Understanding Big-O Notation: A Guide to Algorithm Efficiency

I Built GridScript: A New Way to Transform Excel Data with JavaScript and Python TL;DR GridScript.io lets you open Excel/CSV files, run JavaScript or Python scripts to transform the data, and export the result — all in your browser. No backend. No install. Instant feedback. Developers constantly bounce between spreadsheets and code when working with data — cleaning CSVs, merging reports, converting formats. I wanted a way to stay inside the browser and use the languages I already know (JavaScript and Python) to transform and visualize data. That idea became Gridscript.io. But it's also more than that, Gridscript offers access to Tensorflow.js and Scikt-learn allowing engineers to directly build AI/ML models. Next.js – fast frontend framework AG Grid – Excel-like grid for editing and filtering Monaco Editor – same editor used in VS Code Client-side architecture – no data leaves your browser Undo/Redo system – safe experimentation Create a New Pipeline Upload an Excel, JSON or CSV file Write a few lines of code in JS or Python Instantly see the grid update Export https://lnkd.in/gU-TAens

I Built GridScript: A New Way to Transform Excel Data with JavaScript and Python TL;DR GridScript.io lets you open Excel/CSV files, run JavaScript or Python scripts to transform the data, and export the result — all in your browser. No backend. No install. Instant feedback. Developers constantly bounce between spreadsheets and code when working with data — cleaning CSVs, merging reports, converting formats. I wanted a way to stay inside the browser and use the languages I already know (JavaScript and Python) to transform and visualize data. That idea became Gridscript.io. But it's also more than that, Gridscript offers access to Tensorflow.js and Scikt-learn allowing engineers to directly build AI/ML models. Next.js – fast frontend framework AG Grid – Excel-like grid for editing and filtering Monaco Editor – same editor used in VS Code Client-side architecture – no data leaves your browser Undo/Redo system – safe experimentation Create a New Pipeline Upload an Excel, JSON or CSV file Write a few lines of code in JS or Python Instantly see the grid update Export https://lnkd.in/gU-TAens

📚 Understanding Time & Space Complexities (The “Big O” Basics) When we write code, we care about two things: 1️⃣ How much time our program takes. 2️⃣ How much memory (space) it uses. Here’s a quick, no-jargon guide to the different types of complexities: ⚡ O(1) – Constant: No matter how big your input is, the time stays the same. Example: Accessing array[0] or checking if a number is even. 📈 O(log n) – Logarithmic: Time increases slowly as inputs grow. Example: Binary Search (it keeps cutting the problem in half). 🚶 O(n) – Linear: Time grows directly with your data size. Example: Looping through an array once to find the largest number. 🧩 O(n log n) – Linearithmic: A bit slower than linear, but still efficient. Example: Merge Sort or Quick Sort. 💥 O(n²) – Quadratic: Gets slower quickly, often due to nested loops. Example: Bubble Sort or Insertion Sort. 🧮 O(n³) – Cubic: Even slower — usually three nested loops. 🧠 O(2ⁿ) – Exponential: Doubles in time with each extra input. Example: Recursive Fibonacci. 😰 O(n!) – Factorial: The slowest! Used in complex tasks like checking all permutations (e.g., Traveling Salesman Problem). The smaller your “Big O,” the faster and more efficient your code is. 🚀 Learning this helps you think like a problem solver — not just a coder! #DSA #BigO #LearningInPublic #Coding #Education #ProblemSolving #FrontendDevelopment #JavaScript #WebDev

🚀 Just wrapped up building an AI-Powered Web Scraper that intelligently extracts exactly what you need from any website. Technologies & Tools Used 💻 Programming Language Python 🐍 – for building the core logic, AI integration, and data handling. ⚙️ Frameworks & Libraries Streamlit – to create an interactive and user-friendly web interface. LangChain – to manage the AI pipeline and natural language understanding. Ollama (Llama 3.1 Model) – to run a local Large Language Model for intelligent parsing. Selenium – for browser automation and scraping dynamic websites. BeautifulSoup – for HTML parsing and cleaning the data. 🧰 Tools & Platforms Git & GitHub – for version control and project sharing. VS Code – as the main development environment. ChromeDriver – for Selenium automation. dotenv – for environment variable management and security. This project combines web automation with local LLMs to create a flexible, powerful scraping tool ,no more wrestling with complex selectors or regex! #AI #WebScraping #Python #LangChain #Ollama #Selenium #Streamlit #OpenSource #BeautifulSoup #Automation

The graphical user interface (GUI) is not always the fastest way to view your data. At least, not anymore. Oftentimes an LLM gives me more quickly what I need by generating a 50 to 150 line Python or SQL script that I quickly inspect and then run. The only prerequisite is that I have the right (API) access. One reason, I think, is GUIs aren’t always that intuitive. They’re complicated and UX has probably been an afterthought. That’s when LLMs win by generating simple data retrieval scripts. In the category of 200$ “how to get data faster?”

𝗧𝗼𝗼𝗹 𝗖𝗮𝗹𝗹𝗶𝗻𝗴 𝘃𝗶𝗮 𝗠𝗖𝗣 vs. implementing it 𝗡𝗮𝘁𝗶𝘃𝗲 𝘁𝗼 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁. What's the difference? Recently there have been many MCP vs. Function Calling articles floating around. Let’s simplify. ❗️ 𝗖𝗹𝗮𝗿𝗶𝗳𝘆𝗶𝗻𝗴 𝗼𝗻𝗲 𝗱𝗲𝘁𝗮𝗶𝗹: Function Calling and Tool Use in Agentic Systems are almost the same thing. You can implement tools via functions, the only difference is that functions are usually used to enforce stricter structure to the input and output schema. 𝘜𝘴𝘪𝘯𝘨 𝘔𝘊𝘗 𝘵𝘰 𝘦𝘹𝘱𝘰𝘴𝘦 𝘵𝘰𝘰𝘭𝘴 𝘵𝘰 𝘺𝘰𝘶𝘳 𝘈𝘨𝘦𝘯𝘵𝘴: ℹ️ In this case your Agent becomes an MCP Host and implements one or more MCP Clients to communicate with MCP Servers. 𝟭. User Query is passed to the Agent (usually a Python application). 𝟮. The application implements MCP Client and via it retrieves all available tools from the MCP servers. 𝟯. The list of available Tools is passed together with the User Query to a LLM via a prompt. The LLM figures out which tools need to be invoked and with what parameters. 𝟰. The Agent application communicates with the MCP server (via MCP Client) again and sends the tool execution request. After execution completes the Agent receives the required data. 𝟱. User Query is sent to the LLM together with the data retrieved by the Tool calls. 𝟲. The answer is constructed and returned to the user via the Agent. 𝘜𝘴𝘪𝘯𝘨 𝘕𝘢𝘵𝘪𝘷𝘦 𝘍𝘶𝘯𝘤𝘵𝘪𝘰𝘯 𝘊𝘢𝘭𝘭𝘪𝘯𝘨: 𝟭. User Query is passed to the Agent (usually a Python application). 𝟮. All of the available Functions/Tools are defined as part of the Agent code (procedural memory). 𝟯. The list of available Tools is passed together with the User Query to a LLM via a prompt. The LLM figures out which functions need to be invoked and with what parameters. 𝟰. The Agent application directly executes the functions. 𝟱. User Query is sent to the LLM together with the data retrieved after function execution. 𝟲. The answer is constructed and returned to the user via the Agent. 𝗠𝘆 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀: ❗️ Will MCP eat up the practice of Native Function Calling? If we adopt the standard then I believe so. ❗️ Will MCP compete with LLM Orchestration frameworks? I believe MCP will replace the Tool use abstractions as they are used today and frameworks will be responsible mostly for managing the topology and state of the Agentic systems long term. Are you using MCP already to expose Tools to your Agents? Let me know in the comments. 👇

Most people underestimate how complex real-world web data extraction actually is. Behind every clean CSV, database or dashboard there’s a workflow handling dynamic pages, anti-bot systems, hidden requests, rendered JavaScript, and multi-step interactions. Choosing the right tools is what makes scraping reliable, scalable, and efficient. In this carousel, I’m sharing 5 of the tools I personally rely on to automate web scraping in real projects the same workflow that helps me extract data faster, deal with protection mechanisms, and process information at scale. If you’re into automation, scraping, and data-driven systems this breakdown will give you clarity on what works today. → Swipe through the slides → Learn the stack → And decide which approach fits your workflow If you like content about web scraping, data automation and real Python workflows. follow me for more. #webscraping #dataextraction #automation #dataengineering #bigdata #scrapingtips #webscraping #python

Ever built a Flask or FastAPI endpoint and found yourself writing the same tedious data validation logic over and over? 🙋♂️ app.route('create/user') def create_user(): data = request.get_json() if not data.get('name'): return {'error': 'Name is required'}, 400 This approach is repetitive, error-prone, and clutters your business logic. I finally got fed up and went looking for a better way. That's when I found Pydantic. It's not the flashiest tool, but it has quietly become my secret weapon for bulletproof data validation in Python. Here's why it's a game-changer: 1. Type Hints as Validation: You define your data structure using Python's type hints, and Pydantic handles the validation automatically. 2. Clean & Readable: Your validation logic moves from messy if statements to clean, declarative class definitions. 3. Powerful & Flexible: It handles complex nested models, custom validators, and integrates seamlessly with FastAPI. It's one of those libraries that makes your Python code more robust, your development faster, and eliminates entire categories of bugs before they happen. What's your "non-glamorous" Python tool that you can't live without? I'm always looking for new ones to add to the toolkit. #Python #WebDevelopment #FastAPI #Flask #Pydantic #SoftwareEngineering #DeveloperTools #Coding #BackendDevelopment

Most devs use aComposable daily… but don’t know what it actually does. Here’s the clearest, save-worthy breakdown of aComposable you’ll read today. 💚 1) What aComposable really is It’s just an annotation… with superpowers. public annotation class Composable Add it to a function/type → the Compose compiler treats that code differently (state, position, recomposition). 2) Why retention = BINARY (not RUNTIME) - The Compose compiler reads it from .class files. - No runtime reflection = zero runtime tax. - Tools/IDE still see it. ✅ Performance win + great tooling. 3) Where you can put it (targets) - FUNCTION → aComposable fun Screen() - TYPE → val block: aComposable () -> Unit - TYPE_PARAMETER → fun Wrapper(content: aComposable () -> Unit) - PROPERTY_GETTER → val title: String aComposable get() = stringResource(R.string.app_name) 4) The invisible parameter mental model What you write: aComposable fun Greeting(name: String) { Text("Hello, $name") } What the compiler conceptually generates: fun Greeting(name: String, $composer: Composer) { Text("Hello, $name", $composer) } $composer tracks position in the UI tree, remembers values, and enables smart recomposition. 5) The rule that bites beginners ❌ Can’t call a composable from a regular function: fun notComposable() { Text("Hi") // ERROR } ✅ Do this instead: aComposable fun MyScreen() { Text("Hi") } 6) Why the design works - Type Safety → compiler stops illegal calls - Performance → BINARY retention avoids runtime costs - Flexibility → multiple targets cover real-world Compose patterns - Recomposition → only affected nodes re-execute 7) Pocket diagram to remember Data → aComposable functions → UI tree → Screen ↑ ↓ └────── Recomposition ──┘ 8) Copy-paste cheat sheet (save this 🔖) // FUNCTION aComposable fun MyButton() { /* UI */ } // TYPE val content: aComposable () -> Unit = { Text("Hi") } // TYPE_PARAMETER fun Container(body: aComposable () -> Unit) { body() } // PROPERTY_GETTER val title: String aComposable get() = stringResource(R.string.app_name) What’s one thing about aComposable that confused you when starting with Compose? Drop your story or a gotcha below — I’ll reply to every comment. 👇 If this clarified something, comment 💚 so I know to make the next deep dive. #JetpackCompose #androidDev #Kotlin Android Developers #ComposeCompiler #AndroidEngineers