Built hands-on POCs comparing Azure Durable Functions, Temporal, Prefect, and Dapr for orchestrating event-driven AI pipelines. Implemented the same invoice processing workflow (PDF → parse → fan-out/fan-in → aggregate) across all frameworks for an apples-to-apples comparison. Key insights on deterministic replay, multi-service architecture patterns, and how local dev experience impacts iteration speed and debugging.
This project uses lightweight models like MobileNet V3 and robust clustering algorithms (HDBSCAN and K-means) to create an efficient local image clstering solution. The implementation uses cosine similarity to compare high-dimensional image embeddings. Most importantly, it shows that modern edge devices can handle complex machine learning workflows while preserving user privacy and reducing dependency on external services.
Implement a privacy-preserving gesture recognition directly on your device using MediaPipe and OpenCV. Process gestures locally without cloud dependencies, ensuring user privacy while maintaining high performance. The solution combines powerful ML models with robust image processing for real-time analysis, all presented through an intuitive Streamlit interface.
Build an application for generating unique restaurant names using Large Language Models (LLMs) and the LangChain framework. It covers setting up the environment ...
Today's AI applications predominantly use a standard chat interface. While this approach is simple to implement, it creates unnecessary interaction overhead, limits human scalability, and fails to harness the full potential of LLMs.
MarketMind is a small demo Django-based web application that integrates with a language model agent to handle search queries about companies. The application provides a summary of the latest news and stock price for a given company using LLM-based agent.
This guide focuses on setting up Amazon Bedrock, a serverless AI service by AWS. It covers account setup, AWS CLI configuration, accessing Bedrock, and generating a model response, including a hands-on example and troubleshooting tips.
Use Amazon Transcribe to transform an audio file into text. Set up S3 triggers to automatically initiate processing when new audio files are uploaded, and setup permissions. Use the boto AWS Python SDK to interact with AWS services.
Summarize the transcribed JSON files using Amazon Bedrock's Titan model. Deploy the system using AWS Lambda, and set up S3 triggers to automatically initiate processing when new audio files are uploaded, creating an efficient solution for processing and analyzing conversations at scale.
Implement robust monitoring using CloudWatch's integration with AWS Lambda and Amazon Bedrock. Learn how to set up comprehensive logging, configure data protection policies, and create custom alarms. The solution provides complete visibility into your AI applications while maintaining security and cost control through CloudWatch's powerful monitoring capabilities.
Learn how to automate AWS resource provisioning and management using Terraform. This guide covers version-controlled infrastructure, consistent deployments, and error-free configuration.
CandleWise, a portfolio management app that I'm building as part of my exploration journey into .NET Core development. This series documents my learning process with Visual Studio, Docker, and deployment to Azure. In this guide, we'll cover setting up a development environment by installing Visual Studio 2022 and .NET Core 6.0 SDK, then creating an ASP.NET Core Web API project as our foundation....
This article This one discusses the integration of real-time stock data into an application using IHttpClientFactory, a tool that manages HttpClient instances...
The blog post details the process of deploying the app to Microsoft Azure. The process involves encapsulating the application within a Docker container, creating a Docker image of the application, and using Visual Studio tools for developing with Docker containers...
Automated my entire infrastructure using Terraform for Azure. Implemented a comprehensive CI/CD pipeline with GitHub Actions that handles my complete deployment strategy - automatically deploying the Next.js frontend to Vercel and the .NET Core backend to Azure App Service...
Game making is one of the most popular branches of programming. It is also a fun way to learn any new programming language while creating an interesting project. In this series, we will go step-by-step through implementing a simple Tic-Tac-Toe-like game using HTML, CSS and JavaScript. I will called it “Dragons vs Unicorns”. This process will allow us to cover key features of these three technologies learning while we put our skills in practice.
In the previous post we made the markup and applied some styles to our Dragons vs Unicorns game app. Now it’s time to implement the game logic in JavaScript. This part is what makes the game playful. First, we reference the html elements for accessing them more easily.
In this post, I will share my experience of enhancing the "Dragons vs Unicorns" game series integrating an AI opponent into the game, utilizing the minimax algorithm to create a challenging computer player. The post provides a detailed walkthrough of the AI integration process, demonstrating how we can elevate a simple two-player game into a more complex and engaging single-player experience.