Gaurav Patel’s Post

🧵 I built an AI tool that would have saved me 10 hours last month. Last month I spent 3 hours debugging a single TypeError. 2 hours writing documentation nobody asked for. 1 hour writing boilerplate code I've written 50 times before. So I built DevMind AI to solve all three. In the video you can see: ✅ Describing a feature in plain English → getting working code in 5 seconds ✅ Pasting a broken function + error message → AI explains exactly what's wrong ✅ Pasting undocumented code → getting full professional JSDoc in seconds What I learned building this: → How JWT authentication actually works at the code level → Why Redis caching matters (77% latency reduction is real) → How to structure a multi-tenant SaaS backend → Next.js 14 App Router + server components in production → Deploying fullstack apps with CI/CD pipelines This is the project I'm most proud of in my portfolio. 💻 Code: https://lnkd.in/gZkqrUN7 What would you build with an AI coding assistant? 👇 #javascript #typescript #nextjs #nodejs #ai #llm #buildinpublic #webdev #coding #softwaredevelopment

🚀 Why REST APIs Matter in Modern Software Development + FastAPI vs Flask In modern software development, exposing backend functionality through REST APIs has become essential. REST APIs enable seamless communication between different services and clients (web, mobile, third-party apps). This approach helps overcome key limitations of monolithic architectures such as tight coupling and poor scalability by supporting more modular and distributed systems like microservices. In Python, frameworks like FastAPI are widely used to build and serve backend applications as REST APIs efficiently. With features like high performance, automatic validation, and built-in API documentation, FastAPI has become a popular choice for modern backend development. 💡 FastAPI vs Flask — Basic Difference 🔹 FastAPI Modern, high-performance framework (built on ASGI) Supports async/await natively Automatic API documentation (Swagger UI) Built-in data validation using Pydantic Best suited for AI/ML APIs, microservices, and production systems 🔹 Flask Lightweight and simple framework (built on WSGI) Mostly synchronous (async support is limited) Requires extensions for validation and documentation More flexible but needs more setup Best suited for small applications and beginners 🎯 Key Takeaway FastAPI is ideal for building scalable, high-performance APIs in modern systems, while Flask is great for quick prototypes and simple applications. #AI #DataScience #MLOps #FastAPI #Flask #BackendDevelopment #SoftwareEngineering #Microservices

Reading a new codebase is one of the most underrated hard problems in engineering. You clone a repo. 40 files. No docs. You're lost for an hour before writing a single line. I built Codebase Explainer to fix that — a tool that takes any GitHub repo and gives you an AI-generated map of what it actually does. How it works: → Paste a GitHub repo URL → GitHub API fetches the file tree and source → Groq API (Llama) reads the code and generates plain-English explanations per module → D3.js renders an interactive graph showing structure and dependencies The interesting engineering problems: Context window management — You can't dump an entire repo into an LLM call. Had to design a chunking strategy: summarize files individually, then synthesize at the module level. Two-pass architecture. GitHub API constraints — Rate limits hit fast on public repos without auth. Built token-based auth handling to stay within limits without breaking the flow. D3.js with dynamic data — D3 is powerful and painful. Making the graph actually readable (not a hairball) with real repo data required intentional layout decisions, not just default force simulation. What this is really about: Most AI dev tools wrap GPT in a chatbox. This one produces a visual artifact — something you can navigate, not just read. That distinction shaped every design decision. What I'd add next: Cross-file dependency tracing. Right now it's file-level. Making it symbol-level (function calls, imports) would make it genuinely production-useful. Tech stack: Frontend: React + Vite Backend: Node.js + Express AI: Groq API (for code explanation/summarization) Visualization: D3.js (dependency/structure graphs) External API: GitHub API (repo fetching) Deployment: Render, Vercel GitHub: https://lnkd.in/gaXiVsK9 Live Link: https://lnkd.in/gsuEV73y #DevTools #React #D3js #AI #GroqAPI #FullStack #MERN #BuildInPublic #OpenSource

In 2026, "knowing how to code" is the bare minimum. The real skill is knowing how to build at 10x speed. I spent my weekend reflecting on how my workflow has evolved while building complex NestJS and React applications. Two years ago, I’d spend hours debugging boilerplate and writing repetitive Type definitions. Today, that’s gone. By leveraging IDEs like Cursor, I’ve shifted my focus from writing lines to architecting systems. Here is how my "Full Stack" process looks now: NestJS & TypeScript: Instead of manual setup, I use AI to scaffold secure controllers and DTOs. This allows me to spend my time on the logic that matters—like multi-tenant security and complex data pipelines. React & Tailwind: I focus on the UX and component architecture. I let AI handle the repetitive CSS and prop-drilling fixes. Deployment (EC2/AWS): I use AI to generate optimized Nginx configs and CI/CD scripts, reducing the "it works on my machine" headache to almost zero. The result? -> I can take a feature from a whiteboard sketch to a deployed EC2 instance in a fraction of the time it used to take. For international clients and startups, this speed is the difference between leading the market and falling behind. Code is becoming a commodity. Architecture and Speed are the new gold. How has your tech stack changed this year? Are you still writing everything from scratch, or are you augmenting your workflow? #SoftwareEngineering #CursorIDE #NestJS #ReactJS #FullStack #AI #RemoteWork #PakistanTech

The Anatomy of a Better Dev Workflow 🛠️ I just came across this breakdown of the .claude/ folder structure, and it’s a masterclass in AI orchestration. I’m always looking for ways to streamline the "setup" phase of a task. This modular approach allows you to separate: Global Project Rules (committed to Git for the team) Personal Overrides (kept in .local files and gitignored) Automated Workflows (via the skills/ and agents/ folders) It’s essentially Infrastructure as Code (IaC), but for your AI assistant. If you aren't version-controlling your AI instructions yet, you're missing out on serious local development gains. #PHP #NodeJS #Javascript #AIforDevs #CleanCode #MahiITServices

Making Fast-Moving AI Workflows Easier to Steer Developers building with LLMs and agents face a daunting challenge: making fast-moving workflows easier to steer and reuse in real projects. The traditional approach often relies on cumbersome server-side solutions that are difficult to scale and maintain. However, CJackHwang/ds2api offers a more practical solution. This lightweight, high-performance full-stack middleware converts client protocols to universal APIs, supporting multi-account rotation, compiled binaries, Vercel Serverless, and Docker. It's built with Go and features a React management interface, making it easy to manage and deploy. What's more, ds2api doesn't rely on Python runtime, giving developers more flexibility. What stands out about ds2api is its ability to simplify complex workflows. Here are some key features: - Built with Go for high-performance and reliability - Frontend: React management interface for easy deployment - Deepseek to API: A lightweight, high-performance full-stack middleware - Compatible with Google, Claude, and OpenAI API formats The traction makes sense: a repository sitting at #9 with around 418 new stars is usually solving a problem people can feel immediately. It's added roughly 418 new stars in the current trending window, and its star velocity suggests this is more than a short spike. It lands in high-interest areas like openai-api, making it a valuable resource for developers. Repo: https://lnkd.in/gQDbJNbH #GitHub #OpenSource #GitHubTrending #LinkedInForDevelopers #Go #Ds2api #Api #ClaudeApi

🤯 𝗦𝗼𝗺𝗲𝗼𝗻𝗲 𝗿𝗲𝘄𝗿𝗼𝘁𝗲 𝗡𝗼𝗱𝗲.𝗷𝘀 𝗶𝗻 𝗧𝘆𝗽𝗲𝗦𝗰𝗿𝗶𝗽𝘁 𝘀𝗼 𝗶𝘁 𝗿𝘂𝗻𝘀 𝗲𝗻𝘁𝗶𝗿𝗲𝗹𝘆 𝗶𝗻𝘀𝗶𝗱𝗲 𝗮 𝗯𝗿𝗼𝘄𝘀𝗲𝗿 𝘁𝗮𝗯. 𝗔𝗻𝗱 𝗶𝘁'𝘀 𝗳𝗿𝗲𝗲. Meet Nodepod, built by the team at Scelar. No WebAssembly binary. No server. No cost. Just a 100% Node.js-compatible runtime that boots in about 100ms, weighs ~600KB gzipped, and lets you npm install express, write a server, and hit it with HTTP requests without ever leaving the browser tab. The comparison with WebContainers is brutal: WebContainers reportedly costs upwards of $27k per year and takes 2-5 seconds to boot a multi-megabyte WASM binary. 𝗡𝗼𝗱𝗲𝗽𝗼𝗱 𝗶𝘀 𝗠𝗜𝗧 𝗹𝗶𝗰𝗲𝗻𝘀𝗲𝗱 𝗮𝗻𝗱 𝘀𝘁𝗮𝗿𝘁𝘀 𝗶𝗻 𝟭𝟬𝟬𝗺𝘀. For AI products, this is a big deal. If you're building an AI coding assistant, a code generation tool, or any kind of agentic workflow that produces runnable code, you now have a free, instant, browser-native way to preview and execute that output right where the user is. No spinning up cloud containers per user. No infra costs. The browser is the runtime. They even shipped wZed, a full browser-native IDE built on top of it, to prove the point: Monaco editor, integrated terminal, live preview, npm installs, all in a single browser tab. No install. The runtime space is moving fast. Between EdgeJS, WebContainers, and now Nodepod, the gap between "runs in a browser" and "runs like a real computer" is basically closing. Link below in the comments 👇 #OpenSource #JavaScript #NodeJS #AIEngineering #WebAssembly

I believe the best software often starts with a personal problem that needs solving. My latest project, Matthew, began exactly that way. The backstory is simple but significant: My friend Matthew had a collection of documents and PDFs that were becoming impossible to manage. He needed a way to query his own library without sending his entire document collection to the cloud. I decided to build him a tool that keeps the heavy lifting local and searchable. I am excited to share that I have just open-sourced the result. Matthew is a local-first desktop application designed for semantic search and AI-powered chat over your personal PDF collection. Here is how the privacy model actually works: 🔒 What stays on your machine: PDF parsing, chunking, embedding generation, and vector search all run locally. Your documents are never uploaded anywhere. 🌐 What goes over the internet: AI chat is powered by OpenRouter — when you ask a question, your query and the relevant retrieved passages are sent to whichever LLM you choose. ✅ Tech Stack • Tauri for a lightweight desktop shell • Rust for high-performance backend logic • React + TypeScript for a responsive interface • LanceDB + fastembed for fully local vector search ✅ Languages Used: TypeScript · Rust · JavaScript · CSS · HTML If you have been looking for a way to semantically search your local document library — or want to see how Tauri and Rust can power AI-driven desktop apps — check out the repository below. Explore the project here: https://lnkd.in/dSTit3m8 I would love to hear your feedback on the architecture. If you find it useful, please consider giving the repo a star, forking it to build your own features, or contributing to the codebase. #OpenSource #RustLang #Tauri #ArtificialIntelligence #SoftwareEngineering #DeveloperTools

🐰 Claude code writes it. CodeRabbit reviews it. You merge it. AI agents let us ship code faster than ever. But speed without quality is just a faster way to break production. That's why I sat down with Erfan Al-Hossami, engineer at CodeRabbit, for a full hands-on walkthrough of the AI code review platform that's quickly becoming essential in modern dev workflows. We went end-to-end on a Next.js project: ✅ Installing CodeRabbit and connecting it to GitHub ✅ Reviewing real PRs with severity levels (critical, major, minor, nitpicks) ✅ Path instructions for framework-specific guidelines ✅ Running reviews locally via the CodeRabbit CLI — no PR required ✅ The Claude Code plugin for inline reviews in your terminal ✅ How CodeRabbit learns your codebase and team conventions over time 🎯 The part that stood out to me: CodeRabbit has full context of your codebase. It reads your dependencies, your CLAUDE.md, your agents.md, and can pull the latest docs via web search or MCP servers like Context7. It's not reviewing your PR in isolation — it actually understands your project. Whether you're a solo dev vibe coding features or a team trying to keep AI-generated code from reaching main unchecked, this is worth the watch. 🔗 Full video linked in the comments. Huge thanks to Erfan for joining 🙏 Drop your questions below and we'll line up a round two. #AICodeReview #NextJS #ClaudeCode #WebDevelopment #CodeRabbit #AIEngineering

In full-stack development, "𝘜𝘱𝘵𝘪𝘮𝘦" is the gold standard. During my time at 𝗡𝗲𝘅𝘅𝘁.𝗮𝗶, we hit 𝟵𝟵.𝟵% 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 by monitoring infrastructure and response times. But in 𝗠𝗟𝗢𝗽𝘀, a "Green" status code isn't enough. You can have a perfectly functioning 𝗡𝗲𝘅𝘁.𝗷𝘀 frontend and a 𝗡𝗼𝗱𝗲.𝗷𝘀 backend, but if your machine learning model is experiencing 𝗗𝗮𝘁𝗮 𝗗𝗿𝗶𝗳𝘁, your application is technically "𝘥𝘰𝘸𝘯" for the user. The transition to 𝗔𝗜/𝗠𝗟 is teaching me that we aren't just monitoring 𝘀𝗲𝗿𝘃𝗲𝗿𝘀 anymore; we are monitoring 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗶𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆. Key takeaway for my fellow 𝗠𝗘𝗥𝗡 𝗱𝗲𝘃𝘀:  1. 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗢𝗽𝘀: Is the service up?   2. 𝗠𝗟𝗢𝗽𝘀: Is the prediction still accurate? I'm currently exploring how to integrate 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗱𝗿𝗶𝗳𝘁 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 into standard 𝗚𝗶𝘁𝗛𝘂𝗯 𝗔𝗰𝘁𝗶𝗼𝗻𝘀 CI/CD pipelines—the same ones I've used to ship products like 𝗩𝗲𝗿𝗶𝗳𝗶𝗲𝗱𝗫 and 𝗠𝟯𝗗. How are you handling model monitoring in your production environments? 𝗟𝗲𝘁'𝘀 𝗱𝗶𝘀𝗰𝘂𝘀𝘀! 👇 #MLOps #FullStack #SoftwareEngineering #AI #MachineLearning #WebDevelopment #SystemDesign #NodeJS #AWS #data #dataengineering