🚀 Day 4 of DSA: Mastering Stacks & The LIFO Principle! As I continue my AI Engineer Roadmap, today I focused on a data structure that we interact with every single day without realizing it: The Stack. Whether it's the "Undo" button in your code editor or the "Back" button in your browser, they all rely on the LIFO (Last-In, First-Out) principle of a Stack. 🔍 What I implemented today: I built a custom Stack class in Python using collections.deque. While Python lists can act as stacks, deque is optimized for faster append and pop operations. 1️⃣ Core Stack Operations: • Push: Adding elements to the top. • Pop: Removing the most recently added element. • Peek: Looking at the top element without removing it. • is_empty & size: Essential utility methods for error handling and validation. 2️⃣ Real-World Problem Solving (LeetCode Challenge): • I solved the "Valid Parentheses" problem using my Stack implementation. • The Logic: When we see an opening bracket (, [, {, we push it onto the stack. When we see a closing bracket, we pop and check if it matches the top. This is a classic example of how stacks manage nested structures. 💡 Why this is critical for AI Engineering? In AI development, Stacks are more than just simple lists: • Algorithm Foundation: Stacks are the backbone of Depth-First Search (DFS), which is used in pathfinding and exploring tree structures. • Expression Parsing: Useful in compilers and for evaluating mathematical expressions in neural network computations. • Function Calls: Understanding the "Call Stack" is vital for debugging complex recursive functions in Machine Learning models. Key Insight: Choosing collections.deque over a standard list for stacks is about Efficiency. In high-scale systems, O(1) operations are the gold standard we strive for! ⚡ Documented the implementation and successfully passed multiple LeetCode test cases. Building logic, one layer at a time! 💪 Next Step: Moving towards Queues – The FIFO principle and its role in asynchronous processing! 📥 #Python #DataStructures #Stacks #AIMLEngineer #SoftwareEngineering #LearningInPublic #CodingFundamentals #DSA #LeetCode #BackendDevelopment

Day 1: Building a Strong Foundation for AI Engineering I’ve officially kicked off my journey into the 2.0 Ultimate Data Science & GenAI Bootcamp Today was all about peeling back the layers of our machines to understand how hardware and software work together to power the AI of tomorrow. Here are my top takeaways from Day 1 1. The Hardware "Brain" 🧠 CPU Power: Learned that a 3.0\text{ GHz} CPU performs roughly 3 billion calculations per second—the raw speed needed for complex processing. RAM vs. Storage: RAM is our "short-form" fast memory that holds active apps, while SSDs provide permanent, high-speed storage for our data. 2. The Language of Instruction 🐍 High-Level vs. Low-Level: Computers communicate in binary (0s and 1s), but languages like Python allow us to write code in a way that is human-readable. Interpreter Advantage: Python uses an Interpreter, translating code line-by-line. This gives us the quick feedback and agility needed when building and testing models. 3. Professional Workflow 🛠️ We explored the importance of Virtual Environments to keep projects isolated and avoid version conflicts—a must-have skill for any serious developer. Moving from basic text editors to IDEs like VS Code to streamline writing, debugging, and running advanced code. This bootcamp isn't just about learning to code; it’s about mastering the full stack—from data fundamentals to building autonomous AI Agents. Excited to document this journey as I transition into the world of AI Architecture. Stay tuned! #DataScience #GenerativeAI #Python #AIArchitect #ContinuousLearning #TechJourney #Day1 Krish Naik Monal S.

📚 Day 3 of my AI Learning Journey Today I focused on understanding Pydantic, a Python library designed for data validation and parsing using type annotations. As applications grow more complex—especially in AI systems that interact with APIs, tools, and external data—ensuring that input and output data follow the correct structure becomes essential. Pydantic provides a clean way to define structured data models and enforce validation rules. Key concepts I explored: • Data Validation – Ensuring that incoming data matches expected data types and formats. • Automatic Type Conversion – Converting compatible types automatically • Structured Data Models – Defining clear schemas using BaseModel to manage application data. • Error Handling – Generating clear validation errors when incorrect data is provided. A simple workflow where this becomes useful: User Input → Data Validation (Pydantic) → Application / AI Processing → Structured Output Understanding data validation is a small but important step toward building reliable AI applications and backend systems. I’ve documented my detailed notes and code examples as part of my learning process. #Python #AI #DataValidation #Pydantic #LearningJourney

Why I spent my last weekend "breaking open" AI Code Agents. 😀 We use AI to write code every day, but I wanted to move past the magic box. I wanted to understand the mechanism: How does an LLM actually turn a prompt into a working script, execute it, and fix its own mistakes? As part of my journey through the Generative AI Software Engineering program, I built a small prototype to deconstruct the "brain" of a Coding Agent. What I learned about the design of Code Agents: The "Think-Execute-Observe" Loop: It’s not just one prompt. It’s a cycle. The agent writes a plan, calls a Python tool to execute it, and then "reads" the terminal output to decide the next step. LiteLLM as the Nervous System: I used LiteLLM to handle the communication. It acted as a translator, allowing me to swap between OpenAI models effortlessly while keeping a consistent API. This taught me how critical abstraction is when building agentic workflows. Tool Use is Everything: The "Agent" isn't just the LLM; it's the LLM + a set of strictly defined Python functions it can call. Defining those tools clearly is where the real engineering happens. The Experiment: It started as a small script to see if I could make an agent debug its own ZeroDivisionError. By the end of the day, I had a working prototype that could navigate a small directory and suggest refactors. It’s just an experiment for now, but it completely changed how I view the future of software development. We aren't just writing code anymore; we are designing systems that write code. #GenAI #SoftwareEngineering #Python #LiteLLM #AICoding #BuildInPublic #Coursera #VanderbiltUniversity #AgenticAI

𝐓𝐡𝐞 𝐞𝐫𝐚 𝐨𝐟 𝐜𝐡𝐚𝐭𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐀𝐈 𝐢𝐬 𝐨𝐯𝐞𝐫… 🤖 𝐓𝐡𝐞 𝐞𝐫𝐚 𝐨𝐟 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐢𝐬 𝐡𝐞𝐫𝐞. Most people are still learning prompts. But the real opportunity? Learning how to build systems that act on their own. This roadmap breaks it down step-by-step 👇 ✨ Step 1: Python + LLM fundamentals ✨ Step 2: RAG + Vector databases ✨ Step 3: Agent frameworks (LangChain, AutoGen, etc.) ✨ Step 4: Tool use + orchestration Because agents aren’t just “smarter chatbots.” They: • Plan tasks • Use tools • Access data • Handle workflows • Improve over time That’s a completely different skillset. And it’s where everything is heading. If you’re still only prompting… You’re early but not early enough. Start building. That’s where the leverage is. #Ai #Agents #AiEngineering #MachineLearning #BuildWithAi #AiDevelopment

Attended the “Introduction to RAG” session by Scaler and Rohit Jindal 🤖 Still relying only on LLMs for answers? You might be missing something big… 🚀 Meet RAG (Retrieval-Augmented Generation) — the smarter way to build AI applications. Instead of depending only on pre-trained knowledge, RAG: 🔎 Retrieves relevant data from external sources 🧠 Feeds it into LLMs ✍️ Generates accurate, context-aware responses 💥 Why does this matter? Because LLMs alone can: ❌ Hallucinate ❌ Give outdated answers ❌ Miss domain-specific context ✅ RAG solves this by grounding responses in real data. 🔥 Real-world use cases: • AI chatbots with company knowledge • Smart document search & summarization • Customer support automation • Enterprise AI assistants 💡 If you're working with Python, FastAPI, or AI APIs — learning RAG is a must in 2026. 👉 Curious to build one? Let’s connect or discuss below! #AI #GenerativeAI #RAG #MachineLearning #Python #Scaler #LLM #Tech

📊 Most people think building AI is about models. It’s not. It’s about how well you can work with data, and that’s where Python quietly does all the heavy lifting. Behind every model that works, there’s a layer most people overlook: clean loops, efficient transformations, and readable logic. Things like: ➡️Turning messy data into usable features (list & dict comprehensions) ➡️Combining datasets without friction (zip, enumerate) ➡️Handling edge cases without breaking pipelines (defaultdict, dict.get) ➡️Writing flexible, reusable code (*args, **kwargs, lambda) ➡️Managing memory when data gets large (generators, yield) None of these are “advanced AI topics.” But they’re exactly what make AI systems actually work. Because in reality: AI isn’t just models. * It’s pipelines. * It’s data flow. * It’s structure. And the engineers who understand this build faster, cleaner, and more scalable systems. If you're getting into AI (or already in it), improving your Python fundamentals isn’t optional, it’s leverage. Which of these Python concepts do you actually use daily — and which ones are you still avoiding? Credit: Naresh Edagotti #ArtificialIntelligence #Python #MachineLearning #DataScience #AIEngineering #Programming #TechSkills #SoftwareEngineering #GenAI #LearnInPublic

𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗔𝗜 𝗶𝗻 𝗬𝗼𝗎𝗿 𝗖𝗼𝗱𝗲 You see demos of AI tools that answer questions about code. But what happens behind the scenes? You can build your own AI-powered codebase assistant. This guide shows you how to build a practical assistant. You will learn about the technical architecture that makes it possible: Retrieval-Augmented Generation (RAG). By the end, you will have a working Python prototype that answers questions about a local project. To build this, you need two core functions: - Indexing: creating a searchable map of your code - Querying: finding relevant parts of the map and generating a human-friendly answer You will use open-source models and tools like langchain and Chroma. Your assistant will load code files, split them into chunks, and convert those chunks into numerical vectors. Then, you will retrieve relevant context and generate the final answer using a local LLM. You can customize this for your stack and debug failures. You can control your data and make the AI decide to look up definitions or read documentation. The true power is in the architecture you design. Start experimenting and make it yours. What will you ask your codebase first? Source: https://lnkd.in/gzqpjbTb Optional learning community: https://t.me/GyaanSetuAi

I decided to transition from Data Analytics Engineering into AI Engineering. Here is what I actually built: Week 1: Career agent on HuggingFace Spaces. GPT-4o-mini, Gradio UI, SQLite, push notifications to my phone. Week 2: Deep Research agent. Breaks a question into sub-searches, runs them in parallel, writes a full report, emails it. OpenAI Agents SDK. Week 3: 4-agent Engineering Team with CrewAI. Give it a requirements doc. Get back working code, a deployed UI, and passing unit tests. I wrote zero lines of the output. Week 4: The Sidekick with LangGraph. Opens a real browser, searches Google, writes files, runs Python, and loops with a self-evaluation pattern until it actually meets your criteria not just until it thinks it's done. Week 5: Self-replicating agents with AutoGen. One agent writes Python code for 20 others at runtime. All 20 run in parallel. They randomly pick each other to collaborate. None of that was hardcoded. Week 6: Autonomous trading floor. 4 traders Warren, George, Ray, Cathie each with their own investment strategy. Running every hour. Researching news, checking live prices, making trades, sending me notifications. No human in the loop. I also merged 5 pull requests into the open-source course repo on GitHub. All 5 merged. Including a custom MCP server I built from scratch. What I keep thinking about: The framework doesn't matter as much as people think. What matters is the patterns, tools, handoffs, structured outputs, multi-agent coordination, memory. Once you understand those, every framework is just syntax. If you're building something with agents — I'd love to talk. #AIEngineering #AgenticAI #LangGraph #CrewAI #AutoGen #MCP #OpenAI #Python #LLM #JobSearch Ed Donner