Skip to main content

Few tips for speedier browsing.

How often have you lost your temper when you are searching a forum's thread for a "how-to-fix" the broken piece of electronics your best mate gifted you on your birthday? Or trying to find a nice greeting card on Google Image search, clicking each thumbnail and exhausting yourself in back and forth browsing exercise? Quite often, right?

If you browse the internet, you have experienced the annoyance that multi-page galleries, reviews, and sometimes even Google search creates. By multi-page content, I mean a single piece of information, which is broken into multiple pages, mostly for advertisements that the viewer hates.

This small article should help you speed up your browsing. But your browser should be Google Chrome or Mozilla Firefox (honestly, I haven't tried other browsers).

The Solution


  1. Fastest Chrome/Fastest Fox: automatically loads next pages whenever possible, when you scroll to the bottom of the web-page. This saves you from trying to locate the next button or counter and clicking it. Not only that, you can have quick Wikipedia definitions on simply selecting a word or phrase without having to open a new tab.


  2. Hover Zoom/Thumbnail Zoom: hover your mouse over any image on the webpage and it will most likely magnify, i.e. if the image is actually a low-resolution or thumbnail for preview. This again, saves you from opening the link of the original image.

I have been using these couple of extensions for years now and enjoying an incredibly fast browsing experience.



Please leave a comment if you like or dislike. Corrections are very much appreciated...

-
Owais

Comments

Popular posts from this blog

A faster, Non-recursive Algorithm to compute all Combinations of a String

Imagine you're me, and you studied Permutations and Combinations in your high school maths and after so many years, you happen to know that to solve a certain problem, you need to apply Combinations. You do your revision and confidently open your favourite IDE to code; after typing some usual lines, you pause and think, then you do the next best thing - search on Internet. You find out a nice recursive solution, which does the job well. Like the following: import java.util.ArrayList; import java.util.Date; public class Combination {    public ArrayList<ArrayList<String>> compute (ArrayList<String> restOfVals) {       if (restOfVals.size () < 2) {          ArrayList<ArrayList<String>> c = new ArrayList<ArrayList<String>> ();          c.add (restOfVals);          return c;       }       else {          ArrayList<ArrayList<String>> newList = new ArrayList<ArrayList<String>> ();          for (String

How to detach from Facebook... properly

Yesterday, I deactivated my Facebook account after using it for 10 years. Of course there had to be a very solid reason; there was, indeed... their privacy policy . If you go through this page, you might consider pulling off as well. Anyways, that's not what this blog post is about. What I learned from yesterday is that the so-called "deactivate" option on Facebook is nothing more than logging out. You can log in again without any additional step and resume from where you last left. Since I really wanted to remove myself from Facebook as much as I can, I investigated ways to actually delete a Facebook account. There's a plethora of blogs on the internet, which will tell you how you can simply remove Facebook account. But almost all of them will either tell you to use "deactivate" and "request delete" options. The problem with that is that Facebook still has a last reusable copy of your data. If you really want to be as safe from its s

A step-by-step guide to query data on Hadoop using Hive

Hadoop empowers us to solve problems that require intense processing and storage on commodity hardware harnessing the power of distributed computing, while ensuring reliability. When it comes to applicability beyond experimental purposes, the industry welcomes Hadoop with warm heart, as it can query their databases in realistic time regardless of the volume of data. In this post, we will try to run some experiments to see how this can be done. Before you start, make sure you have set up a Hadoop cluster . We will use Hive , a data warehouse to query large data sets and a adequate-sized sample data set, along with an imaginary database of a travelling agency on MySQL; the DB  consisting of details about their clients, including Flight bookings, details of bookings and hotel reservations. Their data model is as below: The number of records in the database tables are as: - booking: 2.1M - booking_detail: 2.1M - booking_hotel: 1.48M - city: 2.2K We will write a query that