𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝗠𝗲𝘀𝘀𝘆 𝗗𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗩𝗶𝘀𝘂𝗮𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 📈 Handling raw sales data is time-consuming and complicated. I built 𝗘𝘅𝗚𝗿𝗼𝘄𝘁𝗵 to bridge that gap; a full-stack web app that 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝘀 𝗿𝗮𝘄 𝗖𝗦𝗩/𝗘𝘅𝗰𝗲𝗹 𝗳𝗶𝗹𝗲𝘀 𝗶𝗻𝘁𝗼 𝗶𝗻𝘀𝘁𝗮𝗻𝘁, 𝘃𝗶𝘀𝘂𝗮𝗹 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. 𝗪𝗵𝗮𝘁 𝗘𝘅𝗚𝗿𝗼𝘄𝘁𝗵 𝗵𝗮𝗻𝗱𝗹𝗲𝘀: 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗞𝗣𝗜𝘀: Revenue, Profit, and Average Order Value (AOV). 𝗗𝗲𝗲𝗽 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Performance by Product Category, Region, and Top Customers. 𝗧𝗿𝗲𝗻𝗱 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴: Monthly charts for sales and customer acquisition. 𝗦𝗲𝗰𝘂𝗿𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: File history with interactive previews and cloud deletion. 𝗧𝗵𝗲 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗕𝗮𝗰𝗸𝗯𝗼𝗻𝗲: I prioritized a modular architecture using the Service Layer Pattern to keep logic decoupled from the UI. 𝗦𝘁𝗮𝗰𝗸: Python, Django 6.0.4, Pandas, and NumPy. 𝗙𝘂𝘇𝘇𝘆 𝗠𝗮𝘁𝗰𝗵𝗶𝗻𝗴: Integrated RapidFuzz to intelligently map inconsistent column headers. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: Supabase (Postgres & Storage) with local fallback for resilience. 𝗧𝗵𝗲 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀: This project is a complete V1 overhaul of a Flask prototype I built in my first year. I migrated to Django to implement JSON caching for near-instant dashboard loads. To move faster, I used AI as a "technical partner" to troubleshoot edge cases while I maintained control over the architecture. 𝗕𝗼𝗻𝘂𝘀 𝗦𝗸𝗶𝗹𝗹: This was also my first time editing a software demo video! It was a great challenge to align the visual flow with the app's logic. It’s not perfect, but it was a massive deep dive into building cloud-native data pipelines. 𝗜'𝗱 𝗹𝗼𝘃𝗲 𝘆𝗼𝘂𝗿 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸! 🔗 𝗚𝗶𝘁𝗛𝘂𝗯:https://lnkd.in/dAtZu2MV 🔗 𝗣𝗿𝗼𝗳𝗶𝗹𝗲: https://lnkd.in/d-vXp97X #Python #Django #DataScience #FullStack #ExGrowth #BuildInPublic #ExGrowth #SoftwareEngineering #WebDevelopment #Supabase

Let’s talk about something fun and interesting I did quite a while ago. I optimized a keyword-driven query system, focusing on improving throughput and stability under constraints. The core problem: Maximize queries/hour while avoiding conflicts, throttling, and system instability. Key optimizations: • Parallel processing with controlled concurrency • Keyword-based query pipeline for structured input distribution • User-agent rotation to distribute request patterns • Retry + backoff mechanisms for handling transient failures • Idempotent execution to avoid duplicate processing One interesting tweak that made a noticeable difference: I introduced a keyword expansion strategy - combining each keyword with incremental alphabet variations (e.g., keyword + a, keyword + b, ...). This helped: • Increase result coverage without changing the core keyword set • Avoid repetitive query patterns • Improve overall discovery efficiency per keyword After multiple iterations, the system stabilized at ~70 leads/hour from about ~15–20 leads/hour with consistent performance. This was one of the most interesting things I had worked on, may not be as flashy but interesting for sure that such a small change can have such a great impact! Curious to know your thoughts! #Optimizations #Python #Software #SaaS

🚀 Why FastAPI is Taking Over Modern Backend Development If you're building APIs in 2026 and still not using FastAPI… you're missing out. Here’s why developers are rapidly switching: ⚡ Blazing Fast Performance Built on ASGI, FastAPI rivals Node.js and Go in speed — perfect for high-performance applications. 🧠 Automatic Documentation Out-of-the-box interactive docs with Swagger & ReDoc. No extra effort. Just write code → get docs instantly. 🔒 Type Safety = Fewer Bugs Powered by Python type hints, FastAPI validates requests & responses automatically. 📦 Developer Productivity Less boilerplate. Cleaner code. Faster development cycles. 🔌 Perfect for AI & Data Apps Seamlessly integrates with ML models, making it a go-to for AI-driven products. 💡 Use Cases: • Microservices • AI/ML APIs • Real-time data systems • Backend for web/mobile apps 📌 Bottom Line: FastAPI isn’t just another framework — it’s a productivity multiplier for modern engineers. Are you still using Flask/Django for APIs, or have you switched to FastAPI? #FastAPI #Python #WebDevelopment #Backend #APIDevelopment #AI #MachineLearning #SoftwareEngineering #Developers #TechTrends

Your FastAPI backend is fast to build. But is it fast to run? Most developers find out the answer at the worst possible moment when real users hit it at the same time. Endpoints slow down. Requests pile up. Users drop off. Not because the code is wrong. Because it is blocking. Here is what blocking actually looks like in production: Your user hits an endpoint. FastAPI calls the database. That query takes 200ms. During those 200ms your server is frozen. Not slow. Frozen. Every other request sits in a queue waiting for that one query to finish. 100 users hit your API at the same time. User 1 gets served. Users 2 to 100 wait in line. That is sync. That is blocking I/O. FastAPI was built to never work that way. With async/await while your database query runs in the background, your server is already picking up the next request. And the next. And the next. 200ms of database wait becomes invisible to every other user. In real backend terms. SYNC — blocks: def get_orders(user_id: int): return db.query(user_id) ASYNC — non blocking: async def get_orders(user_id: int): return await db.query(user_id) Same logic. Same database. Same server. But now 100 users get served in the time it used to take to serve 1. This matters even more when your endpoints call external services. 1. Payment gateway 300ms wait. 2. AI model response 2 to 3 seconds wait. 3. Email service 500ms wait. Sync every user feels every millisecond of every one of those waits. with Async none of them do. FastAPI gives you non-blocking I/O natively. No extra setup. No plugins. No workarounds. Just write async. Add await. Let FastAPI handle the rest. Your backend was already fast to build. Now make it fast to run. Are you using async endpoints in your FastAPI projects? 👇 #FastAPI #Python #BackendDevelopment #AsyncProgramming #SoftwareEngineering #APIDesign #PythonDeveloper #WebDevelopment #TechIn2026 #BuildInPublic

🚀 Vision AI demo with Google’s Gemma 4 Over the past few days since its release, I’ve been exploring the vision capabilities of Gemma 4. I’m sharing a Streamlit web app where you can upload images and ask natural language questions about them — all running locally via Ollama. ✨ What it does • Analyze any image (JPG, PNG, GIF, etc.) • Ask questions in natural language • Get AI-powered answers using gemma4:e2b (2B, 4-bit quantized) • Runs entirely on your machine — no cloud APIs 🛠️ Tech stack • Python • Streamlit • Ollama • Gemma 4 💻 The project includes: • Web UI (Streamlit) • CLI demo • VS Code debug setup 👉 Check it out: https://lnkd.in/dtegduUc #Gemma4 #AI #MachineLearning #ComputerVision #LocalAI #Ollama #Streamlit #Python #OpenSource

🚀 rst-queue v0.1.6: Scaling Terabytes with Megabytes In a world of bloated data systems, we often find ourselves throwing more hardware at software problems. But what if our tools were engineered to be small, grounded, and incredibly powerful? Introducing rst-queue v0.1.6, a high-performance async queue system built for the modern developer who values efficiency above all else. Inspired by the psychology of the Leafcutter Ant, this project is the first major release from the Datarn initiative. Why rst-queue? Most Python-based queues are limited by the Global Interpreter Lock (GIL) and high memory overhead. rst-queue is different. By using Rust and the Crossbeam framework, we’ve built a system that: ⚡ Bypasses the GIL: Achieve true parallelism with native Rust worker pools. 🐜 Microscopic Footprint: 30-50x less memory usage than traditional message brokers. 🛡️ Dual Modes: Choose between AsyncQueue (In-memory for 1M+ items/sec) or the new AsyncPersistenceQueue (Durable storage with Sled KV). Grounded in the Kernel The secret to our speed is "Simple OS Layering." We’ve designed rst-queue to sit as close to the OS kernel as possible, utilizing direct system calls and memory-mapped I/O. This isn't just a library; it's a high-velocity data crossing (Taran) for your most critical applications. Get Started in Seconds We believe in zero-setup excellence. You can add high-performance queuing to your Python project with a single command: Bash pip install rst-queue==0.1.6 Join the Datarn Movement At Datarn, we are building a suite of "Small but Mighty" tools for data-intensive domains like B2B e-commerce and real-time analytics. rst-queue is just the beginning. Explore the project on PyPI: https://lnkd.in/d54yqdea Contribute on GitHub: https://lnkd.in/d_x3E-zj #Python #RustLang #DataEngineering #OpenSource #Efficiency #Datarn #PerformanceOptimization #SoftwareArchitecture

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

I've been building TerpNav's backend without leaning on AI, and it's been significantly harder. That was the point. I migrated from Flask to Django and built the backend using Django REST Framework. What I have right now: clean URL routing, API endpoints serving data, API keys saved in .env files and a project structure I understand end to end because I built it line by line. What I don't have yet: a real database, authentication, rate limiting, HTTPS config, or environment-based secrets management. The data currently lives in flat JSON files. That's the honest state of the project. But here's what I've mapped out next and actually understand well enough to implement: PostgreSQL with Django models (replacing the JSON files), Token authentication via DRF, Rate limiting with django-ratelimit, Secrets managed through environment variables, deployed behind HTTPS The hardest part isn't the code. It's slowing down enough to understand what I'm actually building. Every concept that AI used to abstract away is something I now have to research, break, and fix myself. That's the trade-off I made. It's worth it. Code is available on my gitHub #Django #Python #FullStackDevelopment #TerpNav #BuildInPublic

Data extraction from modern enterprise websites in 2026 is officially an extreme sport. 🧗♂️ If you’ve tried to pull data from large-scale hospitality or e-commerce systems lately, you’ve likely slammed into a brick wall. The "Standard Stack" (Python Requests + BeautifulSoup) just isn't cutting it anymore. You're probably seeing: ❌ 403 Forbidden errors on the first attempt. ❌ TLS Fingerprinting that identifies your script in milliseconds. ❌ IP Bans after fewer than 5 requests. ❌ Anti-bot walls feel impossible to scale. Standard headers aren't enough when the server is looking at your JA3 fingerprint and HTTP/2 settings. The Good News? There is a way through. 🛠️ Over the past few weeks, I’ve been reverse-engineering to understand the process. I’ve built a production-grade, asynchronous system specifically for hotel booking APIs that uses a methodology to extract the data by providing the required assessment. In my next post, I’ll dive into the exact architecture and the specific Python libraries I’m using to build the system. What’s the toughest challenge you’ve faced down recently? Let's swap war stories in the comments. 👇 #WebScraping #DataEngineering #Python #Backend #SoftwareDevelopment #APIs #SunnyJaiswal

#Day_24/100: Last day of polish on HERVEX. Here's what changed from my original vision. 14 project days ago I started with a simple idea: build an API where you give an AI a goal and it figures out how to accomplish it. The idea didn't change. Almost everything else did. I was going to use LangChain from day one. I skipped it entirely — I wanted to understand every layer before any framework hid it from me. The planner, executor, memory, and aggregator are all custom. I was going to use PostgreSQL. I chose MongoDB — agent outputs are unstructured by nature and forcing a rigid schema would have meant migrations every phase. I was going to call it "Autonomous AI Agent API". I renamed it HERVEX — derived from my own name, Heritage — built to sound like something that executes precisely and without hesitation. The frontend didn't happen. That's not failure — that's scope discipline. What HERVEX v1.0.0 actually is: You submit a goal. It plans, executes, searches the web, reasons with memory, and returns one complete result. No hand-holding. Stack: Python · FastAPI · Groq · Celery · Redis · MongoDB · Tavily What building this taught me: → Architecture decisions made early are the ones you live with longest → Scope discipline is not failure → Building in public creates accountability you can't manufacture → Understanding a system deeply before abstracting it makes you a better engineer HERVEX is done. On to the next build.🚀 #BuildingInPublic #AgenticAI #Python #FastAPI #BackendEngineering #HERVEX #100DaysOfCode #ProjectDay14