🚀 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 built a programming language for Ai Agents from scratch. AI agents burn through tokens writing Python. 70% of every API call is boilerplate. That costs real money — and it's getting worse. So I built Kode — a language where AI agents write 48% less code for the same result. The architecture: → TypeScript interpreter (lexer → parser → AST → evaluator) → 143KB single-file bundle → 36 keywords averaging 2.3 characters (Python: 5.1) → TOON data format — 30-45% fewer tokens than JSON What's built in (no dependencies): → Multi-agent system with message passing → 4-type cognitive memory (working, semantic, episodic, procedural) → State machines for workflow tracking → Self-healing code with auto-retry → Web server + SQLite database → 35+ stdlib modules → Built-in testing framework Benchmarked across 5 real tasks: Python: 1,740 tokens → Kode: 905 tokens (-48%) JavaScript: 1,410 tokens → Kode: 905 tokens (-36%) 179 tests passing. Published on npm. MCP server so AI agents can discover it. ~8,000 lines of TypeScript. Open source. If you're building AI agents, try it and tell me what breaks. GitHub: https://lnkd.in/gWSAr8Tt Website: kode-website.vercel.app #Kode #ProgrammingLanguage #AIAgents #OpenSource #SoftwareEngineering #BuildInPublic #Ai

🚀 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

What if your portfolio could talk back? I built an AI agent that represents me on my website — answers questions about my background, captures leads, and logs anything it doesn't know. All powered by Gemini 2.5 Flash + tool calling. No framework. Pure Python. The two tools it uses: 🔧 𝐫𝐞𝐜𝐨𝐫𝐝_𝐮𝐬𝐞𝐫_𝐝𝐞𝐭𝐚𝐢𝐥𝐬 — captures name + email when someone's interested 🔧 𝐫𝐞𝐜𝐨𝐫𝐝_𝐮𝐧𝐤𝐧𝐨𝐰𝐧_𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 — logs gaps so I can improve it over time Every lead, every unknown question → instant push notification to my laptop/phone via ntfy.sh. The whole agentic loop is just ~50 lines of Python: • Build messages array • Call LLM • If tool_calls → execute → feed back • Repeat until done Frameworks abstract this. But writing it raw makes you actually understand what's happening under the hood. GitHub → https://lnkd.in/eB2VwqDs This is step 1 of building my Personal Concierge Agent in public. Step 2: rebuilding this as a full Next.js + FastAPI web app — proper UI, real deployment. Follow along if you're into agentic AI, Python, and building real things — not just demos. #AgenticAI #Python #BuildingInPublic #LLM #Gemini

🚀 FastAPI vs REST API — What’s the Difference? Many developers confuse FastAPI with REST API, but they are not the same thing 👇 🔹 REST API (Architectural Style) REST (Representational State Transfer) is a design pattern for building APIs. It defines how clients and servers communicate over HTTP using methods like GET, POST, PUT, DELETE. ✔️ Language-agnostic ✔️ Widely adopted standard ✔️ Focuses on structure & principles 🔹 FastAPI (Framework) FastAPI is a modern Python framework used to build APIs, often following REST principles. ✔️ Built with Python 🐍 ✔️ High performance (comparable to Node.js & Go) ✔️ Automatic API docs (Swagger UI) ✔️ Async support out of the box ✔️ Data validation using Pydantic ⚖️ Key Difference 👉 REST is how you design APIs 👉 FastAPI is a tool to implement APIs 💡 In Simple Terms: You can build a REST API using FastAPI, Django, Express, or any framework — FastAPI is just one of the fastest and most developer-friendly options today. 🔥 When to Choose FastAPI? - Building high-performance APIs - Working with Python ecosystem - Need auto docs & validation - Creating AI/ML backend services 📌 Final Thought: REST gives you the blueprint 🏗️ FastAPI helps you build it faster ⚡ #FastAPI #RESTAPI #Python #WebDevelopment #BackendDevelopment #API #SoftwareEngineering #Coding #Developers #Tech

Built an Advanced Personal Assistant from scratch. Here's what it actually does. Started with a blank Next.js project and a FastAPI skeleton. The result is Ava — an AI assistant that reasons, remembers, and acts across sessions. The stack: Next.js 16 · FastAPI · SQLite · Groq API · Python Groq handles inference at blazing speed. Everything else — memory, plugins, sessions, file operations — runs on your own machine. ● Agentic tool calling: The LLM doesn't just respond — it decides. Every message goes through an orchestration loop that determines whether to answer directly or invoke a tool. Weather, time zones, calculations, web search, GitHub stats, crypto prices — all fire as live tool calls with transparent execution blocks in the UI. ● Multi-model fallback cascade: If the primary model hits rate limits, the system silently falls back through a chain of models without breaking the conversation. The user never sees an error. ● Code execution: Ava writes Python, runs it in a sandboxed subprocess, reads the output, fixes errors, and iterates — all in a single turn. The full execution trace is visible inline . ● Persistent memory: After every conversation, a background extraction pass pulls facts, preferences, and events into a structured vault. Location, tech stack, habits — remembered across sessions without any manual tagging. ● Voice and Vision: Push-to-talk via MediaRecorder piped to Groq Whisper for transcription. Image upload routes to a vision model for analysis, OCR, and structured extraction. ● Dynamic plugin system: Install and uninstall tools at runtime. Register a custom skill by uploading a markdown file — the parser extracts the schema and makes it callable immediately, no backend changes required. ● Session archive: Every conversation is stored and browsable. Restore any past session back into the live chat with one click. The hardest parts were never the features themselves. They were the details — preventing tool call JSON from truncating mid-generation, stripping internal reasoning tokens before they reach the UI, making a free tier feel unlimited through intelligent model routing. The gap between a working demo and a reliable product is where most AI projects fall apart. This one doesn't. Happy to go deep on any part of the architecture in the comments. #llm #nextjs #fastapi #python #ai #groq #softwaredevelopment #webdevelopment

Bridging the Gap Between Web Development and AI Architecture Most tutorials show you how to call an LLM API. But in production, "just calling an API" is a recipe for a crashed server and a frustrated user. To wrap up our recent Python & Django bootcamp with Django Rwanda, I delivered a final session on building Production-Ready RAG (Retrieval-Augmented Generation) Systems. I’m sharing the full presentation today to help both beginners and experienced engineers understand that RAG isn't just about AI, it’s about a reliable data pipeline. Key takeaways included in the guide: - The Worker Pattern: Why you should never run AI logic inside a standard Django view (and how Celery saves your UX). - Observability as a Priority: Using the Django Admin as a "Command Center" to make invisible ingestion errors visible. - Beyond Simple Search: Why Hybrid Search (Vector + Keyword) is the standard for accuracy. - The Re-ranking Layer: How to move from simple "mathematical similarity" to true contextual relevance. - Whether you are just starting with Python or you are a Senior Architect looking to integrate LLMs into your stack, I hope this provides a clear mental model for your next project. #DjangoRwanda #PythonRwanda #AI #SoftwareArchitecture #RAG #DjangoRwanda #SoftwareEngineering

 Bridging the gap between Machine Learning and Production: An Uncertainty-Aware Forecasting System 🌤️ Most weather apps give you a single deterministic number. But in the real world, data is rarely 100% certain. I’ve spent the last few weeks building a weather forecasting system that doesn't just predict the temperature—it communicates confidence ranges and handles real-time environmental data. Key Engineering Highlights: 🔹 Machine Learning: Uses an XGBoost Regressor for recursive 7-day forecasting, with dynamic uncertainty calibration (95% confidence intervals). 🔹 Live Data Anchoring: Integrated the Open-Meteo API to ensure forecasts are anchored to real-world "Day 0" conditions. 🔹 Modern Stack: Built a decoupled architecture using FastAPI (Python) for the logic and React + Tailwind CSS for a premium, dark-mode UI. 🔹 DevOps & Deployment: Fully containerized using Docker & Docker Compose for seamless environment management. Moving from monolithic Python scripts to a modern, containerized Full-Stack architecture was a massive learning experience in system design and dependency management. Check out the full source code and documentation in the comments below! 👇 #MachineLearning #ReactJS #Python #FastAPI #Docker #FullStack #BuildingInPublic #CSStudent #DataScience

Built a full AI Agent system on Django. Tool System, Agent Loop, Multi-Agent, Streaming, and RAG — single project, unified architecture. 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀: → Agent Loop: A while loop that checks whether the LLM returned a function call or a text response. If function call — execute the tool, append the result to context, send it back to the LLM. Repeat until it returns text. → Tool System: Strategy Pattern on top of a BaseTool abstract class. Each tool implements name, description, parameters, and execute(). ToolRegistry handles central registration — adding a new tool = 1 class + 1 line of register. → Multi-Agent: Inter-agent communication layer. Researcher agent gathers data, Validator agent verifies, Reporter agent formats the output. Each agent runs independently with its own system prompt and tool set. → Streaming: Token-level real-time delivery via SSE (Server-Sent Events). StreamingHttpResponse on the Django side, EventSource on the frontend. → RAG Pipeline: Chunk documents, convert to embeddings, index in a vector DB. On user query, run similarity search to pull the most relevant chunks and inject them as context to the LLM. → Memory: Persistent conversation history via Conversation & Message models. The agent carries prior context into the LLM's context window. Stack: Django + DRF / Gemini API Function Calling / SQLite + Vector DB #AIAgents #Django #Python #LLM #RAG #Gemini #MultiAgent

Have you ever stared at a massive PDF or textbook and thought, "I wish I could just ask this book a question"? 🤔 That exact problem led me to build BookBuddy, my personal AI reading assistant, and today I’m making it open-source! I wanted a tool that didn't just understand documents, but could manage multiple books at once, keep their contexts perfectly isolated, and—most importantly—keep my personal data completely private. With BookBuddy, you can: ✅ Upload multiple PDFs and switch between them using a sleek, tabbed UI. ✅ Maintain separate, isolated chat memories for each document. ✅ Run everything 100% locally. No API keys, no cloud servers, total privacy. I built the backend using Python, FastAPI, and LangChain, utilising ChromaDB for vector storage and Ollama (Llama 3) for local inference. The frontend is a custom-designed React application built for speed and aesthetics. I’ve structured the repo so anyone can clone and launch the entire full-stack application with just one `make run` command. 🔗 Check out the repository here: https://lnkd.in/dKT8MpTQ If you give it a try, let me know! Feedback and contributions are always welcome. 🚀 #SoftwareEngineering #AI #LocalLLM #WebDevelopment #React #FastAPI #LangChain #Python