication. Amazon Bedrock AgentCore and Mistral AI Studio streamline this process.

When you deploy generative AI agents with Amazon Bedrock AgentCore as production API endpoints, you might want to enforce web application firewall policies, rate limiting, protection against common web threats, or audit controls via AWS WAF. AWS WAF integrates with Elastic Load Balancing Application Load Balancers (ALBs), Amazon CloudFront distributions, and Amazon API Gateway REST

As organizations expand AI adoption across their workforce, IT administrators need a scalable way to manage how AI applications are configured and used on employee devices. These applications include Claude Code, Claude Desktop, and OpenAI Codex. Users, meanwhile, can open approved applications and start working without manual setup. Jamf, trusted by more than 78,000 organizations

If you’ve been managing Amazon Quick legacy Topics alongside your datasets, you know the challenge: two assets that must stay perfectly synchronized, each with its own permissions, lineage, and versioning. Column synonyms drift. Calculated fields diverge. A rename in the dataset breaks the Legacy Topic silently. You can now use Amazon Quick to embed that