Think Different

                                                          generated by gemini ai I observed that what AI Coding tools do is only mimic a human programmer. Same way: write code • convert to machine language • execute on a computer. And it cuts to the heart of a real limitation in most current AI coding agents. My question is simple: Why does AI still mimic the human “write code → compile → run” cycle instead of directly translating human intent into computer actions? Let me break down why this happens, and where real intelligence might eventually break the pattern. --- 1. Current AI coding agents are pattern-matching machines, not understanding machines Large language models (LLMs) are trained on human-generated data — including billions of lines of code, documentation, and discussions.   What they learn is statistical regularities in ...

                                                                  Generated by Gemini For the last 28 years and more I have been working as Solutions Engineer / Solutions Architect (SE/SA) + Professional Services / Implementation Engineer + Customer-Facing Software Engineer together combined. Various companies I have worked on pre-sales technical scoping & integration design resembled SE/SA work or/and FDSEs inherit full production-grade coding responsibilities and Enterprise deployment & customisation tasks. From understanding requirements from internal domain expert like here in BNY or from pre sales team of a SaaS or AI company. Never thought about what exactly my role or position is. Responsibilities always came first. Despite being a hard-core computer science student with vast fundamental knowledge, including ...

Here are the key lessons from Uber's architectural redesigns for risk management, synthesized from their engineering blogs and public case studies. 🚦 Lesson 1: Orchestrate Risk Across Services, Not Just Within Them The first major lesson came from addressing the "blast radius" problem. In a monorepo architecture, a single bad commit could potentially break thousands of services at once . - The Problem: Traditional safety checks (pre-commit tests, per-service health metrics) were insufficient. If a change passed initial tests but failed in production, automated deployment pipelines could rapidly propagate the failure to hundreds of critical services before anyone noticed . - The Solution: Uber introduced a cross-cutting service deployment orchestration layer. This system acts as a global gatekeeper, coordinating rollouts across all services affected by a single commit . - How It Works:     - Service Tiering: Services are classified into tiers from 0 (most critical, e.g., ...

  meta ai The philosophy of combining Open-Source Software (OSS) like Kubernetes and Docker with proprietary offerings like Azure Cosmos DB, while often pragmatic, presents several potential issues, particularly for Azure users: 1. Vendor Lock-in (especially with proprietary services like Cosmos DB): Dependency on a single vendor: When you adopt a proprietary service like Cosmos DB, you become heavily dependent on Microsoft for its functionality, updates, and support. This makes it challenging and costly to switch to another database or cloud provider if your needs change, if Microsoft alters its pricing or features unfavorably, or if you simply want to leverage a different technology. Proprietary APIs and data formats: Cosmos DB uses its own APIs and internal data structures, which are not directly transferable to other databases. Migrating data and refactoring application code built around these proprietary interfaces can be a massive undertaking, incurring significant time a...