“Data is not truth. Data is a clue. Trust is built when we know where it came from, how it was shaped, and why it matters.” — Monica Rogati Building Trust in Data: From Gut Feelings to Instrumented Confidence For […]
In Data We Trust: Building Confidence from Lineage to Leadership Read More »
“Facts are stubborn things, but statistics are pliable.” — Mark Twain From Curiosity to Clarity: Navigating the Lifecycle of Data Exploration and Analysis In the early days of computing, data was a static asset. It was entered into ledgers, summarized
Numbers Don’t Lie—But They Do Mislead: A Guide to Doing Data Right Read More »
“You don’t drown by falling into water. You drown by staying there.” — Edwin Louis Cole Cloud Without Borders: The Art and Agony of Hybrid Multicloud Architecture In the early days of cloud computing, the vision was pure: break free
Too Many Clouds, Not Enough Umbrellas: Taming Hybrid Multicloud Chaos Read More »
“In AI, the prompt is just the beginning—the real intelligence is in the system around it.” — Anonymous Systems Architect Pattern Recognition: How Emerging AI Design Patterns Are Shaping the Future of Intelligent Platforms In the early days of artificial
From Prompts to Platforms: The Blueprint for Modern AI Systems Read More »
“The next great cloud isn’t one cloud—it’s all clouds, stitched together by intention.” – Anonymous CTO at an AI Infra Summit Neoclouds and the AI Renaissance: Stitching the Future of Cloud Strategy Once upon a time, the cloud was a
Beyond the Big Three: Building Smarter AI Workloads with Neoclouds Read More »
“If you can’t describe what you are doing as a process, you don’t know what you’re doing.” — W. Edwards Deming Pattern Recognition: The Unsung Architecture of Scalable AI, ML, and LLM Systems There’s an adage in software engineering: “Design
From Prototype to Production: Design Patterns That Make AI Actually Work Read More »
“The best way to protect data is not to collect it in the first place.” — Bruce Schneier Gone Without a Trace: The Rise of Zero Data Retention in the Age of AI Introduction: Memory as a Liability Once, data
Stateless by Design: Why Smart Systems Don’t Remember Everything Read More »
“There is no such thing as a one-size-fits-all database. Choosing the right one is less about features and more about understanding your data’s behavior.” — Alex DeBrie Cache Me If You Can: Choosing the Right Database and Optimization Strategy for
The Right Tool for the Write Job: Databases, Caches, and When to Use What Read More »
“Security is always excessive until it’s not.” — Robbie Sinclair Fortifying the Core: A Practical Guide to Hardening Your MCP Server When the Model Context Protocol (MCP) was introduced, it offered an elegant standard for sharing context between AI models,
Trust, But Verify: Hardening Your MCP Server from the Socket Up Read More »
“Trust in AI isn’t just built on what it knows—but on how transparently, fairly, and safely it decides what to do with what it knows.” — Cynthia Dwork Wired for Good: The Rise, Risks, and Responsibilities of AI Ethics The
Terms of (Algorithmic) Endearment: Engineering Trust into Your AI Stack Read More »
