In the Senegalese political arena, one question deserves to be asked honestly: are we confusing influence and leadership?
A deep dive into the architectural collision between agentic-oriented data models and the legacy relational paradigm.
A deep dive into one of the most seductive anti-patterns in agentic AI, and what actually works instead.
Artificial Intelligence (AI) is everywhere—transforming industries, reshaping business models, and dominating headlines. Yet, despite the billions invested, many organizations report disappointing outcomes from their AI initiatives. The algorithms work. The tools are powerful. But the results? Often underwhelming.
Implementing an Enterprise Architecture (EA) modeling language like ArchiMate in a large, complex organization presents a significant strategic decision: should the language be "diluted" for quicker adoption, or should a gradual implementation respecting its core principles be pursued? This choice profoundly impacts the long-term effectiveness, consistency, and value derived from the EA practice.
In his influential book, "Start With Why," Simon Sinek presents a powerful concept called The Golden Circle. He argues that truly inspiring leaders and organizations think, act, and communicate from the inside out, starting with their fundamental purpose—their "Why." This "Why" is the cause or belief that drives them. While many of us can articulate what we do and how we do it, discovering our underlying "Why" can be a more profound challenge.
A new wave of AI-powered software development, dubbed "vibe coding," is taking the tech world by storm. Developers are increasingly using natural language prompts to generate code, focusing on the creative "vibe" of a project rather than the painstaking details of implementation. While this approach can accelerate prototyping and empower a wider range of creators, it's also creating a significant headache: a growing mountain of undocumented, unmaintainable, and ultimately unstable code. Enter Amazon's newly released Kiro IDE, a tool designed to bring order to the chaos and bridge the gap between rapid-fire "vibe coding" and production-ready software.
For years, we've focused on Prompt Engineering—the art of crafting the perfect words to get a specific output from an AI. But this is like giving a driver turn-by-turn directions for a single trip. A more powerful discipline is now emerging: Context Engineering.
Context Engineering is the science of building the entire GPS network for the AI—complete with real-time data, memory of past journeys, and an understanding of the driver's ultimate destination. It’s the difference between a one-off command and an intelligent, persistent system. As AI evolves from a simple tool into a trusted copilot, mastering context is no longer an option; it's the key to building applications that are not just powerful, but reliable, personalized, and truly revolutionary.