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All digests tagged Containerization
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In the AI era, exploits now beat patches. Learn how to secure your software supply chain against advanced threats targeting CI/CD, developer laptops, and zero-day vulnerabilities using SLSA and Sigstore.
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Learn how to build a private, cost-effective AI workflow using an Obsidian vault and Superlinked's open-source inference engine. We cover self-hosting, model routing, and secure document processing in a controlled VPC.
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To ensure AI safety, agents must run in sandboxes that are modeled after the task's intent. Safety comes from limiting tools and context, not from the harness itself.
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Four approaches to agent execution are compared: Docker's microVM sandboxes, Helix ML's centralized compute, Adobe's browser-native agents, and tldraw's canvas collaboration. Where should your AI agent run?
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Learn how to build robust, reproducible RL environments using OpenM and HF sandboxes. We cover the `reset()/step()` contract, integrating TRL, and training sophisticated agents like OpenCode.
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To prevent performance regressions in Managed Deep Agents, use Harbor and `mda evals init` to scaffold robust eval suites. Evals are structured into environments, jobs, and checks, and results are monitored in LangSmith.
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Aspire AppHost simplifies polyglot development by providing a unified orchestration layer for mixed-stack projects. It automates service wiring, manages configuration via environment variables, and offers centralized observability across languages.
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Discover SQL MCP Server: The open-source solution for secure data access. It provides a standardized API layer to connect AI agents and modern apps to your enterprise database, simplifying development and enforcing security.
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Docker introduces sandboxing using MicroVMs to safely run autonomous AI agents locally. This hardware-level isolation prevents rogue agents from accessing sensitive host data, balancing speed and security.
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