AI/ML
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The Nine Rules of AI PoC Success: How to Build Demos That Actually Ship
Build AI POCs that ship. Use remocal workflows, start small, design for production, track costs, and involve users to move from demo to dependable deployment.
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From Hallucinations to Prompt Injection: Securing AI Workflows at Runtime
Stop LLM mishaps before production. Secure AI agents at runtime with Docker Desktop, Docker Scout, hardened images, and policies against prompt injection.
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Hybrid AI Isn’t the Future — It’s Here (and It Runs in Docker using the Minions protocol)
Learn how to use Docker Compose, Model Runner, and the MinionS protocol to deploy hybrid models.
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Prototyping an AI Tutor with Docker Model Runner
Get inspired to build an AI tutor with this proof of concept project designed to reduce context-switching and improve the dev experience with Model Runner.
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Docker Desktop 4.44: Smarter AI Modeling, Platform Stability, and Streamlined Kubernetes Workflows
In Docker Desktop 4.44, we’re delivering enhanced reliability, tighter AI modeling controls, and simplified tool integrations so you can build with confidence.
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The GPT-5 Launch Broke the AI Internet (And Not in a Good Way)
When GPT-5 launched, AI apps broke overnight. Learn why it happened, and how to build resilient AI systems that survive sudden model and API changes.
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Tooling ≠ Glue: Why changing AI workflows still feels like duct tape
Explore how the fragmented landscape of AI tooling contributes to shaky workflows and how to move toward composable, swappable AI infrastructure.
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Remocal and Minimum Viable Models: Why Right-Sized Models Beat API Overkill
Cut costs, reduce latency, and build faster with right-sized AI. Learn why Remocal and Minimum Viable Models are the future of practical AI development.
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