From Prompts to Processes: When AI Stops Being a Tool and Starts Being a System

The shift from prompt engineering to agent architecture isn't about job titles—it's about recognizing that intelligence requires loops, not just prompts.

The Convergence of Silicon and Statutes: Bridging the Gap Between AI Safety and Legal Alignment

Exploring the emerging framework of legal alignment as a bridge between existential AI safety and socio-technical ethics.

Deploying an app on AWS ECS with CDK

How to deploy an application on AWS ECS with load-balancing and auto-scaling using CDK by writing the CDK code in TypeScript

Testing Redux Connected React Components Using Jest

Testing React components connected to a Redux store using Jest and React Testing Library.

Running a Nuget Feed on S3 with Automated Deployments

There are many ways of creating a Nuget feed. but did you know that you can run a Nuget feed on S3 without any computing infrastructure? In this post, I will show you how you can create a Nuget feed on S3 using Sleet and automate the deployment process for packages.

Deploying a Website on S3 With SSL, Continuous Integration

In this post, I am creating a website and host it on S3 and setup a continuous integration pipeline setup using Gitlab

Creating a Re-Usable Presto Worker Image in EC2

Creating a machine image that can be used to spin up presto worker instances without configuring each one. this could be helpful when you don't want to use any orchestration tool

React Form Validation Using Custom Hooks

Form validation in react is bit tricky. This is an attempt to make it bit easy by using react hooks. here we write few custom hooks to create a re-usable validation logic that can be used in any form.

Multiple Linear Regression With Python

Regression is a machine learning model which we can use to predict values by using previously observed data. In simple linear regression, we had to use only one independent variable for the prediction. but in the real world often a dependent variable is dependent upon several variables.

Simple Linear Regression With R

Regression models are used to predict real values such as salary, spending, income. Simple linear regression is a model of regression which is used to identify the correlation between two variables and possibly predict the dependent variable by using the independent variable.This will enable us to establish a relationship between two attributes such as Income and Spending and we can use what we know about the relationship to forecast unobserved values.