Hindcopper -- Can face selling pressure from 122-125 zone. Bull are weak and Previous bottoms 135-136 major hurdle. Not Crossing 125/135 can take stock to lower levels

#Hindcopper https://t.co/KMpDhWTc53

Arya Samaj Exposed ( A Thread)

Today I'm going to expose these so called reviver of Sanatan dharma.

Arya samaj's main book which they treat as their bible is Satyarth Prakasha. Which was written by "Maharishi" Dayanand Saraswati.

Dayanand Condemned Idol worship and blamed it on jains . like really??

The book peddles hate against several Hindu sects.

here is hate against Vaishnavas

Low-risk, High-return stocks are what we look for relentlessly

But many of them we know but ignore
some of them don't grab financial headlines

Here are 10 low-risk, high return stocks (that we ignore):

What I mean by “high-return”?

These stocks have OUTPERFORMED the market:

💹Chart with upwards trend Since IPO
💹Chart with upwards trend Over the last 5 Years or since IPO

These are all long-term winners that will keep on winning

What do We Mean by “low-risk”?

A combination of:

❣️Great management
❣️Broad moat
❣️Financially strong
❣️High ROCE
❣️Low Disruption/Dilution/Volatility
❣️Consistent Organic Growth
❣️Assorted Customers/Suppliers
❣️Low debt

1-#CDSL

A depository is a big business and will grow continuously, Its a sustainable business
cause "Market hai toh CDSL bhi hai or rahega"

2- #Dixon

High Management Efficiency with a high ROE of 19.30%
Healthy long-term growth as Net Sales has grown by an annual rate of more than 30%
High Institutional Holdings

Ancient Egypt was the most advanced civilization in the ancient world. Why was Egypt so special?

There are so many firsts in Egypt that we all know about, and that makes Egypt unique. For example, the Sumerians did not build the pyramids of Egypt.

Save as history

1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.