#ACC -2304

#Thread
Lessor known facts of Sudarshan Chakra or Neela Chakra of Puri Jagannath Temple.

The metal wheel at the top of Bhagwan Jagannath temple is also known as Neela Chakra (Blue Wheel). The wheel is made of eight metals comprising of.. 1/n

iron, copper, zinc, mercury, lead, brass, silver & gold. It's circumferance is about 36 feet & it is so designed that there is a wheel within the wheel. The circumferance of the inner wheel is about 26 feet. Eight hubs join together the inner and the outer wheels. 2/n

There are decorative designs on the outer wheel. The thickness of the Neela Chakra is 2 inches.The wheel is said to be Bhagwan Vishnu's most powerful weapon, Sudarshana Chakra. Inside Jagannath temple, Lord Vishnu's wheel is also worshipped in the name of 'Sudarshana'. 3/n

But inside the temple Sudarshana is not in the shape of a wheel, but in the shape of a small wood pillar placed to the left of image of bhagwan Jagannath. It is believed that the same Sudarshana is also at the top of the temple and is known by the name of Neela Chakra. 4/n

There is a specific category of people among the temple sevaks to serve the Neela Chakra and they are known as Garuda Sevaka or Chunara Nejoga. The pilgrims hold these sevaks in high esteem because everyday at Sunset. 5/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.

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