#Radico reached our 1st target and is trading at 870 now.

@MD_ABNSTOCKS

One thing that goes unremarked about Wikipedia: the vast majority of the content was written over 12 years ago—which means (given turnover) that the editors dominant today were not directly involved in that creation.

Put another way, the editors who built the dominant nodes in this network...

...have little overlap with the ones who made this much more recent managerial flowchart.

Internet time runs at hundred-fold speed—the difference between the people who painted what's in the Uffizi, and the people in charge of keeping those paintings from deteriorating. Very different tasks, and (one presumes) very personalities as well. @PaulSkallas?

Thats a gg, Satan

You want to buy a house.

You take a loan from the bank.

The bank asks you, “what if you don’t pay back?”

You say, “take my home and sell it”.

(Thread)

That makes sense. The bank gives you a loan and in case you don’t pay, the bank will take over your house and sell it and recover its money.

That’s collateral.

Banks also lend money to big corporations. With them, banks don’t always have collateral.

So a loan where the bank has collateral is considered safer for the bank.

In the early 2000s, the banks in the USA were giving out loans to people for buying houses.

The economy had just recovered from the tech bubble in 2000 where many banks had given out loans to businesses that had shut down.

In that backdrop, the banks found giving home loans safer.

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.