Thread on

👉How to choose best stocks for long term #Investing.

1⃣ Check out fundamentals:

🔹Debt to Equity Ratio = 0 or <1
🔹Revenue Growth > 10%
🔹Net Profit Growth > 15%
🔹Return on Equity > 20%
🔹Earning per share increases 10% Y-O-Y.

(1/N)

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.

#Guruvayur_Krishna_Bhakthi  🕉
#Guruvayur
#Storytime
Beautiful Story of Guruvayurappan

Poonathanam Namboothiri was a great devotee of Guruvayurappan. One day Poonthanam was going to Guruvayur Kshetram. It was night time and eventhough Namboothiri was tired he continued to walk

Suddenly some thieves came and blocked his way. Poonthanam cried for Shri Krishna to help. Thieves snatched all valuables from him, but Namboothiri holding his Bhaghavatam book called Shri Krishna to help. That time a man came in a horse back and fought with thieves and protected

Namboothiri. Poonthanam thanked Krishna for helping him from danger and said to the man that "Shri Krishna has send you here and I don't know how to thank you".  The man smiled and told that he is Mangattachan, minister of  samoodhiris. Poonthanam once again thanked him and

gave his ring to Mangattachan as a token of thanks and continued his journey. He reached Guruvayur early morning for Nirmalya Darshan. Temple poojari came near him and told that Guruvayurappan came in his dreams and asked to hand over the ring to poonthanam.

When he opened Kshetra Nada poonthanam was surprised to see his ring on Guruvayurappan, which he gifted to Mangattachan. He understood that it was Guruvayurappan who came as Mangattachan to protect him. This is how Bhagwan treat his devotees.

Hare Krishna
🙏🕉🙏

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.