common intimate phrases ft. hozier

Thiruvizha Mahadeva Kshetram
#Alappuzha 
#Keralatemples 🚩

Thiruvizha Mahadeva Kshetram is located in Cherthala and it is believed that Prathishta of temple was done by Vilwamangalam Swamy. Paramashiva is Swayambhu here and is worshipped as Kalakandha which means who swallowed

Halahala visham to save the world during Samudra mathanam. 

It is a famous temple where people seek the blessings, for curing the Kaivisham. The poison given by someone to destroy a person is made to vomit here. This is removed by making them vomit the poison by a special

medicine.
The medicine for vomiting is made from a plant near temple by Palodathu family. The milk offered to Bhagwan is mixed with the extracts of the plant.

Story says that once this place was a dense forest, and a woman was looking for the presence of a tortoise in the pond

and the tip of her stick hit the bottom and it started bleeding in the pond. 
Frightened, the lady informed every one and after searching in pond people found Swayambhu Lingam inside the pond. Thus Prathishta was done here.

Om Namah Shivaye
Shambo Mahadeva
🙏🕉🙏

The 2018 State of JavaScript survey is out. They got 20,000 responses and have some delicious, delicious data. I'mma thread in some highlights:

First up: JavaScript flavors. There's TypeScript at 47% adoption, a tad higher than npm's own survey results (which said 46%). npm's survey is coming up again soon and will ask about TypeScript in a lot more detail.

I really, *really* like SoJ's "would not use again" question, which lets people who've abandoned a tech self-identify. This is noticeable in the graph above with Flow users -- 41% of people who've used Flow say they wouldn't use it again.

Now JavaScript frameworks. It's been a full year since npm's survey, so these numbers differ from ours in the directions I expected:
React 65% (vs. 60%)
Vue 29% (vs. 24%)
Ember 5% (vs 4%, I was expecting a bigger rise)

But there's a shocker in here: Angular.

npm's survey had Angular at 40% last year and SoJ has it at either:
- 58% (if you include those who don't want to use it again)
- 24% (if you count only those who like it)
Since npm's question didn't ask if they intend to *continue* using it I think that might explain this.

An interesting set of games that I've been vaguely interested in since I was a kid, after seeing one at a friend's house and never being able to find it again, was the BATTLES IN A DISTANT DESERT/BATTLES ON DISTANT PLANETS series.

These are turn-based wargames for windows 3.x, sharing the same engine.
Battles in a Distant Desert is from 1992, and is based on the first Iraq war (Desert Storm)

and Battles on Distant Planets is from 1991, and takes place in SPACE!
I'm pretty sure this is the one I played as a kid.

They've got 3 options to play with:
* Player vs. Player
* Player vs. Computer
* Computer vs. Computer

So it's a 0-2 player game!

They also did a DOS strategy game called "STARDATE 2140.2: GALACTIC CONQUEST" in 1990, but it seems to be lost.
All the games share an experimental AI system based on neural networks.

BANASHANKARI AMMA, NEAR BADAMI (KAR)
#bharatmandir #navratri2021

Originally built by Chalukyas in 7th century. Devi Banashankari was their family deity. This image of Banashankari Amma was installed by Jagadekamalla I in 630 AD.

Later renovations were done by Marathas.

Once, a demon Durgamasur was troubling the people of this area. Answering the prayers of Devas, Ma Parvati emerged from the yajna fire as Banashankari and killed Durgamasur.
This area has rich forests of coconut, betel nut and other trees.
@GunduHuDuGa

It is said that during a severe famine, Devi provided vegetables and fruits to the people of this area.
Hence, she is also worshipped here as Shakambhari.
In the annual festival called Banashankari jatre, a Rath yatra takes place and Devi moves around the city in the chariot.

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.