🤖 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐝𝐨𝐧'𝐭 𝐡𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐞 𝐢𝐧 𝐩𝐚𝐫𝐚𝐠𝐫𝐚𝐩𝐡𝐬 𝐚𝐧𝐲𝐦𝐨𝐫𝐞. 👻 𝐓𝐡𝐞𝐲 𝐡𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐞 𝐢𝐧 𝐉𝐒𝐎𝐍. Last week, I spent 2 hours debugging why my agent kept failing a tool call. GPT-5.2 output v1: "score": "42" GPT-5.2 output v2: "score": 42 Switching keys or types can really throw off your whole workflow—that's just the tame example. If you're working in the AI world, you’re probably all too familiar with the real challenges: comparing two LLM function calls side by side, checking what changed in your RAG retrieval payload, debugging why your API v2 returns an extra nested field, or reviewing agent memory before and after running a tool. We tend to handle these tasks the same old way—opening two VS Code tabs, squinting, scrolling, and often missing that one crucial detail. That’s why I created the tool I really needed: 𝐉𝐒𝐎𝐍 𝐃𝐢𝐟𝐟 𝐕𝐢𝐞𝐰𝐞𝐫. It’s open source, instant, and accessible without any login. Just paste JSON A and JSON B, hit compare, and it shows you exactly what’s new in green, what’s been removed in red, what’s changed in yellow, and any type changes in orange (this last one has saved me multiple times!). It’s exactly like in the screenshot—side by side, clear, distraction-free. I built this because JSON is the most reliable way we have to understand the contracts between models, tools, and APIs in agentic systems—but we’ve lacked good tools to see what actually changed. It's free, open source, and works right in your browser. If you’re building agents, LLM apps, or working with APIs every day, give it a try—you'll wonder how you ever managed without it. Think of how many hours you might have lost last month to a missing comma or a confusing string versus a number. Find the link in the first comment, or comment "𝐃𝐈𝐅𝐅" and I'll DM it to you. #buildinpublic #opensource #aiagents #llm #developers #javascript #python #claudecode #anthropic

Your AI agent is probably parsing HTML with regex instead of using proper DOM selectors. Built an agent last week that extracts product data from e-commerce sites. First version used regex patterns to grab prices and descriptions. Worked fine on the test site. Then we pointed it at real websites. Complete disaster. Regex failed on escaped quotes, nested tags, and dynamic content. The agent was extracting random text fragments and calling them product prices. Switched to Playwright with CSS selectors. Same task, 90% fewer errors. The lesson: HTML isn't a regular language. Stop treating it like one. What's the worst parsing mistake you've made? --- Want to automate your workflows or build AI-powered systems for your business? DM me — I help teams ship automation that actually works. #AI #Automation #WebScraping #Python #Playwright #DataExtraction #TechTips #WebDev

Strengthening backend logic by practicing URL normalization in JavaScript, using trim, lowercase, regex, and domain parsing. These are AI‑generated exercises to keep improving my workflow and consistency. :) #JavaScript #Backend #WebDev #Coding #AIExercises

Smart Summarizer, an AI-powered text summarization web app built using Flask and JavaScript. I wanted to go beyond tutorial projects and build something that felt real. CORS errors, async debugging, and midnight bug fixes it was all part of the process. Every error taught me something I could not have learned any other way. Features: A fully functional Flask backend with dedicated routing and JSON communication CORS configured to handle cross-origin requests between frontend and backend Dual-layer input validation on both client and server side Rate limiting to throttle requests to 10 per minute Real-time word count tracking on both input and output fields Async request handling with loading state and error feedback Automatic sentence capitalization applied to summarized output 🔧 This project helped me learn: How to structure a Flask backend and handle routing How to integrate third-party AI APIs into a backend The importance of validating input on both ends How CORS works and why it matters Frontend-backend communication Secure API key management with environment variables https://lnkd.in/df97hbVf Demo: https://lnkd.in/dMTPHT8z #Python #Flask #JavaScript #WebDevelopment #FullStack  #MachineLearning #NLP #HuggingFace #SoftwareEngineering  #BackendDevelopment #API #BuildInPublic  #CStudent #Programming #100DaysOfCode

Let’s recap what we know in JavaScript! These days, my feed is full of “use this AI” and “use that AI.” While AI is powerful, we often overlook the fundamentals that actually drive our day-to-day work and what truly gets tested in interviews. Before jumping to tools, it’s important to strengthen the core. Because at the end of the day, AI can assist you but it can’t replace your understanding of JavaScript fundamentals. From closures, hoisting, and promises to async/await, event loop, and this keyword these are the building blocks every developer should be confident in. I’ve attached a PDF below let’s go back to basics and explore JavaScript the right way. For more insightful content checkout below: 🟦 𝑳𝒊𝒏𝒌𝒆𝒅𝑰𝒏 - https://lnkd.in/dwi3tV83 ⬛ 𝑮𝒊𝒕𝑯𝒖𝒃 - https://lnkd.in/dkW958Tj 🟥 𝒀𝒐𝒖𝑻𝒖𝒃𝒆 - https://lnkd.in/dDig2j75 or Priya Frontend Vlogz #JavaScript #WebDevelopment #CodingBasics #Frontend #LearnToCode #Programming #Developers

As many of you might know by now, I’m building a JavaScript engine for .NET using 100% AI-generated code. It’s gone way beyond what I initially thought was possible. Out of roughly 93,000 ECMAScript 262 tests, I’m down to about 500 failing. Everything I expected to be difficult is already solved. And what’s left? what are those 500 failing tests? It’s almost comical; ** Regex **. The .NET regex engine simply can’t handle everything the ECMAScript spec requires. Variable-length lookbehind being the main offender, plus a few other edge cases. Right now, I’m transpiling JavaScript regex into .NET regex syntax. That works for almost everything, except for those limitations. So now I’m at a crossroads. I could stop here. Point proven. AI can build something like this. Ship it, move on, build something actually useful. Or… How hard can it be to build a regex engine? 😄 Surely it can’t be harder than building an entire JavaScript engine. Right? What would you do?

𝗗𝗼𝗻𝘁 𝗣𝘂𝗯𝗹𝗶𝘀𝗵 𝗔𝗜 𝗖𝗼𝗱𝗲 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗧𝗵𝗶𝘀 𝗖𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁 AI makes coding fast. It often adds bloat. It hurts performance. It creates bad patterns. Use this checklist to keep your code lean. Must do: - Add width and height to images. - Import only the functions you need. Optimize these: - Check for heavy libraries like axios or lodash. - Use dynamic imports for big tools. - Use transform and opacity for animations. - Set explicit transitions. - Load fonts in HTML. - Use Promise.all for parallel requests. - Use React Server Components for static parts. Scale your project: - Use .cursorrules for standards. - Use ESLint to ban bad imports. - Set bundle size limits in CI. - Put performance rules in your prompts. Ask these questions before merging: - Does this add unnecessary bloat? - Is lazy-loading better here? - Is this the smallest import? - Does this cause layout shifts? What bad AI code did you find lately? Source: https://lnkd.in/gkWHFPNb

A while ago I wrote an #eslint plugin to automatically correct inlined SVG elements to be proper JSX. Small problem, huge win for developer experience. Happy to announce that eslint-plugin-svg-jsx is now fully modernized and supports eslint version 8, 9, and 10! In today's day and age when all you hear is #AI solves everything, it's easy to use it as the first choice. But why use something non-deterministic and extremely compute-heavy to catch things like lint and style issues? https://lnkd.in/eAg_7ai7

🤖 What if your browser could think? No Python. No heavy backend. Just JavaScript running machine learning models directly in the browser. Sounds futuristic? It’s already happening. 🚀 JavaScript for Machine Learning: The New Frontier With tools like TensorFlow.js, developers can now build and run ML models on the client-side—in real time. That means: ✔ No server dependency ✔ Faster predictions ✔ Better privacy (data stays on-device) ✔ Interactive, intelligent web apps From image recognition to sentiment analysis, JavaScript is no longer “just for UI”—it’s becoming a full-stack AI tool. 💡 Where You Can Use It 🧠 Image classification in web apps 🎤 Voice recognition & commands 😊 Sentiment analysis for user feedback 🎮 AI-powered browser games 📊 Smart dashboards with predictive insights 💡 Practical Tips to Get Started 🔹 Start with pre-trained models Don’t train from scratch. Use existing models for faster results. 🔹 Optimize for performance Use smaller models or quantized versions to avoid slowing down the browser. 🔹 Leverage WebGL TensorFlow.js can use GPU acceleration—huge boost for performance. 🔹 Handle async operations properly ML tasks can be heavy—use async/await to keep UI smooth. ✨ Pro Tip: Think experience-first, not just accuracy. 👉 A slightly less accurate model that runs instantly often beats a perfect model that lags. 🔥 Why This Matters We’re entering a world where apps don’t just respond—they predict, adapt, and learn. And JavaScript developers are no longer limited to front-end logic… They can now build intelligent, AI-powered experiences directly in the browser. 💬 Let’s discuss: If you could add AI to one of your web projects today, what would it do? #JavaScript #MachineLearning #TensorFlowJS #WebDevelopment #AI #FrontendDev #Tech #Innovation #CodingTips

Building a smarter search with RAG I spent the last 24 hours building The Atheneum, a search tool that uses Retrieval-Augmented Generation (RAG) to help find books based on context rather than just titles. The Goal: Most book sites fail if you don't know the exact name. I wanted to see if I could use vector embeddings to let users search by "feeling" or "intent." The Technical Breakdown: Scraping: Built a custom Python script (BeautifulSoup) to pull and clean data. Vector Store: Integrated ChromaDB to store book descriptions as embeddings. Intelligence: Used a RAG pipeline to match natural language queries to the most relevant books in the database. Frontend: Built a clean React interface using Tailwind CSS and Framer Motion to handle the AI's "Librarian Console" responses. Why this mattered to me: Instead of just building a basic CRUD app, I had to figure out how to handle "dirty" scraped data, manage vector similarity, and keep the UI responsive while the AI processes a request. It was a great challenge in combining data engineering with a functional user experience. #BuildInPublic #Python #Django #React #AI #RAG #SoftwareEngineering #DeepLearning #OpenSource