They have to declare stock position of paddy and rice every Friday

What are the measures the government is taking to bring down the increasing price of rice?

Traders are allowed to export this quantity till March 31

PM Modi made his comments while inaugurating the pilot project of “world’s largest grain storage plan”, which is being done across 11 States

Union Minister for Food and Consumer Affairs Pralhad Joshi has announced that States can buy rice from the Food Corporation of India at ₹2,800/quintal without participating in e-auction.  

A group of Indian Ocean islanders, forcibly removed from their homes 50+ years ago and deported to England, are still fighting for recognition and basic rights. By Alexi Demetriadi.

Marc Robert's monthly cartoon, from our June magazine.

The guerrillas are handing weapons over to the UN, but they are in fear. Thomas Mortensen reports from Urabá.

I often use surfing as a metaphor for new technology. Go too early and you don't catch the wave. Go too late and you don't catch it either. Similarly next generation hardware or software may be too early for its time. I found myself wondering if this was the case for Google Glass and AI.

For those who don't remember, Google Glass was an early augmented reality headset that despite early excitement was ultimately shuttered. I spent time with the developer version of Google Glass in 2013 and, while promising, didn't think it was ready. But the technical capabilities of the device were impressive especially for its time. Glass featured:

• a camera for taking photos and video
• a microphone for accepting voice commands
• a speaker for audio input only you could hear (bone conduction)
• a mini projector to display information and interface controls in the corner of your field of vision
• a trackpad for controlling the interface and voice commands
• a number of sensors for capturing and reacting to device movement, like head gestures
• WiFi and Bluetooth connectivity

What Google Glass didn't have is AI. That is, vision and language models that can parse and react to audio and video from the real World. As I illustrated in a look at early examples of multi-modal personal assistants: faced with a rat's nest of signs, you want to know if it's ok to park your car. A multi-modal assistant could take an image (live camera feed or still photo), a voice command (in natural language), and possibly some additional context (time, location, historical data) as input and assemble a response (or action) that considers all these factors.

Google Glass had a lot of the technical capabilities (except for processing power) to make this possible in a lightweight form factor. Maybe it just missed the AI wave.

New technology companies often have grand ambitions. And for good reasons - ambitious plans help recruit talent, raise capital, and set the bar high. But progress toward these high-level goals relies on identifying and excelling at much lower-level use cases.

It's very common for new technology companies to aspire being "the platform for... the Internet of things, AI analytics, mobile testing, etc." Being a platform means you capture a lot of uses cases or to put it more simply... people use your service for a lot of different things. And more use equals more value.

But vision is not the same as strategy. Vision is about the end goal. It paints a picture of the future state you're aiming for. It’s what you want to achieve. Strategy, on the other hand, is how you get there.

When you use a broad vision as a strategy, you end up having a hard time making decisions and rationalizing a never-ending set of opinions. With a strategy like “we’ll be the platform for the Internet of things”, everyone has an opinion on how things on the Internet should work -which one do we listen to?

Consider instead a specific market for the Internet of things, like home automation, and an even more specific use case for home automation like "controlling the temperature in your house". It's much easier to evaluate decisions about what a good experience for controlling the temperature in your house is than for "we’ll be the platform for the Internet of things”.

But if you focus on such a narrow use case, how will you ever build a big business? I'm not suggesting abandoning the big vision instead I'm advocating for having a strategy based on solving concrete uses cases to get there. Let's look at another example: Yelp.

Today, Yelp is used for recommendations for all kinds of services: skydiving training, auto body shops, tea parlors, and more. But it didn't start that way. Yes, Yelp likely started with the ambitious vision of being a platform for all service recommendations. But it first launched in San Francisco with restaurant reviews. A very specific market and very specific use case.

Why start with restaurants? A good starting use case is the one with the most acute pain. In the context of services, people need to eat three times a day. They get their hair cut once a month and maybe need a plumber once a year. So where should Yelp start? Probably restaurants.

When solving for a specific use case, it's important to build with the bigger vision in mind and not paint yourself into a corner of only being useful for one thing. But you definitely have to be great at solving each use case your platform supports. How else will you convince people to adopt your solution? Once you can demonstrate clear value for a specific use case, you can tackle more (likely adjacent ones).

This way of scaling ensures your solution is actually good at addressing a concrete problem people have not just an abstract vision. When you hear "What's your platform for? Well... you can use it for pretty much anything." in a sales pitch, that's a warning sign.

When you instead address specific use cases well, you learn what parts of your platform matter the most by identifying patterns and doubling down on them. It's only from solving highly specific use cases that you actually get to a platform that can be broadly used for many different things. And why Amazon started by only selling books on the Web.

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Edmonton, Alberta : Brush Education Inc., [2023]

Bloomington, IN : Solution Tree Press, [2023]

Oxford ; New York : Peter Lang, [2024]

Lynnfield, Massachusetts CAST [2024]

Bloomington, Indiana : Solution Tree Press, 2018.

London : Bloomsbury Academic, [2024]

New York : Peter Lang , 2024.

Baltimore : Johns Hopkins University Press, [2024]

Bloomington, IN : Solution Tree Press, [2024]

Cham, Switzerland : Springer Nature, 2024.

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