Hey network! You might remember it (or not), but last weekend I posted about my personal projects and how I've made peace with generative AI; to the point that I've put my greatest efforts into automating my routines and making my agents my co-pilots (and even my full pit crew). Among the comments I received, some asked about the strategies I followed or my personal setup in general; while I could have shared this directly, I decided to take it a step or two further; "One command to scaffold them all, one command to brief them, one command to wire the agents and in the darkness free them." 🧙♂️ This is primer! It does in a few minutes what used to take me a full day of setup; everything AI agents need to actually be useful: the rules they read, the domain knowledge they reference, the git discipline they follow, and a tailored brief that tells them exactly where to start building. Althought it is still a WIP, this first version works fine with JS/TS stacks, but the idea is to provide a library big enough to work with the most popular architectures, always prioritizing industry (and personal) standards. For v2.0.0 I hope I can integrate the really big difference: - `primer plan`: describe a timeline and available hours, and primer generates a phased build plan with tasks ready to push to GitHub Projects. - Vue & Svelte skill packages: expanding the skill library beyond the current React/Node.js focus. What other ideas would you suggest? npm (@monomit/primer) - https://lnkd.in/eJrpwDbr primer monorepo - https://lnkd.in/eUKKJHxY #AI #DevTools #OpenSource #DeveloperExperience #Cursor

📚 Weekly Code Check-in: Keep Building! Sometimes the best coding happens in the background. While this week was quiet on the commit front, it doesn't mean I'm not thinking about the next big feature or project. 🎯 This is a good reminder that consistent progress beats perfect productivity. Every developer knows that feeling — taking time to plan, refactor, and brainstorm before the next push. 💪 My current toolkit: ✓ JavaScript & TypeScript — building fast, scalable web apps ✓ Python & ML — experimenting with AI and data ✓ React & Node.js — full-stack web development ✓ AI/ML projects — AI Mock Interview, Jarvis AI, Learning Recommender 🚀 What's next? Keep shipping, keep learning, keep improving. To everyone in their coding journey — celebrate the small wins, embrace the grind, and remember: shipping something imperfect beats shipping nothing perfect. Let's go! 💻 #DeveloperLife #CodingJourney #GitHub #FullStack #WebDevelopment #MachineLearning #StartupMindset #AlwaysLearning

#Devboard Continuing on my coding for fun project. On Sunday evening, I began a no AI for code personal campaign. I have been meaning to expand my coding skills from just web dev into core infrastructure. I took on a master program on Information technology and lowered my usage on AI for code. Now I am trying to build distributed system frol scratch up. Yeah, it's a learning curve but for this two days, I've learnt quite alot. Yesterday I built a basic log ingestion + querying system (as the v1 of my first project - an observability tool). I chose to use nextjs App router, this is to reduce stress🤣. Nextjs already provide basic extraction for me and the fact it is serveless deployment on vercel... so yeah🥲. For the development, I choose to use docket to run postgres database and I feel docker is under utilized by many web dev. why run your local machine down with heavyweight infrastructure when you can just containerized them and use docker? Well, so my setup is nextjs App Router, docker (postgres database), drizzle orm. I used the old time method, yeah documentation and google. It gives a deeper understanding. there is something about AI that gives you the illusion of being smart when infact it is a delusion. Many people that code with AI arr actually a risk to themselves and the tech space. It's becoming clear to me. they can't even read their code or tell you how it works. Well it work and so what? That's not engineering and it kills innovation and the "Pythagoras" moment. I was able to build two endpoints for ingesting logs and querying the logs. I'll be spending sometime scaling these two endpoints to be production ready to be a self hosted observability platform. The engineering challenge 1. Storing high-cardinality traces affordably 2. Sub-second query response over weeks of data 3. single-binary deploy with zero external deps yup.. let's see how it goes #build #engineering #corecoding

Syntax is a commodity, but Architecture is the differentiator—in 2026, the most successful developers aren’t just writing lines of code, they are orchestrating entire digital ecosystems where Intelligence meets Scalability. To build truly future-proof applications, I focus on the intersection of four critical pillars: crafting high-performance interfaces with React.js, embedding AI & Python for predictive logic, securing the "plumbing" via Cloud & Network Architecture, and ensuring long-term maintainability through advanced Software Logic. This "Developer’s Blueprint" ensures that every feature shipped isn't just functional, but carries real-world impact. The goal is no longer just to make it work, but to make it scale without limits. I’m curious to hear from my network: when you start a new build, do you prioritize the User Experience (Front-end) or the System Integrity (Back-end/Architecture) first? Let’s discuss here #FullStackDeveloper #AIArchitecture #CloudComputing #SoftwareEngineering #ReactJS #Python #TechInnovation #FutureOfTech #LinkedInGrowth

Every time I opened a pull request, I felt it. That awkward wait for feedback. What if the first round of review didn't have to wait at all? That question became my MEng Capstone at the University of Cincinnati. My inspiration was simple: combine the AI with traditional web development to solve a real developer pain point. So I built an AI-Powered Pull Request Reviewer, a GitHub App that automatically analyzes code the moment a PR is opened and posts intelligent feedback directly in the thread. And I did it entirely with free resources, even for deployment: React frontend on GitHub Pages FastAPI backend on Render MongoDB free tier for storage 4 AI providers (Hugging Face, Groq, OpenRouter, SambaNova) via free inference APIs, and the architecture is flexible enough to plug in many more! Biggest lesson? Real engineering happens when you work within real-world constraints and still ship something end-to-end. 🔗 Frontend (Live): https://lnkd.in/gkGF-PWE 🔗 Frontend Code: https://lnkd.in/ggJyY-Bc 🔗 Backend (Live): https://lnkd.in/gYHUkmwX 🔗 Backend Code: https://lnkd.in/guRag2ht 🔗 GitHub App: https://lnkd.in/gVZFYcWN #AI #SoftwareEngineering #GitHub #CodeReview #FastAPI #React #CapstoneProject #UniversityOfCincinnati #BuildingInPublic #Opensource #CICD

Ever stared at a massive GitHub repository and wished you could just ask it how it works? I’ve been diving deep into local AI orchestration and wanted a better way to navigate new codebases. So, I built GitChat, A Retrieval-Augmented Generation (RAG) assistant that lets you chat directly with any public GitHub repository. It dynamically clones a repo, chunks the architecture, and streams context-aware answers back to a sleek, minimalist dark-mode interface. The best part? The LLM inference runs entirely locally. Features: - Dynamic Repo Ingestion: Automatically clones, filters out heavy/binary files, and chunks code for AI ingestion. - Zero-Hallucination Guardrails: Strict prompt engineering ensures the AI only answers using the provided codebase context. - Local LLM Orchestration: Runs models (like Qwen 2.5 Coder) locally, meaning zero API costs for inference. - Real-Time Streaming UI: Seamless, non-blocking message streaming with syntax-highlighted markdown rendering. - Dynamic Sandbox Controls: Real-time adjustments for LLM Temperature, K-value context retrieval, and model selection. The Tech Stack: - Frontend & API: Next.js (App Router), Tailwind CSS - AI Engine: Vercel AI SDK v6, LangChain - Database & Vector Search: MongoDB Atlas Vector Search - Local Models: Ollama (qwen2.5-coder, nomic-embed-text) I'm really proud of the resource management and the seamless integration between the backend data pipeline and the frontend UI. GitChat is fully open-source, and I am actively welcoming contributions! I would love for you to check out the architecture, test it out, or even open a PR if you have ideas for new features. Repository & Code: https://lnkd.in/dNuBfnzZ #Nextjs #LangChain #Ollama #MongoDB #SoftwareEngineering #AI #WebDevelopment

I spent three hours debugging a production API issue last month, only to realize I was chasing a phantom bug caused by a messy local environment. That morning cost the client time and cost me my focus. I realized that if my workspace isn't predictable, my code won't be either. Now, my productivity isn't about fancy gadgets; it’s about ruthlessly removing friction. Here is my current stack: - **Terminal:** Warp + Oh My Zsh. The AI command search saves me from constant documentation hunting. - **Editor:** VS Code with Cursor. Using it for codebase-wide refactoring in our Django projects has cut my boilerplate time by 40%. - **Stack:** I stick to Next.js and FastAPI. Keeping the tech stack consistent across client projects means I spend less time context-switching and more time shipping features. - **Mindset:** "Deep work" isn't a buzzword. I block out 9 AM to 12 PM for core architecture work, phone in the other room, notifications silenced. If the environment is cluttered, the output is cluttered. I’ve learned that the best tool is the one that stays out of your way. What is one tool in your dev environment that you refuse to change? #SoftwareEngineering #Productivity #WebDevelopment #CodingLife #TechLead

Tiffin It Up V1.0 is Officially LIVE! I've been building Tiffin It Up : a full-stack home-chef marketplace. Today, I'm thrilled to announce that Version 1.0 is finally deployed and ready for the world! Over the last few weeks, I took this from a basic concept to a production-ready application. Crossing the finish line meant tackling some serious technical hurdles, but the final feature set is something I'm incredibly proud of. For the final sprint, I focused heavily on intelligence, oversight, and production stability. Here are the final features that complete the V1 experience: 1. AI-Powered Intelligence (Groq & Llama 3) 🤖 I didn't want this to be just a static marketplace. I integrated Groq Cloud's ultra-low latency inference to add real-time AI: -Customer Recommendations: The system analyzes a user's order history, spice preferences, and allergies to generate a personalized "Recommended for You" carousel. -Chef Business Insights: A native asynchronous background thread analyzes recent order volumes and subscription trends to give chefs actionable, auto-cached kitchen insights. 2. The Admin Control Center 🛡️ To manage the ecosystem, I built a dedicated, secured `/admin` portal.  -Secured via JWT role-based claims. -Provides a bird's-eye view of total revenue, active subscriptions, and user health (Chefs vs. Customers) 3. Production Deployment & Security 🌐 -Database:Successfully migrated from local SQLite to a fully managed -PostgreSQL instance on Render. -Auto-Provisioning: Implemented secure environment-variable injection to auto-provision Super Admin accounts without exposing credentials in the codebase. -CORS & Vercel: Secured the FastAPI backend to specifically accept requests from the Vercel production frontend. 🛠️ The Final Tech Stack -Frontend: React 18 (Vite), Tailwind CSS, Context API -Backend: FastAPI (Python), SQLAlchemy, PostgreSQL -Integrations: Groq Cloud SDK (AI), Cloudinary (Image CDN), WebSockets (Real-time alerts) Building this platform end-to-end has been a massive learning experience—from managing complex SQLAlchemy table joins to handling asynchronous WebSockets and AI prompt engineering. #ReactJS #FastAPI #Python #WebDevelopment #AI #Llama3 #Groq #OpenSource #FullStack

If you cloned the repo last week, you had to navigate my undocumented bugs, an unintuitive UX, and my personal info hardcoded directly into the scraping engine. 😅 That’s the reality of shipping fast. But I've spent the last few days acting on your GitHub issues and bug reports. The UX is fixed, the hardcoded data is gone, and the suite is officially stable. To make up for the initial chaos, I just dropped a comprehensive CONFIGURATION_GUIDE.md for cold-starting the tool. If you are a job seeker in the tech sector and tired of the standard job boards, this tool is built to find high-signal, relevant jobs before the masses do. Everything you need to set it up from scratch is now documented. 👇 Link to the repo is in the first comment! 👇 Question for the devs: What is the most embarrassing bug you’ve ever accidentally shipped to production? Let me know I'm not the only one who is cooked. #OpenSource #SoftwareEngineering #Python #DevOps #TechCareers

🛠️ I got tired of writing bad commit messages. And when you're learning, some concepts need more than a quick search to fully click. So, I built a tool to fix both. The idea came from two real frustrations => Stack Overflow is useful but slow, and writing meaningful commits takes more thought than it looks. ⚙️ Now let me introduce "Dev Tools", a full-stack app with: - Commit message generator: compares old and new code and writes conventional commits - Code explainer: breaks down snippets into structured, readable explanations - TypeScript Express backend with structured JSON prompting and custom hooks 🤖 The real challenge was the experimentation: - Hit 503 overload errors - Tested multiple models and learned they perform differently per task - Learned that you can control exactly how the AI structures its response. The model provides the information, you define the format through your prompt. Building something you'd actually use yourself is a different kind of motivation. 😁 🔗 Link: https://lnkd.in/e3q8KFxF #buildinpublic #developerjourney #gemini #react #typescript #nodejs