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Microsoft engineers spent years building AI Systems were now fired and replaced with the same AI Systems Absolutely ironic 😼

As someone learning and building AI/ML systems, understanding LLM security risks is essential.

If you're building anything in AI, these matter a lot. I've explained in simple below. 1. 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰𝘀 Create structured meaning so AI understands context clearly. Instead of raw text, information is organized with relationships and concepts.

Most people learn AI randomly. That’s why they struggle moving from experiments to real production systems later. A strong AI career needs structured depth across fundamentals, systems thinking, modeling, and product execution. Not just model tutor

Real Agentic AI is a multi-layered system, where each layer solves a specific challenge from reasoning to compliance: 1. LLM (Core Reasoning) – Handles language understanding and generation. Alone, not enterprise-ready. 2. RAG (Retrieval Layer) – G

Software engineering is about to split. One side shrinks. The other becomes more valuable than ever. Anthropic CEO Dario Amodei recently said AI may handle most of what engineers do within 6-12 months. That grabbed attention. But the important par

I am building an app by self. i have my designs and context ready. now is coding. can anyone suggest me free AI tools that can code for me using the design, like a functional project? Engineers ?

Data is a precious thing and will last longer than the systems themselves. - Tim Berners-Lee

AI systems will fail if these 2 layers are mixed. I've explained step by step below. 1. 𝗔𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 How modern AI systems manage intelligence safely. Step-by-step: • 𝗜𝗻𝗰𝗼𝗺𝗶𝗻𝗴 𝗣𝗿𝗼𝗺𝗽𝘁: User sends a prompt. • 𝗖𝗮𝗰𝗵𝗲 𝗖𝗵𝗲𝗰

AI systems will fail if these 2 layers are mixed. I've explained step by step below. 1. 𝗔𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 How modern AI systems manage intelligence safely. Step-by-step: • 𝗜𝗻𝗰𝗼𝗺𝗶𝗻𝗴 𝗣𝗿𝗼𝗺𝗽𝘁: User sends a prompt. • 𝗖𝗮𝗰𝗵𝗲 𝗖𝗵𝗲𝗰