India’s Reliance to buy majority stake in Google-backed Fynd for $42.3M

Indian conglomerate Reliance Industries is acquiring 87.6% stake in Fynd, a seven-year-old Mumbai-based startup that connects brick and mortar retailers with online stores and consumers, for 2.95 billion Indian rupees ($42.33 million), the two said in a brief statement late Saturday.

Fynd, which was founded in 2012, helps offline retailers sell their products to consumers directly through its online store, and also enables them to connect with other “demand channels” such as third-party e-commerce platforms Amazon India and Walmart-owned Flipkart.

More than 600 brands including Nike, Raymond, Global Desi, and Being Human, and 9,000 stores are connected through Fynd’s platform, Harsh Shah, co-founder of Fynd, told TechCrunch in an interview. Many brands also use Fynd’s products to ramp up sales on their own respective e-commerce businesses.

Since Fynd works directly with brands, it offers a wider selection of items and newer inventories to consumers, as well as faster delivery, Shah claimed.

Fynd’s website

Reliance Industries, which owns the nation’s biggest physical retail chain Reliance Retail, has been a customer of Fynd for more than six years, Shah said. “Reliance runs a few major brands in the country. 25 of our existing brands are owned by them. Our Find Store product has helped their stores plug a lot of sales,” he said.

Fynd, which counts Google as one of its early investors, will continue to operate its existing business and has an option to secure an additional 1 billion India rupees ($14 million) by end of 2021 from Reliance Industries, Shah said. He declined to reveal how much capital his startup had raised prior to this week’s announcement. According to Crunchbase, Fynd has raised about $7.3 million.

“Reliance is taking the majority stake in Fynd, but at the end of the day, for us it is like any other investor coming in. We will still continue to work separately, we have our own independent roadmap, and we have own clients and products that we plan to grow. So things continue as it is,” he said.

Fynd, which takes a small commission on each transaction that occurs online, is already profitable on an operating level and expects to be fully profitable in the coming quarters, Shah said.

It will continue to build and scale its existing products, including OpenAPI that allows merchants to quickly list their products on either their own stores or third-party sites and manage their inventories and sales.

Despite tens of billions of dollars of investment in India’s e-commerce market in recent years by Amazon India and Flipkart, physical retail dominates the sales in the country. But e-commerce businesses in India are growing, too.

The nation’s e-commerce space is estimated to scale to $84 billion by 2021, up from $24 billion in 2017; compared to India’s overall retail market that is estimated to be worth $1.2 trillion by 2021, according to a recent study by Deloitte India and Retail Association of India.

Reliance Industries, run by Asia’s richest man Mukesh Ambani (pictured above), additionally has its own plan to enter the e-commerce business. Earlier this year, Ambani announced that his telecom operator Reliance Jio and Reliance Retail are working on an e-commerce platform.

Reliance Jio, which began its commercial operations in the second half of 2016, recently became the nation’s biggest telecom operator with more than 331 million subscribers at the end of June.

Separately, Amazon.com is in talks with Reliance Industries to buy more than a quarter stake in Reliance Retail, a person familiar with the matter told TechCrunch. News outlets Reuters and Economic Times were first to report this development.

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You Can Now Play the Original Diablo in Your Browser

The Diablo series has kept gamers entertained for decades, and now you can now go back to the very start by playing the original Diablo in your web browser. The visuals are bound to disappoint, but the storyline and gameplay are both as good as ever.

Blizzard is currently working on several new Diablo games, either confirmed or rumored. There’s Diablo 4, obviously, plus a mobile game called Diablo: Immortal. But while you’re waiting for these to arrive, why not play the original Diablo from 1996 in your browser.

How to Play Diablo in Your Web Browser

Diablo is a hack and slash action RPG. You pick a character ( Warrior, Rogue, or Sorcerer) and head off to rid the world of Diablo, the Lord of Terror. You battle your way through randomly generated dungeons, eventually entering Hell itself to face off against Diablo.

The game is now available to play in your web browser. This is the original source code which comes complete with bugs. The shareware version is available to play for free, but this limits you to the Warrior class and the first two dungeons.

If you own a copy of the game (which is available to buy from GoG), you can load the main DIABDAT.MPQ file to play the full game in your browser. To play the original Diablo in your browser, just visit the website and choose your options to get started.

Other Old Games Still Worth Playing Today

If you’re an older gamer who remembers playing the original Diablo in the 1990s, this should bring back nostalgic memories. And if you’re a younger gamer who has never played anything so ancient, this is an opportunity to sample a slice of gaming history.

If you enjoy playing older games, there are plenty of other options beyond Diablo. We have previously listed the old PC games still worth playing today, and the remastered video games worth playing again. Which should help you sort the wheat from the chaff.

Read the full article: You Can Now Play the Original Diablo in Your Browser

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Is the Nooie Cam 360 the Affordable IP Cam You’re Looking For?

What Should an Executive Know about Machine Learning? "Supervised Learning"

This post is in continuation of my earlier post on Machine Learning & the three buckets in which it can be understood. You can read it here

In this post, I am going to express my opinion on Supervised Learning.

In supervised learning, the output variable is known, and this output variable is used in the training.

There are three steps for building a supervised model: Building model, Training model & Testing model. Let us understand these three with the help of an example.

Step #1: Building model

Suppose you have joined a coaching class to learn machine learning. Hence, in this case, you become the model.

Step #2: Training model

Your faculty will be teaching you. She will also use various teaching aids during this process. This is the training process. Here, we try to train the model using historical & recent data. The basis of this process is to identify either patterns or dependencies in the data.

Step #3: Testing model

Now is the time when you (model) has to appear for the exam. Obviously, the teacher will not use the same data to test you on which she has trained you, hence, the exam paper will have similar patterns on which you have to respond, but not the same.

Generally, to test the prediction or accuracy of the model, we test it on the untrained data. Usually, the ratio of training data to test data is 70/30.

If your exam score falls below a configured value, then, re-training happens.

Let me correlate it with IT & Business use-cases:

Case #1: IT | Proactive Maintenance of Infrastructure

You take 100,000 tickets from your ITSM tool, build a data model (70,000 tickets) & test it with the rest of the 30,000 tickets. If the accuracy of the data model is >85% (e.g.), then you roll the model for proactive maintenance of your infrastructure (servers, routers etc.).

Case #2: Business | Detect Fraud Transactions of Credit Cards

You gather data on the fraud transactions. Again you split the data into 70:30 ratio. In this case, let’s assume that the model has 75% accuracy (which may be good for rolling it out live). So, this model, when encounters pattern abc in the new transactions, it can predict the probability of fraud in that transaction. Hence, now, you can take necessary actions

Both the cases which I mentioned falls under the category of Classification problems under our initial Supervised Learning. There is another category, called Regression (same thing which you did during your MBA days using SPSS, & hence you all have learnt some machine learning!), which is the second category under Supervised Learning.

Technically speaking, Regression is independent of any framework: machine learning or any classical statistical methods.

Regression refer to a model to predict some numbers, like real values. This is different from Classification, which predicts discrete variables (fraud, mangoes etc.)

Summary: 

● In the supervised learning, we know the outcomes

● The Three steps process: Build, Train, Test

● It can be of two types: Classification (discreet classes) & Regression (real values)

Hope it helps in your next sales pitch to convey these concepts better!

Special thanks to Aditi Aggarwal for helping me with the content & Debapriya for the motivation!