🚀 Introducing ALGO_TRACKER.AI – Bridging Machine Learning with Static Code Analysis for Python. As software systems scale, quantifying Technical Debt and maintainability becomes crucial. Traditional rules-based linters often miss the complex interplay of metrics that define genuine code risk. To address this, I built ALGO_TRACKER.AI, an intelligent auditor that moves beyond rigid rules. It leverages a trained XGBoost model to analyze static code metrics (LOC, Cyclomatic Complexity, Halstead Metrics) recursively fetched from any public Python repository via the GitHub API. The goal is simple: Provide developers and tech leads with a predictive, probability-based "Bullish" (Clean/Maintainable) or "Bearish" (High Technical Debt) rating for their codebase. Key Features: 🔹 Deep recursive scanning of Python (.py) files using GitHub’s /git/trees API. 🔹 Static Metric Extraction (Radon/Lizard) to quantify complexity. 🔹 Intelligent Risk Prediction using an optimized XGBoost classifier. Tech Stack (High Performance & Scalable): ⚛️ Frontend: React, Tailwind CSS (Deployed on Netlify) ⚡ Backend: FastAPI (Python), (Deployed on Railway) 🤖 Machine Learning: Scikit-learn & XGBoost Check out the working prototype here: https://lnkd.in/g2tVERcH #MachineLearning #SoftwareEngineering #Python #FastAPI #ReactJS #FullStack #ArtificialIntelligence #Innovation

I got tired of writing the same ML boilerplate over and over. So I built a full AutoML platform from scratch — this weekend. Here's what it does: ↳ Upload any CSV dataset ↳ Auto-detects Classification vs Regression ↳ Preprocesses data automatically (encoding, scaling, imputation) ↳ Trains 4 models with GridSearchCV hyperparameter tuning ↳ Picks the best model and explains WHY using SHAP ↳ Shows live training progress via WebSockets And it's not a Jupyter notebook or a Streamlit script. It's a proper full-stack product: ⚛️ React frontend with glassmorphism UI ⚡ FastAPI backend with REST + WebSocket API 🐳 Fully containerised with Docker Compose 🧠 scikit-learn + SHAP for ML + explainability One command to run everything: docker compose up --build This is the kind of tool I wish existed when I started in ML. Building things that solve real problems is what I love doing. #MachineLearning #Python #React #FastAPI #Docker #MLOps #OpenToWork #FullStack #DataScience

Excited to share my latest project: an AI-powered document query system Over the past few weeks, I built a full-stack application that lets users upload documents and interact with them using natural language — basically turning static files into something you can actually talk to. One thing I really focused on was getting the pipeline right. Instead of writing everything in one script, I used LangGraph to structure the RAG pipeline as a stateful workflow. This helped me clearly separate each step — document parsing, chunking, embeddings, vector search, and final response generation. The biggest advantage? It made the system way easier to debug, extend, and scale. Plus, handling more complex queries feels much cleaner when the state is properly managed. Under the hood, the system combines semantic search with LLM reasoning to return context-aware answers instead of generic responses. Tech stack I worked with: • Backend: Python, FastAPI, Uvicorn • AI/ML: LangChain, LangGraph, OpenAI • Vector DB: Qdrant • Database: PostgreSQL • Frontend: React (Vite) + Tailwind CSS • Infra: Docker & Docker Compose Would love to hear if others are experimenting with LangGraph or RAG pipelines — always open to learning and improving! Git Repo: https://lnkd.in/gUJMvxRs #AI #MachineLearning #RAG #LangChain #LangGraph #FastAPI #React #Docker #Qdrant #PostgreSQL

Spending some time deep in architecture decisions for a side project, and honestly, this is the part I love most. The stack is taking shape: 🔹 FastAPI + Python on the backend 🔹 React + Vite on the frontend 🔹 PostgreSQL with pgvector for semantic search 🔹 LlamaIndex for the RAG pipeline 🔹 Anthropic + OpenAI APIs for generation The domain is healthcare-adjacent. I'm keeping the specifics close to the chest for now, but the core challenge is interesting: how do you build a system that retrieves clinically relevant evidence, reranks it, and generates structured recommendations that a practitioner can actually trust and trace back to sources? Some of the design decisions I've been enjoying: • Separating QueryBuilder, VectorRetriever, Reranker, and PlanGenerator into composable pipeline stages • A human-in-the-loop approval flow before any recommendation goes live • Audit trails baked in from day one, not bolted on later Still a lot of road ahead, but it's the kind of project that makes you a better engineer regardless of where it lands. Building in public (ish). Happy to geek out with anyone working on RAG systems or clinical AI. 🙌 #RAG #FastAPI #pgvector #LLM #Python #AIEngineering #BuildingInPublic

🔥Built a RAG system from scratch using only local models. No cloud APIs, no hand-holding tutorials just a lot of debugging. The stack: FAISS for vector search Local embeddings (no external dependencies) Ollama running dolphin-mistral locally Custom chunking and similarity search logic The actual work was fixing problems: The tutorial version never tells you about file path issues, API failures mid-development, or why your system confidently returns completely irrelevant answers. I spent more time fixing retrieval logic than writing new code. The biggest lesson: your LLM doesn't matter if your retrieval is broken. A good retrieval system with an average model beats a great model pulling the wrong context. What it does now: Reads from a local knowledge base Retrieves contextually relevant chunks Generates answers that actually use that context Filters low-confidence matches to reduce hallucinations What's next: PDF ingestion pipeline Basic web UI Better chunking strategies #ArtificialIntelligence #RAG #LLM #Python #AI

🚀 Built Something Useful for Every Claude Developer While working with Claude Code, I realized one big gap — there’s no clear visibility into usage, tokens, or costs. So I built a solution 👇 🔗 https://lnkd.in/g7kCBnCn 💡 Claude Usage Dashboard A lightweight, local-first tool to track, analyze, and optimize your Claude usage in real-time. ✨ What it does: • Tracks token usage across sessions • Estimates API costs • Provides a clean dashboard + CLI insights • Detects anomalies & suggests optimizations • Includes a budget guard (yes, it can even stop overspending) ⚡ Best part: No setup headache. No dependencies. Just run it with Python. 🧠 Why I built this: When you're building with LLMs, visibility = control. This tool gives you exactly that. If you're working with Claude or exploring AI tools, this might help you 👇 Would love your feedback, ideas, or contributions 🙌 #AI #LLM #Claude #OpenSource #Developers #Python #BuildInPublic #GitHub

 10 Million Rows in 0.26 Seconds. Why is your Python pipeline still crawling? 🐉🚀 Standard data processing often pays a massive "Abstraction Tax." I see teams throwing expensive, high-memory AWS/Azure instances at slow Pandas pipelines just to avoid "Out of Memory" crashes. I decided to solve this at the hardware level. I built HydraCore: A native C-extension for Python that bypasses the Global Interpreter Lock (GIL) and talks directly to the metal. The Benchmark Results: ⚡ Performance: Processed 10,000,000 rows in just 0.26 seconds. 📈 Efficiency: $\approx 10.3\times$ speedup compared to standard ingestion methods. 💰 ROI: HydraCore allows massive-scale data processing on low-resource micro-instances, potentially reducing per-byte compute costs by up to 90%. The Technical Architecture: The Hydra: Parallel POSIX threading for multi-core execution. Zero-Copy: Direct mmap allocation into NumPy buffers. Native C-Engine: High-frequency ingestion with a seamless Pythonic handshake. I’m currently looking for data engineering teams or startups hitting the "Pandas Wall." If your ingestion pipelines are the bottleneck in your stack, let’s talk. I’m offering 3 free performance audits this week to show exactly where you can slash latency and infrastructure spend. Check the code and benchmarks here: 👉 https://lnkd.in/gi8rdzkM #DataEngineering #Python #CProgramming #CloudOptimization #PerformanceEngineering #HighFrequencyTrading #HydraCore #SystemsArchitecture #SoftwareEngineering

The "Future of IT" In 2026, the question isn’t if you use Python, but how many layers of your stack it powers. It has evolved far beyond a simple scripting tool into a dominant force in enterprise software. Here is where Python is winning the IT sector right now: 1)Generative AI & LLMs: While the models are complex, the orchestration is Python. Frameworks like LangChain and AutoGPT have made it the native language for building Agentic AI. 2)The Rise of FastAPI: For high-performance microservices, FastAPI has become the industry gold standard, rivaling Go and Node.js for speed and developer experience. 3)Cloud-Native Automation: Python is the "Secret Sauce" in DevOps, driving CI/CD pipelines and infrastructure as code (IaC) across AWS, Azure, and GCP. 4)Data Engineering 2.0: With the convergence of Data Science and Software Engineering, Python is the bridge between raw data in SQL and actionable insights in Power BI. Python’s "Human-First" design reduces development time by nearly 40% compared to traditional languages, allowing teams to ship faster and iterate with precision. Are you using Python for automation or innovation this year? Let's discuss! 👇 #Python #TechTrends2026 #DataEngineering #AI #SoftwareDevelopment #ITIndustry

🚀 Car price prediction ML Project – Part 3: Bringing Model to Life (Flask API + Frontend) In my previous posts, I built and trained a Machine Learning model. Now in Part 3, I focused on turning it into a real-world application using Flask and a simple frontend. 🔹 What I built: • Developed a Flask API to serve the trained ML model • Created endpoints to take user input and return predictions • Designed a basic frontend (HTML/CSS/JS) for user interaction 🔹 How it works: User Input → Frontend → Flask API → ML Model → Prediction → Display Result 🔹 Tech Stack: Python | Flask | HTML | CSS | JavaScript 🔹 Key Learning: • How to deploy ML models using APIs • Connecting frontend with backend • Handling real-time user inputs 📌 This step helped me understand how ML projects work in real-world applications. Next Part: Deployment (making it live 🚀) #MachineLearning #Flask #WebDevelopment #Python #DataScience #Projects #LearningJourney