Model Context Protocol is becoming one of the most important ideas in AI engineering. Think of MCP as: AI App External Capabilities = Smarter Agent Simple explanation: MCP Client The AI app, IDE, chatbot, or agent that asks for capabilities. Example: Cursor, Claude Desktop, AI coding assistant, internal company bot. MCP Server The standard bridge that exposes capabilities to the AI client. It says: “Here are the tools, resources, and prompts I can provide.” Tools Actions the AI can call. Examples: Search files Query database Run script Fetch API data Create ticket Deploy service Resources Readable context the AI can use. Examples: Docs Files Schemas Logs Dashboards Knowledge base Database metadata Prompts Reusable instructions and workflows. Examples: Code review prompt Incident analysis prompt Data quality check prompt Architecture review prompt Release notes prompt Why MCP matters: One standard for AI integrations Less custom glue code AI clients can discover server capabilities Tools become reusable across agents Internal systems become AI-accessible Easier to scale AI workflows Best analogy: MCP is like USB-C for AI. Before USB-C: Every device needed a different cable. Before MCP: Every AI app needed custom integrations. With MCP: One standard connection pattern. Client talks to Server. Server exposes Tools, Resources, and Prompts. AI becomes useful inside real systems. Real-world use cases: IDE assistant using code tools Support bot reading company docs Data copilot querying databases DevOps agent checking services Research assistant using internal knowledge HR bot answering policy questions Security agent reviewing logs Key takeaway: MCP standardizes how AI clients connect to external capabilities. It makes AI integrations more modular, reusable, and scalable. If RAG gives AI knowledge, MCP gives AI hands. Farrukh Naveed Anjum Follow on X: @farrukh_codes #MCP #AI #AIAgents #LLM #GenerativeAI #SoftwareArchitecture #DevOps #DataEngineering #SoftwareEngineering #AgenticAI

RAG Explained in simple words: AI Your Data = Smarter Answers Most people make RAG sound complex. But the flow is simple: Text to Embeddings Documents become meaning-rich vectors. Vector Database Vectors are stored for fast similarity search. Retrieval AI finds the most relevant chunks. Context Injection Those chunks are added into the prompt. Grounded Response The LLM answers using your real data. Why RAG matters: Reduces hallucinations Works with private/company docs Keeps answers fresh without retraining Powers support bots, HR bots, code assistants, research copilots, and internal search Best analogy: RAG is like giving AI an open-book exam. The model does not need to memorize everything. It first finds the right page, then gives the answer. Key takeaway: RAG helps an LLM answer smarter by retrieving the right information first, then generating a grounded response. Farrukh Naveed Anjum Follow on X: @farrukh_codes #RAG #AI #LLM #GenerativeAI #SoftwareArchitecture #DataEngineering #DevOps #VectorDatabase #AIAgents

🤖 Single Agent vs Multi-Agent Systems Many teams jump into Multi-Agent AI because it sounds more advanced. But that's not always the right choice. 🔹 Single Agent ✅ Simpler Architecture ✅ Lower Cost ✅ Easier Debugging ✅ Faster Responses Perfect for: 💬 Chatbots 👨‍💻 Coding Assistants 📚 Knowledge Bots 🔹 Multi-Agent ✅ Specialized Expertise ✅ Better Scalability ✅ Parallel Execution ✅ Complex Problem Solving Perfect for: 🔬 Research Teams 💻 Software Factories 🏢 Enterprise Workflows 💡 Think of it like this: 👤 Single Agent = Freelancer 👥 Multi-Agent = Specialist Team The best architecture isn't the most complex. It's the simplest one that solves the problem. Which would you choose for your next AI project? #AI #AIAgents #AgenticAI #SoftwareArchitecture #SystemDesign #LLM #GenAI #techglareexclusive

💡 AI Agent Architecture in 10 seconds: 👤 User asks a question 🧠 Agent creates a plan 🔧 Agent selects tools 💾 Agent retrieves memory ⚙️ Agent executes actions 🔄 Agent evaluates results Not done yet? Repeat the loop. The best AI Agents are not the smartest. They're the ones with: ✅ Better tools ✅ Better memory ✅ Better feedback loops Different agents. Same loop. What do you think is the most important component? #AI #AgenticAI #SoftwareEngineering #SoftwareArchitecture #TechTwitter #OpenAI #DataScience #Developers

When you ask an agent to comment on the architecture they often come back with a list of recommendation. As you work down that list you may come to the conclusion that the list doesn't end. They always have more recommendations. If you follow those recommendations for too long you wind up with a vastly over-architected (if that's a word) result that is more like a bunch of dust in the wind than a nicely partitioned structure. (And yes, I looked at the code.)

System Design Interview cheat sheet ↓↓↓

➡️ 𝐌𝐞𝐦𝐨𝐫𝐲 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐢𝐧 𝐋𝐢𝐧𝐮𝐱 – 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 1. Physical Memory (RAM): The actual hardware memory used by the system to store active programs and data for fast access. 2. Virtual Memory: A memory management technique that allows processes to use more memory than the available physical RAM by utilizing disk space. 3. Memory Pages: Fixed-size blocks into which Linux divides memory, making allocation and management more efficient. 4. Page Cache: A portion of memory used to cache frequently accessed files and disk data, improving system performance. 5. Swap Space: A dedicated disk area used when RAM becomes full, allowing inactive memory pages to be temporarily stored on disk. 6. Memory Allocation: The process of assigning memory resources to running applications and system processes as needed. 7. Memory Mapping (mmap): A mechanism that maps files or devices directly into a process's address space for efficient access. 8. Out-Of-Memory (OOM) Killer: A kernel feature that automatically terminates processes when the system runs critically low on memory. 9. Shared Memory: A method that allows multiple processes to access the same memory region for fast inter-process communication. 10. Memory Monitoring Tools: Utilities such as free, vmstat, top, and htop that help administrators monitor and analyze memory usage in Linux. Grab the Linux ebook: codewithdhanian.gumroad.com/…

4 Basic SQL joins Explained with Examples

Backbone of Data Science Strong data science skills are built on solid foundations. Python, SQL, statistics, Git, and reproducible environments form the backbone of effective analysis, collaboration, experimentation, and scalable machine learning workflows.

50 System Design Topics – Simple to Complex Perfect Learning Roadmap for 2026. Save this list. 1. Design a Rate Limiter 2. Design a URL Shortener 3. Design Pastebin 4. Design a Unique ID Generator 5. Design Consistent Hashing 6. Design a Load Balancer 7. Design an API Gateway 8. Design a Basic Key-Value Store 9. Design a Caching System (e.g., LRU Cache) 10. Design a Notification System 11. Design a Typeahead/Autocomplete System 12. Design a Web Crawler 13. Design a Message Queue 14. Design a 1:1 Chat System 15. Design a Group Chat System 16. Design a News Feed System 17. Design a Proximity Service (e.g., nearby friends) 18. Design Instagram (photo/video sharing feed) 19. Design Twitter/X (timeline posts) 20. Design WhatsApp (real-time messaging) 21. Design Dropbox (file storage & sync) 22. Design a Ticket Booking System 23. Design an E-commerce Platform (catalog checkout) 24. Design a Recommendation System 25. Design a Distributed Cache 26. Design Uber (ride-sharing matching) 27. Design Netflix (video streaming platform) 28. Design YouTube (video upload streaming) 29. Design TikTok (short-video platform) 30. Design Facebook-like Social Network News Feed 31. Design Google Docs (real-time collaborative editing) 32. Design a Content Delivery Network (CDN) 33. Design a Search Engine (indexing querying) 34. Design Google Maps (routing location services) 35. Design a Distributed Database 36. Design a Real-time Analytics System 37. Design an Ad Serving & Tracking System 38. Design a Fraud Detection System 39. Design a Stock Trading/Exchange System 40. Design a Distributed Job Scheduler 41. Design Event Sourcing CQRS Architecture 42. Design a Multi-tenant SaaS Platform 43. Design Live Video Streaming at Scale 44. Design a Highly Scalable NoSQL Database 45. Design a Real-time Multiplayer Game Backend 46. Design Machine Learning Model Serving Infrastructure 47. Design a Geo-distributed Low-Latency System 48. Design a Strongly Consistent Global Database 49. Design a High-Frequency Trading Platform 50. Design a Planet-Scale Distributed System (billions of users, multi-region HA) What will you add to this list?

🚨 𝟔 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐋𝐋𝐌𝐬 𝐩𝐨𝐰𝐞𝐫𝐢𝐧𝐠 𝐭𝐨𝐝𝐚𝐲’𝐬 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 1️⃣ 𝐆𝐏𝐓 – 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐏𝐫𝐞-𝐭𝐫𝐚𝐢𝐧𝐞𝐝 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫 (𝑇ℎ𝑒 𝐺𝑒𝑛𝑒𝑟𝑎𝑙𝑖𝑠𝑡) Trained on massive datasets, these autoregressive models are the foundational engines for writing, reasoning, coding, and open-ended conversation. ➜ Highly versatile across diverse domains ➜ Excels at zero-shot and in-context learning ➜ The ultimate foundation for downstream fine-tuning 2️⃣ 𝐌𝐨𝐄 – 𝐌𝐢𝐱𝐭𝐮𝐫𝐞 𝐨𝐟 𝐄𝐱𝐩𝐞𝐫𝐭𝐬 (𝑇ℎ𝑒 𝑆𝑐𝑎𝑙𝑒𝑟) Instead of activating the full neural network, MoE uses sparse routing to send each input only to the most relevant subset of "expert" sub-networks. ➜ Radically higher compute efficiency during inference ➜ Scales seamlessly to trillions of parameters ➜ Achieves deep specialization without sacrificing overall performance 3️⃣ 𝐕𝐋𝐌 – 𝐕𝐢𝐬𝐢𝐨𝐧-𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥 (𝑇ℎ𝑒 𝑀𝑢𝑙𝑡𝑖𝑚𝑜𝑑𝑎𝑙) Combines advanced vision encoders with language models to natively process and reason over spatial data—like images, complex diagrams, and video streams. ➜ Understands deep visual and spatial context ➜ Perfectly aligns pixel data with semantic text ➜ Enables rich multimodal tasks (like visual QA and image-based telemetry) 4️⃣ 𝐋𝐑𝐌 – 𝐋𝐚𝐫𝐠𝐞 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥 (𝑇ℎ𝑒 𝑇ℎ𝑖𝑛𝑘𝑒𝑟) Built for "System 2" thinking. Optimized for multi-step reasoning, logical problem-solving, and planning through explicit verification and self-correction loops. ➜ Elite mathematical and logical planning ➜ Drastically reduced hallucinations through step-by-step verification ➜ Excels at complex, highly constrained problem-solving 5️⃣ 𝐒𝐋𝐌 – 𝐒𝐦𝐚𝐥𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥 (𝑇ℎ𝑒 𝐿𝑖𝑔ℎ𝑡𝑤𝑒𝑖𝑔ℎ𝑡) Compact, highly optimized models engineered specifically for edge devices, offline execution, or highly cost-sensitive environments. ➜ Ultra-low latency and blazing-fast inference ➜ Highly cost-effective to deploy and maintain ➜ Ensures data privacy through strictly on-device processing 6️⃣ 𝐋𝐀𝐌 – 𝐋𝐚𝐫𝐠𝐞 𝐀𝐜𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥 (𝑇ℎ𝑒 𝐷𝑜𝑒𝑟) Designed not just to generate text, but to execute real-world tasks using tools, APIs, and external environments. It operates on a continuous agent loop: 🔄 Plan ➟ Action ➟ Observation ➟ Reflect ➟ Update Memory ➜ Autonomous real-world execution ➜ Native integration with external systems and software ➜ Dynamically adapts to environmental feedback Agents aren’t just chatbots anymore. They see, act, reason, and run anywhere from cloud GPUs to edge devices. 𝐶ℎ𝑜𝑜𝑠𝑖𝑛𝑔 𝑡ℎ𝑒 𝑟𝑖𝑔ℎ𝑡 𝐿𝐿𝑀 𝑡𝑦𝑝𝑒 𝑑𝑖𝑟𝑒𝑐𝑡𝑙𝑦 𝑖𝑚𝑝𝑎𝑐𝑡𝑠 𝑐𝑜𝑠𝑡, 𝑙𝑎𝑡𝑒𝑛𝑐𝑦, 𝑟𝑒𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦, 𝑎𝑛𝑑 𝑟𝑒𝑎𝑙‑𝑤𝑜𝑟𝑙𝑑 𝑐𝑎𝑝𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠. Cc : Author

9 database types explained in one sentence: 1) 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 ↳ Stores structured data in tables with predefined schemas & SQL queries. 2) 𝗞𝗲𝘆-𝗩𝗮𝗹𝘂𝗲 ↳ Stores simple key-value pairs for ultra-fast lookups & caching. 3) 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 ↳ Stores data as JSON-like documents with flexible, nested structures. 4) 𝗪𝗶𝗱𝗲-𝗖𝗼𝗹𝘂𝗺𝗻 ↳ Stores data in flexible column families for large-scale distributed workloads. 5) 𝗧𝗶𝗺𝗲-𝗦𝗲𝗿𝗶𝗲𝘀 ↳ Stores time-stamped data for real-time metrics, logs, events, & telemetry. 6) 𝗚𝗿𝗮𝗽𝗵 ↳ Stores relationships between entities to query connected data efficiently. 7) 𝗩𝗲𝗰𝘁𝗼𝗿 ↳ Stores embeddings to enable similarity search & AI-powered retrieval. 8) 𝗖𝗼𝗹𝘂𝗺𝗻𝗮𝗿 ↳ Stores data by columns instead of rows to optimize analytical queries. 9) 𝗦𝗲𝗮𝗿𝗰𝗵 ↳ Stores indexed text and structured data to enable fast full-text and relevance-based queries. Most modern systems use several of these together. As systems become more real-time and AI-driven, the need for time-series infrastructure has grown significantly. I like using TimescaleDB by Tiger Data because it keeps the simplicity of Postgres while making it much easier to work with large volumes of time-series and real-time data. Try Tiger Data free with my link below. You'll get a $1,000 30-day credit, no credit card required. It takes just a few minutes to get started, and you can use the credit to build and experiment with whatever you want (new accounts only). Try it here (for free) → lucode.co/postgres-time-seri… What else would you add? —— ♻️ Repost to help others learn and grow. 🙏 Thanks to @TigerDatabase for sponsoring this post. ➕ Follow me ( Nikki Siapno ) turn on notifications.

What is Google’s TPU? A TPU (Tensor Processing Unit) is Google’s custom AI chip, designed from scratch for the giant matrix multiplications that modern models live on. GPUs were built for graphics first. TPUs were built for deep learning from day one. At Cloud Next ’26, Google unveiled its 8th generation, and for the first time it ships in two flavors. TPU 8t is built for training, where raw throughput wins. TPU 8i is built for inference, where latency and chip-to-chip speed matter most. Both still share the same Axion CPUs, liquid cooling, and software stack, so code written for one runs on the other. The diagram below is a quick study guide to what’s the same, what’s different, and why, based on our understanding of published Google articles.

Who else gets mad at Cursor ? When it start yield sloppy things and piece of work ? Telling him its 3rd Grade Sloppy developer making basic mistakes... despite giving designs and every instruction ?

5 levels of evolution of AI Agents, explained visually:

Software Developer Roadmap

Type of APIs and Their use cases

Complete Python Roadmap 📘

30 System Design Concepts every developer should know: → APIs → API Gateways → JWT Authentication → Webhooks → REST vs GraphQL → Load Balancing → Proxy vs Reverse Proxy → Scalability → High Availability → Single Point of Failure (SPOF) → CAP Theorem → SQL vs NoSQL → ACID Transactions → Database Indexing → Database Sharding → Consistent Hashing → Change Data Capture (CDC) → Caching → Caching Strategies → Cache Eviction Policies → CDN → Rate Limiting → Message Queues → Bloom Filters → Idempotency → Concurrency vs Parallelism → Long Polling vs WebSockets → Stateful vs Stateless Architecture → Batch vs Stream Processing → Geohashing Master these concepts and system design interviews become much easier. Save this post for later 📌