Skip to main content

Movie Review - Love, Sex, Aur Dhoka


The only horror movie which scared me to my shits and left me disturbed for a week was "The Blair Witch Project". The use of scenes shot through handycam throughout the movie gave it a very real feel and was its major USP for me. Then "Paranormal Activity" came along. It was a very good movie but failed to impress. But one thing that stood out again was the use of camera. When will we see such camera work in Bollywood, I wondered. Well, my wait was about to be over.

Yesterday, I was torn between watching "Lahore" and "Love, Sex Aur Dhoka (LSD)". But ultimately LSD won precisely for the reasons that it mentions in its title - love, sex and betrayal. Directed by Dibakar Banerjee (of "Oye, Lucky! Lucky, Oye!" fame), LSD breaks all barriers of civilized society. It hold a mirror to the society and the reflection is not pretty. 

Shot through handycams, CCTV cameras and spy-cams, LSD is an entertaining as well as disturbing watch. I didn't realize that the movie has started until 5 minutes into it, for its treatment is so different. But it surely immerses you in it by way of three stories which are intertwined. The first story is a love story about two diploma students who are shooting a movie for their degree and fall in love. This story keeps you in splits until its shocking end when I just couldn't look at the screen. I can't tell you what the end was for it would ruin the experience for you, but judging by the pin-drop silence during that scene in a theater full of restless college students, it was very disturbing.

The second story is about how a guy wants to make a quick buck by shooting a dirty video, and doesn't even hesitate to use the girl that he loves for the video. It makes you feel ashamed for even watching those MMS clips that circulate around. The third and final story is a little convoluted but ultimately binds the three together.

The movie is full of cuss words and songs like "Tu gandi achchi lagti hai" keep you engrossed as well as shocked. But at the end of it all, the movie numbs you.

Please do watch this movie for movies like this come few and far.

Comments

Popular posts from this blog

Creating a Smart Playlist

A few days earlier I was thinking that wouldn't it be nice if I had something which will automatically generate a playlist for me with no artists repeated. Also, it would be nice if I could block those artists which I really hate (like Himesh Reshammiya!). Since I couldn't find anything already available, I decided to code it myself. Here is the outcome -  This application is created entirely in .NET Framework 4/WPF and uses Windows Media Player Library as its source of information. So you have to keep your Windows Media Player Library updated for this to work. It is tested only on Windows 7/Vista. You can download it from here . UPDATE : You can download the Windows XP version of the application here . Please provide your feedback!

Integrating React with SonarQube using Azure DevOps Pipelines

In the world of automation, code quality is of paramount importance. SonarQube and Azure DevOps are two tools which solve this problem in a continuous and automated way. They play well for a majority of languages and frameworks. However, to make the integration work for React applications still remains a challenge. In this post we will explore how we can integrate a React application to SonarQube using Azure DevOps pipelines to continuously build and assess code quality. Creating the React Application Let's start at the beginning. We will use npx to create a Typescript based React app. Why Typescript? I find it easier to work and more maintainable owing to its strongly-typed behavior. You can very well follow this guide for jsx based applications too. We will use the fantastic Create-React-App (CRA) tool to create a React application called ' sonar-azuredevops-app '. > npx create-react-app sonar-azuredevops-app --template typescript Once the project creation is done, we

Serverless Generative AI: How to Query Meta’s Llama 2 Model with Microsoft’s Semantic Kernel and AWS Services

Generative AI is a type of artificial intelligence that can create new content such as text, images, music, etc. in response to prompts. Generative AI models learn the patterns and structure of their input training data by applying neural network machine learning techniques, and then generate new data that has similar characteristics. They are all the rage these days. 😀 Some types of generative AI include: Foundation models , which are complex machine learning systems trained on vast quantities of data (text, images, audio or a mix of data types) on a massive scale. Foundation models can be adapted quickly for a wide range of downstream tasks without needing task-specific training. Examples of foundation models are GPT, LaMDA and Llama . Generative adversarial networks (GANs) , which are composed of two competing neural networks: a generator that creates fake data and a discriminator that tries to distinguish between real and fake data. The generator improves its ability to fool the d