Every developer has that one daily task they secretly despise. For me, it wasn’t writing docs or fixing bugs—it was the daily “swipe-in” on our HR portal, let’s call it “Gx-HR” (you know the one). Here is the irony: I physically go to the office every single day. I am there. But remembering to open the app and click “Sign In” at 10:00 AM? That is a mental load I simply couldn’t carry. ...
A high-energy, real-time quiz application designed for live competitions. This full-stack project enables users to create quizzes, host game sessions, and compete with friends in real-time, featuring instant scoring and dynamic leaderboards. Key Features Live Multiplayer Sessions: Join games instantly using a unique 6-digit PIN. Real-Time Interaction: WebSocket-powered communication for sub-second latency. Dynamic Scoring: Points awarded based on speed and accuracy, with streak bonuses. Host Controls: comprehensive dashboard for managing players and game flow. Responsive Design: Seamless experience across desktop and mobile devices. Architecture The system is built on a microservices-inspired architecture to ensure scalability and performance: ...
I recently built a Real-Time Quiz App (check it out live at quiz.utkarshjoshi.com), and while the coding part was fun, the deployment was a whole other beast. I wanted a professional-grade CI/CD pipeline, but I had one strict constraint: it had to be 100% free. This is the story of how I hacked together AWS Free Tier, Namecheap, Cloudflare, and GitHub Actions to build a robust deployment pipeline without spending a dime. ...
A deep dive into building Citations, a TikTok-style research paper discovery platform that transforms dense academic content into addictive, swipeable experiences using cutting-edge agentic AI workflows. The Problem: Research Discovery is Broken Academic research discovery suffers from a fundamental UX problem. Traditional platforms like arXiv, PubMed, and Google Scholar present research papers as dense, text-heavy lists that overwhelm users. The barrier to entry is too high - researchers need to parse through technical abstracts, understand complex terminology, and invest significant time just to determine if a paper is relevant. ...
A powerful Python framework for building and deploying AI agents that automate complex business processes. Combines the latest advances in AI with practical automation needs. Key Features Modular Agent Architecture: Build custom agents for specific tasks Natural Language Processing: Process and understand unstructured data Task Orchestration: Coordinate multiple agents for complex workflows Monitoring & Analytics: Track agent performance and efficiency Easy Integration: Simple API for integrating with existing systems Technical Stack Python 3.9+ Flask LangChain OpenAI GPT PostgreSQL Redis for caching Docker for containerization Impact 70% reduction in manual processing time 85% accuracy in automated decision making Significant cost savings in operational processes Code Example from ai_agent_framework import Agent, Workflow # Define a custom agent class DocumentProcessor(Agent): def __init__(self): super().__init__() self.model = self.load_model("gpt-4") async def process(self, document): # Extract key information extracted_data = await self.model.extract(document) # Validate and transform return self.validate_and_transform(extracted_data) # Create a workflow workflow = Workflow("Document Processing") workflow.add_agent(DocumentProcessor()) workflow.add_agent(DataValidator()) workflow.add_agent(ReportGenerator()) # Execute the workflow result = await workflow.execute(document_path) Project Structure ├── src/ │ ├── agents/ # Agent implementations │ ├── core/ # Core framework functionality │ ├── models/ # AI model integrations │ ├── workflows/ # Workflow definitions │ └── utils/ # Utility functions ├── tests/ # Test suite ├── examples/ # Example implementations └── docs/ # Documentation Getting Started Install the framework: pip install ai-agent-framework Create your first agent: from ai_agent_framework import Agent class MyAgent(Agent): async def process(self, input_data): # Your agent logic here return processed_data Start building your automation workflows! Documentation For detailed documentation, visit our GitHub repository. ...