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Everyone wants AI agents. Very few enterprises can actually run them. Here’s the uncomfortable truth: AI agents don’t fail because they’re not smart. They fail because organizations aren’t ready. In demos, agents look cheap. In production, costs e

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

AI relies on robust data management across 7 key components to build effective AI models: 1. sources, 2. ingestion, 3. storage, 4. transformation, 5. analytics, 6. governance and security, and 7. orchestration.

Recently learn how LLM works, maths behind attention layers and transformer, continously trying to keep up with rapid development in AI space, getting so overwhelmed 🥲 Now google came with "Mixture-of-Agents (MoA): A Breakthrough in LLM Performance

Most people studying AI agents never deploy one real system people actually use. Because they stop at prompts. Prompting is practice. Building is different. Production systems require architecture, workflows, evaluation, and real operational think

Most people studying AI agents never deploy one real system people actually use. Because they stop at prompts. Prompting is practice. Building is different. Production systems require architecture, workflows, evaluation, and real operational think

Everyone is building agents, but only a small number truly understand what it takes to run them in production. Here’s the part people usually avoid saying: 𝗠𝗮𝗸𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀 𝗶𝗻 𝗱𝗲𝘃 𝗶𝘀 𝗽𝗿𝗲𝘁𝘁𝘆 𝗲𝗮𝘀𝘆. In development, everything

5 Layers of AI Product Development. I've explained each in short, simple steps below. 1. 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 → Handles everything the user sees and interacts with → Ensures login security and smooth user experience → Stores session co

Yep 2025 is the era of ai agents. We have MCPS that is the Model context protocol in which your AI agent that is a LLM model will have a access to server and that server will have tools like duckduckgo serach YFinance etc this was from antopic ai and