Moving beyond Chatbots: Building a Grounded Inference Engine with Spring Boot & Gemini I’ve spent the last few weeks diving deep into the architecture of Inference-Driven Engines, moving past simple prompt-response loops to building something truly "grounded". I wanted to see how far I could push Retrieval-Augmented Generation (RAG) using a local-first approach for data privacy. The Tech Stack: Backend: Spring Boot (Java) for robust orchestration. AI: Google Gemini 3 Flash for high-speed, high-context reasoning. Frontend: React + TypeScript with Tailwind CSS for a professional diagnostic dashboard. Processing: Custom PDF parsing and local context injection to ensure the AI stays within "domain-specific" guardrails. What I solved for: Contextual Grounding: The system doesn't just "guess." It analyzes a 26k+ character local source of truth before providing insights. Gap Analysis: Instead of a binary "correct/incorrect" output, the engine performs Inference Diagnosis—identifying specific structural logic gaps in a user's reasoning. Performance Optimization: Balancing the latency of deep-context RAG calls with a responsive, modern UI. As an IT Product Manager with an engineering heart, it’s been incredibly rewarding to build the "Walking Skeleton" of an adaptive learning system from scratch. It’s a reminder that AI is most powerful when it’s constrained by the right architecture. #AI #Java #SpringBoot #ReactJS #AgileLeadership #RAG #GenerativeAI

🧵 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

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

Someone published Claude Code's entire brain on the npm registry. 59.8 MB. 1,900 TypeScript files. 512,000+ lines of code. That's not a prototype — that's a fully-furnished OS for AI agents. React. Ink for terminal UI. 40 built-in tools. 50 slash commands. A security researcher (Chaofan Shou) pulled apart the sourcemap and found what I suspected: multi-agent swarm orchestration. Production-grade. Already shipping. Not in a lab, not in a whitepaper — in the wild, executable, and now publicly auditable. Then there's the list of 44 unreleased feature flags. Kairos. Persistent memory. Team memory. Voice input. A coordinator mode. These aren't incremental upgrades. They're the next orbit of fully autonomous AI — the architecture that turns "agentic AI" from a buzzword into something you actually ship on Monday morning. Here's the uncomfortable part: that architecture is now a commodity. Two-person startup? Nation-state? Research team with a GitHub account and an angle? Anyone with an API key can download it and start building. The 44 features people were watching from behind closed doors are sitting in a public repo with 1,100+ stars and climbing. This wasn't a breach. It was a forced open-source release of production-grade multi-agent architecture. The real disruption isn't the leak itself — it's what happens when every technically capable founder downloads it and starts building. What happens to AI startups whose entire pitch was "we do agentic orchestration" when the reference implementation is a free download? I don't have a clean answer. But then again, neither did the axios team. GitHub (leaked source): https://lnkd.in/dzE3nNgG Full analysis: https://lnkd.in/dXvKXjWB

Stop debugging your AI Agents in the dark. Building with LangGraph is powerful, but once you add parallel nodes and cycles, the "black box" problem becomes real. Staring at terminal logs to find a state mutation error is a massive productivity killer. I built the Agent Debugger & Visualizer to solve this. It is a real-time observability layer that lets you step inside the agent's brain and change its mind mid-execution. Engineering Highlights: Human-in-the-Loop (HITL): Pause any node, manually edit the state JSON in the UI, and hit "Resume" to steer the agent in a new direction. State Diffs (RFC 6902): No more digging through 500-line JSON blobs. I’m tracking deltas so you see exactly what changed after each node execution. Real-time Cost Attribution: Every node tracks its own token usage and dollar cost in real-time. Async Critic Scoring: A background LLM scores agent reasoning without blocking the main execution loop. Tech Stack: Frontend: Next.js 14 & React Flow (Vercel) Backend: FastAPI & LangGraph (Render) Streaming: Redis Streams for low-latency trace delivery. Check out the demo and code: 🎥 Video Demo: https://lnkd.in/ecvxMDVx 🚀 Live Tool: https://lnkd.in/eE_AcsWD 💻 GitHub: https://lnkd.in/e9pUCHvw I am currently looking for my next challenge in the AI and Software Engineering space. If you are building agentic workflows and want to talk about observability or distributed systems, let’s connect! #AI #LangGraph #Python #SoftwareEngineering #OpenSource #LLMOps #NextJS #FastAPI

Since I started using Claude Code, I make fewer commits. And yet, I’m more productive. I compared my GitHub stats over 15 months: before (January to October 2025) vs after (November 2025 to March 2026). Here’s what changed, on average per month: → -25% PRs → -23% commits → +107% lines of code produced → +81% files touched In other words, each commit became much denser. Roughly 3x more substantial than before. And quality-wise, the signal is interesting too: → Code review change requests dropped by 40% → The approved / changes requested ratio went from 3.8:1 to 6.6:1 So the takeaway is pretty clear: I ship less often, but I ship more complete changes, cleaner changes, and more of them are right on the first pass. And it’s not just visible in the numbers. You can feel it in day-to-day interactions too. In my last 1:1, there was no more: “it still needs a bit more polish” A small detail, but a telling one. That’s probably the most interesting thing I’ve learned about AI in a dev workflow: AI doesn’t just help you move faster. It helps you go further on each task. Fewer iterative micro-commits. More cross-cutting, better-structured changes. Less of: “ah, I forgot that edge case” More code that passes review cleanly. To me, this isn’t a crutch. It’s an amplifier. As long as you stay in control of what it produces. Honest nuance: the “after” period covers 5 months, while the “before” period covers 10, so the sample isn’t perfectly balanced. And the “before” period also included some very active months. Still, the code review quality signal feels hard to ignore. Have you measured the impact of AI on your dev workflow? Curious to see whether others are observing the same kind of shift. #AI #ClaudeCode #SoftwareDevelopment #Productivity #CodeReview #Laravel #PHP #SoftwareEngineering

#Anthropic’s Claude Code Source Just Leaked on npm, and It Exposed Their Entire Product Roadmap Version 2.1.88 shipped with a massive sourcemap file that revealed 512,000+ lines of internal TypeScript, including unreleased features like: • Buddy: Tamagotchi-style AI companion with gacha mechanics and engagement loops • KAIROS: Always-on proactive agent with memory and notifications • Dream System: Background memory consolidation engine • Coordinator Mode: Multi-agent orchestration and verification • ULTRAPLAN: Remote planning sessions with human approval The irony? The code even included an “Undercover Mode” designed to prevent leaking internal details. Anthropic called it a simple packaging error. No customer data exposed — but the competitive intel is now public. PM Takeaway: 
Build velocity is great. Release hygiene and IP protection are non-negotiable. One missed .npmignore or sourcemap can hand your architecture and future roadmap to the world. Security and packaging discipline belong in every PM’s Definition of Done. Have you ever seen (or prevented) a release that exposed too much? #ProductManagement #AI #ProductStrategy #Anthropic #Claude #DevOps https://lnkd.in/gdRN8sjs

Why I never built in public, until now! After years of silent building projects, I realized something uncomfortable: nobody claps for the engineer who hides their work. So, this week, I started building a LinkedIn MCP Server completely from scratch. No frameworks. No shortcuts. Just honest iteration. Here’s what I learned (and failed at) in Week 1: → Misjudged the architecture had to rework 3 components after a day of debugging → Was afraid to ask for feedback, which cost me 2 hours fixing mistakes alone → Always underestimated the time needed for robust logging (turned out to be 6 hours, not 1) → Realized my obsession with “perfect code” was slowing progress more than any technical blocker What surprised me most: building in public is more difficult and more valuable than writing code in a vacuum. Progress feels slower, but the learning hits harder when you share mistakes and own your failures. Next week: API layer and the first AI pipeline integration. I’ll be honest about the decisions, shortcuts, and regrets. What’s one thing you regret NOT sharing about your last project? #BuildInPublic #AI #SystemDesign #NodeJS #BackendEngineering

>>> Built a RAG-powered AI App that converts YouTube videos into a Article I recently worked on a project where I combined LLMs + LangChain to build an end-to-end pipeline that transforms a simple YouTube link into a production-ready frontend website. 🔹 What it does: • Takes a YouTube video URL as input • Extracts transcript automatically • Converts it into a structured technical article • Generates complete HTML, CSS, and JavaScript files • Provides a downloadable website + live preview 🔹 Key Highlights: • Smart summarization for both short & long videos • Recursive chunking for handling large transcripts • Clean, Medium-style UI with responsive design • Dark/Light theme support • Built using Streamlit for quick interaction 🔹 Tech Stack: Python | Streamlit | LangChain | Google Gemini | RAG This project helped me deeply understand how to: ✔️ Design scalable LLM pipelines ✔️ Handle long-context summarization ✔️ Generate structured outputs from unstructured data ✔️ Build real-world AI applications 🔗 GitHub Repository: https://lnkd.in/g6CUREjJ 📌 Next Steps: Planning to extend this with vector databases, SEO optimization, and multi-language support. #AI #GenerativeAI #RAG #LangChain #Streamlit #LLM #WebDevelopment #Python #MachineLearning #BuildInPublic #Innomatics

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