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Personal AI assistants are transforming how we manage our daily lives—from handling emails and calendars to automating smart homes. However, as these assistants gain more access to our private data, concerns about privacy, data residency, and long-term costs are at an all-time high. By combining Clawdbot with Docker …
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We recently showed how to pair OpenCode with Docker Model Runner for a privacy-first, cost-effective AI coding setup. Today, we're bringing the same approach to Claude Code, Anthropic's agentic coding tool. This post walks through how to configure Claude Code to use Docker Model Runner, giving you full control over …
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Customizing Docker Hardened Images In Part 1 and Part 2, we established the baseline. You migrated a service to a Docker Hardened Image (DHI), witnessed the vulnerability count drop to zero, and verified the cryptographic signatures and SLSA provenance that make DHI a compliant foundation. But no matter how secure a …
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Verifying Security and Compliance of Docker Hardened Images In Part 1 of this series, we migrated a Node.js service to Docker Hardened Images (DHI) and measured impressive results: 100% vulnerability elimination, 90% package reduction, and 41.5% size decrease. We extracted the SBOM and saw compliance labels for FIPS, …
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This post is a collaboration between Docker and Arm, demonstrating how Docker MCP Toolkit and the Arm MCP Server work together to simplify architecture migrations. Moving workloads from x86 to Arm64 architecture has become increasingly important. Organizations seek to reduce cloud costs and improve performance. AWS …
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Model Context Protocol (MCP) servers are a spec for exposing tools, models, or services to language models through a common interface. Think of them as smart adapters: they sit between a tool and the LLM, speaking a predictable protocol that lets the model interact with things like APIs, databases, and agents without …
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FIPS compliance is a great idea that makes the entire software supply chain safer. But teams adopting FIPS-enabled container images are running into strange errors that can be challenging to debug. What they are learning is that correctness at the base image layer does not guarantee compatibility across the ecosystem. …
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