Two languages. One powerful business advantage. JavaScript brings your digital presence to life - creating fast, interactive, and engaging user experiences. Python powers the backend - handling data, automation, and smart decision-making with ease. Together, they help businesses move faster, work smarter, and scale effortlessly. From customer experience to data intelligence, this combo is a game changer. #JavaScript #Python #WebDevelopment #DataDriven #BusinessGrowth #TechInnovation #Automation #DigitalTransformation #AI #StartupTech #SoftwareDevelopment

Most modern websites don’t just serve static HTML anymore, as they rely on JavaScript to render content dynamically. That means traditional scraping methods often fail… or miss critical data. So how do you actually scrape dynamic websites effectively? In our latest guide, we break it down: ✅ What “dynamic content” really means in practice; ✅ Why tools like BeautifulSoup alone aren’t enough; ✅ When to use headless browsers like Selenium or Playwright; ✅ How to build scalable scraping pipelines for real-world use cases. Read the full article on SapientPro's website. 🔗 Link in comments! #WebScraping #DataEngineering #Automation #Python #AI #Tech

Last week we've released major updates to SVAR Svelte components library 🎉 The release includes a new Avatar component for Core, dynamic rendering for dropdowns (now handling thousands of items), and FilterQuery – a powerful new component for AI-powered natural language search or structured queries. Learn more 👉 https://lnkd.in/dwb2dQ2v SVAR Svelte Gantt has also been updated to v2.6 with smart filtering powered by FilterQuery, plus new PRO features: slack visualization to understand schedule flexibility, and rollups for simplified visualization of complex projects. Learn more 👉 https://lnkd.in/drwNThn8 All free updates are available on GitHub ⭐ https://lnkd.in/dGqVcYSc #svelte #svelte5 #ui #gantt #webdev #javascript #frontend

your model can be perfect. your RAG pipeline can be clean. your embeddings can be tuned. but if your UI is a mess, nobody will use it. so here's the honest breakdown of how I think about building web interfaces as an AI engineer in 2026: Streamlit - my default for internal tools and demos if I'm showing something to a client or testing an idea fast, Streamlit wins every time. 10 lines of Python and you have a working app. the tradeoff? it looks like every other AI demo on the internet. Gradio - for ML model demos specifically Hugging Face made this the standard for sharing models. great for quick inference UIs. not great for anything complex. Next.js + React - when it actually needs to ship if the product is real, this is where I land. React is still the most hired framework in the market and Next.js is basically the default stack for startups in 2026. server components changed everything. FastAPI + any frontend - the AI engineer's power move your backend is already Python. FastAPI gives you a production-ready API in minutes. pair it with anything on the frontend. you don't need to master all of these. Streamlit gets you 80% there for AI demos. Next.js gets you the remaining 20% when you're shipping to real users. the best stack is the one you can actually build fast in. what's your go-to for AI project UIs? genuinely curious 👇 #AIEngineering #WebDevelopment #BuildInPublic #Python #React

🚀 More than just a project—it’s a solution for developers! 💻✨ I built the AI Code Evaluator because I wanted to solve a real problem: How to get instant feedback during interview practice without a human reviewer? 🧐 Why it’s unique:  🔹 The Engine: Instead of simple execution, I built a structural analyzer using Python Regex. It scores your code based on modularity, loops, and documentation! 🔹 The Persistence: One of my biggest challenges was syncing state—I implemented a custom LocalStorage sync so your code stays with you, even if you refresh! 🔹 The Stack: A truly Full-Stack experience with Flask & SQLite (SQLAlchemy) for a persistent, randomized challenge database. 🔹 The Design: A premium Glassmorphic UI that makes coding sessions feel like a high-end experience (Vanilla CSS/JS only!). I didn't just write code; I built an Engine that thinks. 🧠💪 🔗 Try it here: [ https://lnkd.in/drUHwfUJ] 📂 GitHub: [https://lnkd.in/drkBpTdh] #FullStackDeveloper #ProblemSolver #Python #Flask #WebDesign #UIUX #Achievement #SQLite #WomenInTech

🚀 Just shipped — a fully-featured ReAct Agent built with LangGraph & LangChain, with a clean Streamlit UI. Here's what it can do: 🛠️ Built-in Tools: • 📂 File read / write / delete • 🖥️ Shell command execution • 🌐 Web search 🤖 Multi-Provider LLM Support: • OpenAI • Anthropic • Groq • Ollama (local inference — fully offline capable!) 💡 What makes it stand out: Real-time reasoning transparency. As you chat, you can watch the agent's tool calls and reasoning steps unfold live — no black box. You see exactly what the agent is thinking and doing, step by step. Building this deepened my understanding of agentic workflows, tool orchestration, and how LLMs reason through multi-step tasks. LangGraph's graph-based approach to agent state management is genuinely powerful — highly recommend exploring it if you haven't. Happy to share more details or the repo for anyone curious. Drop a comment or DM me! 🙌 #AI #LLM #LangChain #LangGraph #ReActAgent #GenerativeAI #OpenSource #Python #Streamlit #AgenticAI #MachineLearning

📃 𝗔𝘀𝗸𝗶𝗳𝘆𝗣𝗗𝗙 - 𝗣𝗗𝗙 𝗔𝗜 𝗰𝗵𝗮𝘁 Lately, I've been really interested in how Retrieval Augmented Generation (RAG) actually works behind the scenes. So, to get hands-on experience, I decided to build AskifyPDF, that lets me chat with my PDFs. --- Here's a quick breakdown of how everything works: 1. When you upload a PDF, the React frontend securely pushes it to Supabase Storage. 2. A FastAPI backend immediately downloads the file, extracts the text, and intelligently divides it into overlapping semantic chunks, preserving the original page numbers for every single chunk. 3. These chunks are converted into high-dimensional vector embeddings (via local LLM inference) and uploaded to a Pinecone Vector Database. 4. When you type a query, the backend embeds your question, runs a similarity search against Pinecone, and isolates the most relevant paragraphs from that specific document. 5. The retrieved context is fed into a locally running Mistral LLM with strict instructions to answer only based on the text provided. 6. The AI generates the answer along with structured citations. Back on the frontend, these citations become interactive buttons. Click one, and the pdf viewer instantly leaps to the exact source page so you can verify the AI's claims yourself. 💻 Stack: React (Vite), FastAPI, Supabase, Pinecone, Local Mistral (Ollama). --- Overall it was fun building a tiny project! I will be experimenting more and adding fun features later. --- #RAG #ReactJS #Python #MachineLearning

Reverse-Engineering the Architecture of a Modern AI CLI 🚀 Recently, the source map for a major enterprise AI CLI tool was accidentally exposed on npm (iykyk). Instead of just looking at the files, I saw an opportunity for a deep-dive architectural study. I wanted to understand exactly how top-tier engineering teams route context, manage Model Context Protocol (MCP) servers, and handle polymorphic tools at scale. So, I built a Static DAG Architecture Visualizer. Checkout here: https://lnkd.in/dGFRutw8 🛠️ How I built it: Backend (Python): I wrote a custom Abstract Syntax Tree (AST) parser to statically analyze hundreds of TypeScript files. The script uses deep Regex to extract behavioral metadata—like class inheritance (extends BaseTool), public exported APIs, and JSDoc descriptions—and compiles them into a pure mathematical dependency graph (JSON). Frontend (React + Vite): I built a highly responsive dashboard using React Flow. The Math (Dagre): To prevent the 600+ node graph from turning into an unreadable "hairball," I implemented the dagre layout engine to calculate hierarchical x/y coordinates dynamically, allowing for strict Left-to-Right data flow tracking. 💡 Key Architectural Takeaways I discovered: - The "God Class" Pattern: Almost all CLI capabilities inherit from a centralized Tool.ts interface, proving a highly decoupled, polymorphic command structure. - Terminal as a UI: The CLI heavily utilizes React Ink, treating the terminal prompt like a full-blown React web application with complex state management and dialog lifecycles. - Dynamic Domain Toggling: Dumping 600 nodes crashes the browser's main thread. I implemented a Domain Filter that isolates subsystems (e.g., Core Engine vs. MCP Integration), instantly recalculating the DAG math and preventing layout engine locking. Building tools to analyze other tools is one of my favorite ways to level up as a Full-Stack developer. If you're building complex React applications or scaling Python backends, I'd love to connect! #SystemArchitecture #ReactJS #Python #AST #SoftwareEngineering #WebDevelopment #ReactFlow

I almost broke 300+ calculators on calculator-all.com because I trusted AI too much. 📉 As a Senior Frontend Engineer working with TypeScript and Next.js 15, I thought I’d found the ultimate development shortcut. I was using Cursor and Vercel v0 to rapidly prototype a new complex interest tool. The UI looked stunning with Tailwind CSS, and the initial logic seemed perfect. 🚀 Then things got weird. I asked the AI to integrate TanStack Query for a small data-fetching piece and write unit tests using Vitest. The AI confidently hallucinated a non-existent prop in a Next.js 15 experimental API. When I pointed out the error, it tried to "fix" it by wrapping everything in a hacky 'any' type and a mountain of @ts-ignore comments. 🤖 My compiler was screaming, but the AI was acting like everything was fine. It was a classic "aha" moment: AI is incredible at speed, but it lacks the nuanced judgment of a human dev. I spent the next hour manually refactoring the state logic. It turns out, tools like Cursor are great for the boilerplate, but they still struggle with the "why" behind complex architecture. 🛠️ Building calculator-all.com has taught me that my value isn't just writing code—it's being the gatekeeper of quality. I’d rather ship 10 minutes later than ship a bug that affects thousands of users. 😅 What’s the most confident "wrong" answer an AI tool has ever given you? #FrontendEngineer #TypeScript #ReactJS #NextJS #WebDev #JavaScript #SoftwareEngineering #TechCareer #AI #Programming #Coding #WebDevelopment #DeveloperLife #CleanCode #TailwindCSS

I got tired of spending days trying to understand undocumented codebases, so I built Code Atlas. It’s an AI-powered visualizer that runs entirely locally. You just point it at a folder, and it automatically extracts the AST, resolves imports, and draws an interactive 3D map of your entire architecture. A few cool things it does: - Detects cross-language API connections (Links JS fetch() to Python @app.get) - Automatically flags circular dependencies - Pulls Git Blame data for every specific function block - Includes an Architecture-Aware AI chat (Supports Gemini Cloud OR 100% offline local .gguf models via llama.cpp!) Built with FastAPI, React, D3.js, Tree-sitter, and FAISS. Check out the demo video below, and let me know what you think! GitHub Repo: https://lnkd.in/gQMwaTrf #softwareengineering #opensource #react #python #ai #developerproductivity