“In computer science, memory is as precious as it is elusive.” – Barbara Liskov Understanding Model Context Protocol (MCP) Servers: Architecture, Patterns, and Best Practices As large language models (LLMs) have matured, one of the most persistent challenges has been […]
Remember Me? Architecting Memory with Model Context Protocol Servers Read More »
“The difference between amateur and professional use of language models isn’t about knowing the right prompts, but about building systems that transform ad-hoc experimentation into repeatable, measurable operations. Just as DevOps revolutionized software deployment, LLM Ops is creating the infrastructure
LLM Ops: Turning Prompt Chaos into a Fine-Tuned Machine Read More »
“The most effective engineering organizations don’t just build solutions—they cultivate mental models that shape how problems are perceived. When data scientists and engineers collaborate deeply, they create architectural thinking that transforms raw technology into elegant systems. The path from models
From Models to Microservices: How Great Data and Engineering Teams Lead Through Thought Read More »
“When we integrate AI not as an afterthought but as a foundational element of our software development lifecycle, we transform development from a linear process into an adaptive ecosystem. The best engineers today aren’t just building for current requirements—they’re creating
Pipelines and Predictions: Making AI a First-Class Citizen in Your SDLC Read More »
“The conversation between human and machine is not just about asking the right questions, but about establishing a shared context where language becomes the bridge that connects human intent with artificial intelligence. In this dance of prompt and circumstance, we
Prompt and Circumstance: The Art and Science of Teaching Large Language Models Read More »
“In a world drowning in information, the connections between data points are often more valuable than the data points themselves. Graph databases don’t just store facts – they capture relationships, allowing us to discover patterns that would otherwise remain hidden
Linking It All Together: The Power of Graph Databases and Knowledge Graphs Read More »
“The trouble with data is that it can’t tell you what it doesn’t know. The trouble with intuition is that it can tell you things that aren’t so. The wisdom comes from knowing when to trust each.” – Nate Silver
Gut Feel vs. Cold Facts: When to Trust Soft or Hard Data Read More »
“The future of AI isn’t just about making smarter models—it’s about building intelligence that respects boundaries. Decentralized systems allow us to harness collective intelligence while preserving privacy and autonomy. We can build systems that learn from everyone without exposing anyone.”
Decentralized Intelligence: Smarter Models, Safer Data, Zero Trust Issues Read More »
“The battle between open table formats isn’t just technical – it’s about who controls the future of data architecture. Delta Lake, Iceberg, and Hudi each represent different visions of how data should be organized, accessed, and governed. The winner will
The Great Data Lake Smackdown: Delta Lake, Iceberg, and Hudi Compared Read More »
“The challenge isn’t making AI systems that are powerful, but making AI systems that are powerful and aligned with human values and intentions. As foundation models grow in capability, keeping them grounded in truth and human values becomes not just
Grounding AI: Keeping Foundation Models Rooted in Reality Read More »
