The fuel injected Brexiter comments on the EU's vaccine problems miss many points.

॥ॐ॥
अस्य श्री गायत्री ध्यान श्लोक:
(gAyatri dhyAna shlOka)
• This shloka to meditate personified form of वेदमाता गायत्री was given by Bhagwaan Brahma to Sage yAgnavalkya (याज्ञवल्क्य).

• 14th shloka of गायत्री कवचम् which is taken from वशिष्ठ संहिता, goes as follows..

• मुक्ता-विद्रुम-हेम-नील धवलच्छायैर्मुखस्त्रीक्षणै:।
muktA vidruma hEma nIla dhavalachhAyaiH mukhaistrlkShaNaiH.

• युक्तामिन्दुकला-निबद्धमुकुटां तत्वार्थवर्णात्मिकाम्॥
yuktAmindukalA nibaddha makutAm tatvArtha varNAtmikam.

• गायत्रीं वरदाभयाङ्कुश कशां शुभ्रं कपालं गदाम्।
gAyatrIm vardAbhayANkusha kashAm shubhram kapAlam gadAm.

• शंखं चक्रमथारविन्दयुगलं हस्तैर्वहन्ती भजै॥
shankham chakramathArvinda yugalam hastairvahantIm bhajE.

This shloka describes the form of वेदमाता गायत्री.

• It says, "She has five faces which shine with the colours of a Pearl 'मुक्ता', Coral 'विद्रुम', Gold 'हेम्', Sapphire 'नील्', & a Diamond 'धवलम्'.

• These five faces are symbolic of the five primordial elements called पञ्चमहाभूत:' which makes up the entire existence.

• These are the elements of SPACE, FIRE, WIND, EARTH & WATER.

• All these five faces shine with three eyes 'त्रिक्षणै:'.

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.

Thread about Shashanka and alleged persecution of the Buddhists. The purpose of this thread is to defy their claims while tackling a few other issues.

(Very long thread but useful) 1/n

Shashanka was a Shaivite king of Gauda with his capital being Karnasuvarna.
He is often accused to be a persecutor of Buddhists by the certain historians prejudiced folks to form a picture that Hindu kings were also against other religions just like the muslim kings. 2/n

Much of Shashanka's life after 619 CE is not very much known to us. Historians primarily cite from a few sources like 1) Banabhatta's Harshacharita and 2) Hsuan Tsung's Si Yu Ki(SYK), both being 7th century texts and 3/n

3) Manjushri Mula Kalpa(MMK), an 8th century text sometimes also dated as a 12th century text. 4/n

The method of accusing Shashanka as a Buddhist hater is established by saying that Shashanka desecrated Buddha's sacred marks, destroyed the Bodhi Tree in Gaya and replaced the Buddha statue w Shiva after which he died as he incurred a mortal flesh-rotting disease 5/n

💬 I often get Q’s like these:

How did you get your first customers for WIP? How did you grow BetaList’s traffic? Etc.

Makers are looking to reverse-engineer success. I see it everywhere.

I don’t think it works that way and the answers to those questions are mostly useless. 💥

I have built dozens of different products over the last couple of years. The vast majority failed. 😭

Surely if I know the answers to these questions, but still fail over and over again, these answers aren’t that useful. 🤷‍♀️

So what’s a better question to ask? 🤔

99.9% of the questions I receive are about the products that did well. In a way that makes sense, because we quickly forget about those that didn’t succeed.

🧠 This is known as survivorship bias.

Focusing on what survived, while ignoring what made it survive in the first place.

The real question, what you really want to know, is this:

What makes @WIP, @BetaList, and to some extent @AllStartupJobs succeed where my countless other attempts failed?

What separates a failed product 👎 from a successful product 👍?

Honestly, I don’t know. I wish I did.

It’s like Steve Jobs said “I’ll know it when I see it.” 👀

Same is true when we make products. We don’t know upfront what will work. But once we see an inkling of a product that does have potential, it’s not that hard to spot.

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