Think Different

                                                                  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 Almost all companies today rely on LinkedIn to extract candidate profiles during hiring or onboarding. However, despite widespread use, even large enterprises frequently fail to extract complete and accurate profile data. The result is broken or partial imports, dozens of mismatches and formatting errors, and missing sections like certifications, experience, or education. This often forces candidates to manually re-enter or correct the information—costing them time, creating frustration, and negatively impacting their experience. To read LinkedIn profile details (including licenses and certifications ) after authorization, follow this short and structured approach: ✅ Prerequisites LinkedIn Developer Account A reg...

LLMs Are a New Kind of Software We are going to present a comparative view of Traditional Software vs LLMs (Large Language Models) to highlight how LLMs represent a fundamental shift in the software paradigm. Comparison Table Traditional Software LLMs (Large Language Models) Deterministic Non-deterministic Fast Slow Cheap Expensive Rigid Flexible If → then Reasoning Detailed Explanation 1. Deterministic vs Non-deterministic Traditional Software follows a fixed, rule-based approach. Given the same input, it always produces the same output. LLMs are probabilistic. Their outputs can vary for the same input because they are based on learned patterns from data rather than hard-coded rules. 2. Fast vs Slow Traditional programs execute instructions quickly because they’re optimized and compiled to run directly on machines. LLMs involve complex computations (e.g., matrix multiplications in neural networks), often requiring GPUs or TPUs, w...

Let's dive deeper into Git commands, especially those that are more advanced and relate to your workflow. Understanding Your Workflow Your provided commands outline a common workflow: Feature Branch Development: You're working on a feature branch ( SMGF2201-7370 ). Staying Updated: You're fetching and integrating changes from the team's development branch ( develop_mlops_deployment ). Reviewing Changes: You're using git log -p , gitk , and git show to examine differences and commit details. Tagging: You're using git tag to mark specific commits. Advanced Git Commands and Concepts Here's a breakdown of related and advanced commands, organized for clarity: 1. Branching and Merging (Beyond Basic Pulls) git rebase : Purpose: Integrates changes from one branch into another by reapplying commits on top of the target branch. This creates a cleaner, linear history. Usage: Bash git checkout SMGF2201-7370 git rebase origin/ develop_mlops_deployment ...