Not everyone who needs web data is a programmer. But most web scraping tools assume you are. So I built a No-Code Universal Web Scraper that extracts structured data from almost any webpage and turns it into clean datasets. You simply provide a list of URLs and the tool automatically extracts: • Page titles • Meta descriptions • Clean readable webpage text The goal was simple: Make web data extraction accessible without writing code. Under the hood it's powered by Python, HTTPX, and BeautifulSoup — but the user doesn't need to worry about any of that. Just input URLs → get structured data. This kind of lightweight scraping is useful for: • research • SEO pipelines • lead generation • automation workflows Sometimes the most useful automation tools are the simplest ones. Curious — what would you scrape if extracting website data was this easy? #WebScraping #DataAutomation #PythonAutomation #NoCode #DataEngineering

🚀 I Built an AI-Powered Weather Dashboard using Django What started as a simple weather app turned into a full interactive dashboard. While building this project, I explored how APIs, data visualization, and AI can work together in a real application. 🌦 Features I implemented: 📍 Get weather using current GPS location 🔎 Search weather by any city 📊 Hourly temperature graphs 📅 5-day forecast dashboard 🗺 Interactive weather map visualization 🌅 Sunrise & sunset data 🌫 Air Quality Index 🌧 Weather animations 🤖 AI-generated weather summary ⚙ Tech Stack Python • Django • APIs • JavaScript • Chart.js • Maps • AI This project helped me better understand how to build data-driven web applications and improve both backend and UI development. More projects coming soon as I continue learning and building. 💻 If you want to download source code Kindly visit: https://lnkd.in/g_QHgCNg #Python #Django #AI #WebDevelopment #MachineLearning #APIs #CodingJourney #100DaysOfCode

Most devs are still dumping raw HTML into LLMs.   → Wasted tokens   → Noisy context   → Worse accuracy & hallucinations  That stops now. I built llmparser a fast, open-source Python library that turns any website into clean, structured, LLM-ready content, without using an LLM. Zero API keys. Zero extra costs. Just pure, signal-rich input. 🔥 What it actually delivers: - Strips out navbars, footers, cookie popups, ads, sidebars - Outputs beautiful, structured Markdown (headings, lists, tables, code blocks preserved) - Handles JavaScript-heavy sites with Playwright rendering - Auto-expands collapsed sections & “read more” content - Delivers typed content blocks + rich metadata (title, date, author, etc.) - Smart adaptive engine: picks the best extraction strategy per page type Perfect for anyone building: → RAG pipelines   → AI agents & memory systems   → Knowledge bases / vector stores   → Training / fine-tuning datasets   → Anything where garbage-in → garbage-out kills performance Your LLM is only as smart as the data you feed it.   llmparser fixes the input layer. Install in seconds:   `pip install llmparser` GitHub (⭐ & contribute!): https://lnkd.in/e4RcqkUa   PyPI: https://lnkd.in/eF2c7Jkt Early days v0.1 just dropped. Feedback, issues, PRs all very welcome. Who else is tired of fighting messy web scrapers for LLM work?   Drop a 🔥 if you're giving it a spin, or tell me your biggest web→LLM pain point below 👇 #AI #LLM #RAG #OpenSource #Python #DataEngineering #MachineLearning #BuildInPublic #ArtificialIntelligence

🚀 Built an AI-powered chatbot using Django and Ollama (Phi-3 model)! Key highlights: ✅ Local LLM integration (No paid APIs) ✅ User authentication & authorization ✅ User-wise conversation memory ✅ Customized Django Admin UI ✅ Scalable backend design This project helped me understand how AI models can be practically integrated into real-world web applications. Would love to hear your feedback!   #Django #AI #LLM #Ollama #Phi3 #Python #WebDevelopment #Projects

Bridging the Gap Between AI Agents and Web Interfaces The Challenge: In multi-agent systems, agents generate "Artifacts"—files such as PDFs, images, or data exports. While the agent is aware of what it created, external Web UIs and dashboards often lack a standardized method to "query" or "discover" these outputs without complex workarounds. The Solution: New Web API endpoints have been implemented specifically for Artifact Metadata retrieval. Key Technical Highlights: - Standardized Access: A clean interface has been created to fetch metadata for versioned binary data (images, audio, etc.). - Observability: Web-based monitoring tools have been enabled to track agent session outputs in real-time. - Production Readiness: The integration workflow for developers building front-end dashboards on top of the ADK ecosystem has been improved. This enhancement makes AI-generated content more accessible and manageable for production-grade web applications. Tech: Python, REST APIs, Google ADK Pull Request: https://lnkd.in/gMFdwDQR #GoogleADK #AIAgents #Python #OpenSource #SoftwareEngineering #GenerativeAI

Do you know how to turn your ML models into interactive web apps? Here’s something that makes it easy. Gradio is an open-source Python library that turns your models into interactive web apps with just a few lines of code. Here's what makes it powerful: 1. It turns static projects into portfolio assets Anyone can interact with your model and see real-time predictions. 2. You can share it instantly Gradio generates a public link you can send to recruiters, collaborators, or clients. 3. It proves you think beyond notebooks It shows you understand the full lifecycle: not just training models, but making them usable. Here's how you can start using Gradio today: Step 1: Pick one working model from your portfolio (text classification, image recognition, regression: anything functional). Step 2: Install Gradio: ``` pip install gradio ``` Step 3: Wrap it in a few lines of code: ```python import gradio as gr def predict(input):    # your model code    return output gr.Interface(fn=predict, inputs="text", outputs="label").launch(share=True) ``` Step 4: Test it thoroughly, and observe where your model fails. Step 5: Publish the shareable link. Add it to your GitHub README, include it in job applications, or showcase it on LinkedIn. Gradio supports images, text, audio, video, sliders, dropdowns. You can build a functional demo in minutes. Learn more and get started here: https://www.gradio.app/ What's one project in your portfolio that would benefit from an interactive demo? ♻️ Kindly repost to help this reach more aspiring data scientists. #DataScience #MachineLearning #DataScientist #DataAnalytics #AheadGen2026 #TeamDoings

Amplifying.ai pointed Claude Code at real repos 2,430 times and watched what it chose. No tool names in any prompt. Just open-ended questions. The biggest finding: Claude Code builds, not buys. Custom/DIY is the most common recommendation in 12 of 20 categories. When asked to add feature flags, it builds a config system from scratch instead of recommending LaunchDarkly. When asked to add auth in Python, it writes JWT from scratch. Where it does pick tools, it picks decisively: GitHub Actions 94%, Stripe 91%, shadcn/ui 90%. And newer models systematically favor newer tools. Prisma went from 79% to 0% as Drizzle took over completely. Celery collapsed from 100% to absent. The default stack is being written by the agent, not by you. Full breakdown: #AI #DeveloperTools #SoftwareEngineering #ClaudeCode #WebDevelopment

Not every system needs an API. But the moment two systems need to talk, you probably do. APIs sound complicated, but they’re really just organized doors into your system. If a mobile app, website, AI model, or another service needs your data, an API is how they get it. One of the fastest ways to build one today is Python + FastAPI. Here are general principles I use when building APIs. 1. First decide if you even need an API A quick rule I use: Build an API if • another system needs your data • a mobile or web app needs backend logic • you want multiple services to reuse the same functionality If your code is only used inside one script or one application, an API may be unnecessary overhead. 2. Scope the data before writing code You don’t want to build giant API that return everything. Instead ask: What is the one piece of information the caller actually needs? Example: Bad design /users → returns the entire user database Better design /users/{id} → returns one user APIs should expose small, focused data points. Smaller responses = faster systems and easier maintenance. 3. Create a simple endpoint Example with FastAPI: from fastapi import FastAPI app = FastAPI() @app.get("/status") def read_status(): return {"status": "API is running"} Plain English: Someone requests /status The system responds “API is running.” That’s an API. 4. Return useful data @app.get("/users/{user_id}") def get_user(user_id: int): return {"user_id": user_id, "name": "Isaiah"} Request: /users/1 Response: {"user_id": 1, "name": "Isaiah"} Now any app, system, or AI model can use that data. Why FastAPI is great for this FastAPI gives you: • high performance • automatic API documentation • built-in validation • clean Python code You can go from idea to working API in minutes. Every modern system runs on APIs. Apps. AI systems. Enterprise platforms. Government systems. Understanding how to design them is one of the highest leverage skills in tech. Follow me for more content on systems thinking, architecture, and building software that actually ships. #Python #FastAPI #APIDesign #SoftwareArchitecture #SystemsDesign #AIEngineering #TechCareers

I recently built and deployed a 𝐌𝐎𝐕𝐈𝐄 𝐑𝐄𝐂𝐎𝐌𝐌𝐄𝐍𝐃𝐀𝐓𝐈𝐎𝐍 𝐖𝐄𝐁 𝐀𝐏𝐏𝐋𝐈𝐂𝐀𝐓𝐈𝐎𝐍 that suggests similar movies based on machine learning techniques. Live App: https://lnkd.in/g7NtdHJk 𝐏𝐑𝐎𝐉𝐄𝐂𝐓 𝐎𝐕𝐄𝐑𝐕𝐈𝐄𝐖 The application recommends movies based on similarity using TF-IDF vectorization and cosine similarity. It combines a machine learning backend with a web interface to provide interactive movie discovery. 𝐓𝐄𝐂𝐇 𝐒𝐓𝐀𝐂𝐊 𝗠𝗔𝗖𝗛𝗜𝗡𝗘 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚 • Scikit-learn (TF-IDF Vectorization) • Tokenization and text feature extraction • Cosine similarity for recommendations 𝗗𝗔𝗧𝗔 𝗣𝗥𝗢𝗖𝗘𝗦𝗦𝗜𝗡𝗚 • Pandas • NumPy 𝗕𝗔𝗖𝗞𝗘𝗡𝗗 • FastAPI for building REST APIs • Async requests using httpx • TMDB API integration for movie posters, genres, and metadata 𝗙𝗥𝗢𝗡𝗧𝗘𝗡𝗗 • Streamlit for the interactive web interface 𝗞𝗘𝗬 𝗙𝗘𝗔𝗧𝗨𝗥𝗘𝗦 • Movie search with suggestions • Similar movie recommendations using TF-IDF similarity • Genre-based recommendations • Movie posters and metadata fetched from TMDB • Fully deployed web application This project helped me gain hands-on experience with building 𝗘𝗡𝗗-𝗧𝗢-𝗘𝗡𝗗 𝗠𝗔𝗖𝗛𝗜𝗡𝗘 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚 𝗔𝗣𝗣𝗟𝗜𝗖𝗔𝗧𝗜𝗢𝗡𝗦, including data processing, model integration, API development, and deployment. Feedback and suggestions are welcome. #MachineLearning #Python #FastAPI #DataScience #RecommendationSystem #Streamlit #ScikitLearn

🚀 I Built an AI Shopping Assistant That Can't Be Tricked Ever spent hours comparing phone specs, reading reviews, and still feeling unsure? I built an AI-powered Shopping Chat Agent that lets you just ask — in plain English — and get honest, grounded recommendations. 📱 Features: 🔍 Natural Language Search — "Best camera phone under ₹30,000" → Get 2-3 curated picks with clear reasoning 📊 Phone Comparison — Compare up to 3 phones side-by-side across display, camera, battery & performance 💡 Technical Explanations — "What is OIS?" → Jargon-free answers anyone can understand 🎯 Smart Recommendations — AI understands your priority (camera, gaming, battery, value) and picks accordingly 🛡️ Adversarial Handling — Two-tier safety system blocks jailbreaks, prompt injection & off-topic abuse ⚡ Scalability Ready — Redis caching, rate limiting, Celery task queues, Kubernetes-ready architecture 🔒 Zero Hallucination by Design — The AI only recommends phones from the actual catalog. No made-up specs. No brand bias. ⚙️ Tech Stack: 🔹 Python + FastAPI (async backend) 🔹 Google Gemini 1.5 Flash (AI engine) 🔹 Next.js 14 + React + TypeScript (frontend) 🔹 Redis (caching, sessions, rate limiting) 🔹 Docker + Docker Compose Key takeaways from building this: 1️⃣ Prompt engineering IS software engineering — structured prompts with explicit schemas beat vague instructions 2️⃣ Grounding eliminates hallucination — feed the LLM real data, don't let it freestyle 3️⃣ Safety can't be an afterthought — 50+ regex patterns + LLM semantic detection built from day one 4️⃣ Cache everything — 40x faster responses, 40% reduction in AI API costs I wrote a detailed deep-dive on the architecture, safety system, and scaling strategy 👇 📖 https://lnkd.in/gQNEp7if 💻 GitHub: https://lnkd.in/gY6-5ZK7 Would love to hear your thoughts! What's your approach to LLM safety in production apps? #AI #LLM #Python #FastAPI #GoogleGemini #PromptEngineering #NextJS #MachineLearning #WebDevelopment #OpenSource #SoftwareEngineering