Docker Blog
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Guest Contributor Nov 21, 2025
Beyond the Hype: How to Use AI to Actually Increase Your Productivity as a Dev
Adopt a practical AI coding loop—prompt, plan, produce, refine—to ship better code faster. Agentic tools and context management turn AI into real gains.
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Nov 20, 2025
Docker Model Runner Integrates vLLM for High-Throughput Inference
New: vLLM in Docker Model Runner. High-throughput inference for safetensors models with auto engine routing for NVIDIA GPUs using Docker.
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Nov 19, 2025
Why I joined Docker: security at the center of the software supply chain
Mark Lechner, Docker’s CISO, shares his vision for a future where Docker not only powers the software supply chain, but actively safeguards it.
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Nov 19, 2025
6 Must-Have MCP Servers (and How to Use Them)
Discover the best MCP servers for developer productivity, including Context7, Obsidian, and Notion, and how to use them with MCP Toolkit.
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Nov 18, 2025
Docker + Unsloth: Build Custom Models, Faster
Building and Running Custom Models Is Still Hard Running AI models locally is still hard. Even as open-source LLMs grow more capable, actually getting them to run on your machine, with the right dependencies, remains slow, fragile, and inconsistent. There’s two sides to this challenge: Model creation and optimization: making fine-tuning and quantization efficient. Model…
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Docker Captain Nov 18, 2025
Launch a Chat UI Agent with Docker and the Vercel AI SDK
Build and deploy a production-ready Chat UI agent using Docker and the Vercel AI SDK. Start from the Next.js template and launch locally or in production fast.
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Nov 14, 2025
Making the Most of Your Docker Hardened Images Trial – Part 1
This series will help you make the most of your Docker Hardened Images trial. We cover what the docs don’t: the confusion points you may hit, what metrics actually matter, and how to evaluate results easily.
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Guest Contributor Nov 14, 2025
Investigating the Great AI Productivity Divide: Why Are Some Developers 5x Faster?
Some devs report 3–5x AI gains; others slow down. We unpack the data, why results vary across codebases and experience, and how to set realistic expectations.
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