Mastering Contexts in Go

In Go, Contexts provide a standard way to pass metadata and control signals between goroutines. They are mainly used to manage task execution time, data passing, and operation cancellation. This article covers different types of contexts in Go and examples of how to use them. Introduction to Contexts Contexts in Go are represented by the … Read more

When and When Not to Use Apache Kafka as a Database

Well, no. Apache Kafka isn’t a database. It’s a real-time event streaming platform. However, Kafka’s ability to retain data in a durable and replicated manner does give it some database-like properties, which can be helpful in specific scenarios. In this article, I intend to draw a connecting line highlighting similar properties of Kafka and a conventional … Read more

How I Plan, Track & Organize My Finances In Notion

As a product creator, I love creating micro tools to improve my personal and work productivity. This quarter, I’m focused on improving how I track money and manage personal finances. Today, I’ll explain how I use Notion as my Finance Mastery System and share some cool features I’ve created in my Notion template (available on … Read more

Google’s AI Aftershock: Expert Explains How to Thrive in the New Search Landscape

Google recently dropped a bombshell on the search world, shaking the foundation of how we find information online. Forget scrolling through pages of blue links; now, an AI oracle sits atop the search results, dishing out answers plucked from the web’s vast expanse. Google promises this AI genie will grant users’ wishes for quicker, more … Read more

HDR or SDR? A Study of Scaled and Compressed Videos: Subjective Analysis

:::info Authors: (1) Joshua P. Ebenezer, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work (e-mail: joshuaebenezer@utexas.edu); (2) Zaixi Shang, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed … Read more

HDR or SDR? A Study of Scaled and Compressed Videos: Details of Subjective Study

:::info Authors: (1) Joshua P. Ebenezer, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work (e-mail: joshuaebenezer@utexas.edu); (2) Zaixi Shang, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed … Read more

HDR or SDR? A Study of Scaled and Compressed Videos: Related Work

:::info Authors: (1) Joshua P. Ebenezer, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work (e-mail: joshuaebenezer@utexas.edu); (2) Zaixi Shang, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed … Read more

HDR or SDR? A Study of Scaled and Compressed Videos: Conclusion, Acknowledgment, and References

:::info Authors: (1) Joshua P. Ebenezer, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work (e-mail: joshuaebenezer@utexas.edu); (2) Zaixi Shang, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed … Read more

HDR or SDR? A Study of Scaled and Compressed Videos: Objective Assessment

:::info Authors: (1) Joshua P. Ebenezer, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work (e-mail: joshuaebenezer@utexas.edu); (2) Zaixi Shang, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed … Read more

HDR or SDR? A Study of Scaled and Compressed Videos: Abstract and Introduction

:::info Authors: (1) Joshua P. Ebenezer, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work (e-mail: joshuaebenezer@utexas.edu); (2) Zaixi Shang, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed … Read more

A Synthetic Biography for a Geek

A biography generated by an AI? Indeed. So I decided I’d write my biography, or a sort of an edited autobiography of some particular episodes. Then while on a break from scribing away I ran up on this article about an AI that generates “artificial” web sites based upon a verbal prompt. This page is … Read more

SSV Network Reshapes Ethereum Security with $3B in TVL

SSV Network, a distributed validator platform for Ethereum, has reached a significant milestone of 1 million ETH staked on its network. This development marks a notable advancement in the Ethereum staking ecosystem, potentially enhancing the blockchain’s overall security and decentralization. Key Points: SSV Network now holds over $3 billion in total value locked (TVL) Nearly … Read more

Future of Programming: Enhancing Agile Development with Automated Pseudocode

:::info Authors: (1) Gaurav Kolhatkar, SCTR’s Pune Institute of Computer Technology, Pune, India (gauravk403@gmail.com); (2) Akshit Madan, SCTR’s Pune Institute of Computer Technology, Pune, India (akmadan17@gmail.com); (3) Nidhi Kowtal, SCTR’s Pune Institute of Computer Technology, Pune, India (kowtalnidhi@gmail.com); (4) Satyajit Roy, SCTR’s Pune Institute of Computer Technology, Pune, India (satyajit12.roy@gmail.com). ::: Table of Links Abstract … Read more

The BLEU Benchmark: Ensuring Quality in Automated Code and Pseudocode

:::info Authors: (1) Gaurav Kolhatkar, SCTR’s Pune Institute of Computer Technology, Pune, India (gauravk403@gmail.com); (2) Akshit Madan, SCTR’s Pune Institute of Computer Technology, Pune, India (akmadan17@gmail.com); (3) Nidhi Kowtal, SCTR’s Pune Institute of Computer Technology, Pune, India (kowtalnidhi@gmail.com); (4) Satyajit Roy, SCTR’s Pune Institute of Computer Technology, Pune, India (satyajit12.roy@gmail.com). ::: Table of Links Abstract … Read more

Transforming Text to Code: An Approach to Efficient Agile Development

:::info Authors: (1) Gaurav Kolhatkar, SCTR’s Pune Institute of Computer Technology, Pune, India (gauravk403@gmail.com); (2) Akshit Madan, SCTR’s Pune Institute of Computer Technology, Pune, India (akmadan17@gmail.com); (3) Nidhi Kowtal, SCTR’s Pune Institute of Computer Technology, Pune, India (kowtalnidhi@gmail.com); (4) Satyajit Roy, SCTR’s Pune Institute of Computer Technology, Pune, India (satyajit12.roy@gmail.com). ::: Table of Links Abstract … Read more

The Evolution of Text-to-Code and Pseudocode Automation

:::info Authors: (1) Gaurav Kolhatkar, SCTR’s Pune Institute of Computer Technology, Pune, India (gauravk403@gmail.com); (2) Akshit Madan, SCTR’s Pune Institute of Computer Technology, Pune, India (akmadan17@gmail.com); (3) Nidhi Kowtal, SCTR’s Pune Institute of Computer Technology, Pune, India (kowtalnidhi@gmail.com); (4) Satyajit Roy, SCTR’s Pune Institute of Computer Technology, Pune, India (satyajit12.roy@gmail.com). ::: Table of Links Abstract … Read more

Converting Epics/Stories into Pseudocode using Transformers

:::info Authors: (1) Gaurav Kolhatkar, SCTR’s Pune Institute of Computer Technology, Pune, India (gauravk403@gmail.com); (2) Akshit Madan, SCTR’s Pune Institute of Computer Technology, Pune, India (akmadan17@gmail.com); (3) Nidhi Kowtal, SCTR’s Pune Institute of Computer Technology, Pune, India (kowtalnidhi@gmail.com); (4) Satyajit Roy, SCTR’s Pune Institute of Computer Technology, Pune, India (satyajit12.roy@gmail.com). ::: Table of Links Abstract … Read more

Refactoring 014 – How to Remove IF

The first instruction you learned should be the least you use. TL;DR: Remove all your Accidental IF-sentences Problems Addressed Code Duplication Possible Typos and defects Related Code Smells Code Smell 07 – Boolean Variables Code Smell 36 – Switch/case/elseif/else/if statements Code Smell 133 – Hardcoded IF Conditions Code Smell 156 – Implicit Else Code Smell … Read more

How Artificial Intelligence Can Make Our Smart Homes, Smarter

Artificial Intelligence (AI) is receiving a warm welcome from users and big-league developers around the world as more companies pursue an effort to integrate some form of artificial technology into their software and leverage machine learning capabilities to enhance the end-user experience. Silicon Valley has seen an explosion in the number of AI projects currently … Read more

Recommendations for Verifying HDR Subjective Testing Workflows: Abstract and Introduction

:::info Author: (1) Vibhoothi,Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: vibhoothi@tcd.ie); (2) Angeliki Katsenou, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland & Department of Electrical and Electronic Engineering, University of Bristol, United Kingdom (Email: angeliki.katsenou@bristol.ac.uk); (3) John Squires, Sigmedia Group, Department of Electronic and … Read more

Recommendations for Verifying HDR Subjective Testing Workflows: HDR Standards

:::info Author: (1) Vibhoothi,Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: vibhoothi@tcd.ie); (2) Angeliki Katsenou, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland & Department of Electrical and Electronic Engineering, University of Bristol, United Kingdom (Email: angeliki.katsenou@bristol.ac.uk); (3) John Squires, Sigmedia Group, Department of Electronic and … Read more

Recommendations for Verifying HDR Subjective Testing Workflows: Conclusion

:::info Author: (1) Vibhoothi,Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: vibhoothi@tcd.ie); (2) Angeliki Katsenou, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland & Department of Electrical and Electronic Engineering, University of Bristol, United Kingdom (Email: angeliki.katsenou@bristol.ac.uk); (3) John Squires, Sigmedia Group, Department of Electronic and … Read more

Recommendations for Verifying HDR Subjective Testing Workflows: HDR Subjective Testing Workflow

:::info Author: (1) Vibhoothi,Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: vibhoothi@tcd.ie); (2) Angeliki Katsenou, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland & Department of Electrical and Electronic Engineering, University of Bristol, United Kingdom (Email: angeliki.katsenou@bristol.ac.uk); (3) John Squires, Sigmedia Group, Department of Electronic and … Read more

TypeScript SDK Development: A 5-Year-Old Could Follow This Step-By-Step ~ Part 1: Our First MVP

Helloooooooo! Hope you’re doing great! This is SMY! 👋 Let’s jump right in 🚀 Source Code: https://github.com/smyaseen/typescript-sdk-template Contents: ⚡ Some Background of SDK Development ⚡ Developing and running our first version 1️⃣ What – SDK (sometimes also known as library) serves as a plug-in in applications to derive additional features from the technology. 2️⃣ Why – SDK development … Read more

The Role of AI in Hazmat Response

Artificial intelligence (AI) excels at analyzing data accurately and efficiently. While those advantages apply to virtually any industry, high-risk working environments may benefit from them more than most. Hazmat response is one of the more niche but promising use cases for this technology. What Is Hazmat Response? Hazmat response refers to the management of incidents … Read more

Training-Free Neural Matte Extraction for Visual Effects: Limitations and Conclusion

:::info Author: (1) Sharif Elcott, equal contribution of Google Japan (Email: selcott@google.com); (2) J.P. Lewis, equal contribution of Google Research USA (Email: jplewis@google.com); (3) Nori Kanazawa, Google Research USA (Email: kanazawa@google.com); (4) Christoph Bregler, Google Research USA (Email: bregler@google.com). ::: Table of Links Abstract and Introduction Background and Related Work Method Results and Failure Cases … Read more

Training-Free Neural Matte Extraction for Visual Effects: Abstract and Introduction

:::info Author: (1) Sharif Elcott, equal contribution of Google Japan (Email: selcott@google.com); (2) J.P. Lewis, equal contribution of Google Research USA (Email: jplewis@google.com); (3) Nori Kanazawa, Google Research USA (Email: kanazawa@google.com); (4) Christoph Bregler, Google Research USA (Email: bregler@google.com). ::: Table of Links Abstract and Introduction Background and Related Work Method Results and Failure Cases … Read more