Ransomware Detection: Why Storage Finds the Attack First
Storage systems can detect ransomware early by monitoring data for anomalies like high entropy or sudden loss of compressibility, acting as the earliest line of defense.
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Why AI Makes the Humanities More Important Than Ever AI systems operate on statistical pattern prediction (syntactic), lacking true semantic understanding of intent or value. The human must remain the critical interpreter to ensure outputs are ethically grounded.Build context
Understanding AI Agent Hallucination in AI Systems Hallucination is when an AI system confidently gives information that's totally incorrect. As agents take action, grounding them with tools and defining scope are critical design choices. What Is Context Engineering? Why It Matters for AI Agents Context Engineering is the critical practice of structuring information passed to LLMs and AI agents. Learn how optimizing context, rather than just writing better prompts, improves reliability and reduces hallucinations in complex AI systems. 5 Ways to Connect AI Agents to Tools: From APIs to MCP Grant Miller breaks down five proven patterns for integrating AI agents with tools, detailing the progression from simple APIs to secure token-based architectures using OAuth flows, MCP, and vaults.Recent signals
Ransomware Detection: Why Storage Finds the Attack First Storage systems can detect ransomware early by monitoring data for anomalies like high entropy or sudden loss of compressibility, acting as the earliest line of defense. What Is MLflow? Tracing AI Agents & LLM Workflows MLflow provides deep observability for multi-agent systems, moving beyond simple HTTP monitoring to trace internal LLM calls, tool usage, and complex failures like non-determinism. Learn how to use LLM judges and integrate tracing into CI. How Developers Secure AI-Generated Code: 5 Security Best Practices AI accelerates code generation, but security requires a 'shift-left' approach. Learn five key principles for securing AI-assisted development, focusing on outcome validation and dependency integrity.
What Is MLflow? Tracing AI Agents & LLM Workflows
MLflow provides deep observability for multi-agent systems, moving beyond simple HTTP monitoring to trace internal LLM calls, tool usage, and complex failures like non-determinism. Learn how to use LLM judges and integrate tracing into CI.
Read article
How Developers Secure AI-Generated Code: 5 Security Best Practices
AI accelerates code generation, but security requires a 'shift-left' approach. Learn five key principles for securing AI-assisted development, focusing on outcome validation and dependency integrity.
Read article
What Is Digital Sovereignty? AI, Data & Control Explained
Digital Sovereignty is the ability to maintain control over your digital systems, encompassing data, operations, technology, and AI. It requires answering: who controls the data, operations, technology, and AI itself?
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Why Does AI Need Access to the Web?
In order to get reliable, uninterrupted access to the public web data on the internet, the knowledge layer needs to get the information from specialized tools. This is where the web data infrastructure layer comes into play.
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5 Ways to Connect AI Agents to Tools: From APIs to MCP
Grant Miller breaks down five proven patterns for integrating AI agents with tools, detailing the progression from simple APIs to secure token-based architectures using OAuth flows, MCP, and vaults.
Read article
What Is Context Engineering? Why It Matters for AI Agents
Context Engineering is the critical practice of structuring information passed to LLMs and AI agents. Learn how optimizing context, rather than just writing better prompts, improves reliability and reduces hallucinations in complex AI systems.
Read article
Understanding AI Agent Hallucination in AI Systems
Hallucination is when an AI system confidently gives information that's totally incorrect. As agents take action, grounding them with tools and defining scope are critical design choices.
Read article
Why AI Makes the Humanities More Important Than Ever
AI systems operate on statistical pattern prediction (syntactic), lacking true semantic understanding of intent or value. The human must remain the critical interpreter to ensure outputs are ethically grounded.
Read article