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Practical articles for founders, operators, and technical teams who need software to become predictable again.
Google’s new universal work agent raises a practical question: how should teams define roles, permissions, approvals and measurable outcomes before delegating work?
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NVIDIA’s new agent-safety platform points to a practical architecture: least-privilege runtime controls, independent monitoring and evidence the agent cannot alter.
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AI spend becomes manageable when teams measure cost per accepted outcome—not just tokens. Here is a practical FinOps playbook for product and engineering leaders.
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A mass phishing campaign used invisible Unicode characters to evade text filters. Here is how teams can normalize input without losing evidence or context.
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Anthropic’s new Model Hardware Standard points toward agents that operate lab and factory equipment. Here is the safety architecture teams need before production.
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As AI agents gain access to business systems, shared credentials become a dangerous shortcut. Here is how to make authorization follow the user, tool…
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OpenAI's latest ZDR move makes sensitive AI workloads more practical—but teams still need end-to-end controls for logs, tools, residency, encryption and deletion.
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Agent Plugins 1.0 makes skills and MCP servers portable across compatible AI clients. Here is how teams can gain interoperability without mistaking packaging for…
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A real infrastructure incident shows why AI agents need bounded permissions, hardened sandboxes, complete traces and machine-speed response.
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The UAE's new Cybersecurity Center of Excellence and this week's threat-intelligence signals point to the same conclusion: secure delivery now needs local capability, cloud…
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