Pulled back from the support nicely. The next hurdle is at 2860

Jubilant Foodworks https://t.co/ZBMsHWfg7R

In Academia, people aren’t always supported when they need it. I want to share what @WesternU did when my husband died right after the pandemic shutdown. It should be shared with other uni’s as a model for empathy and proactive care when someone is in crisis.
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#AcademicChatter

The chorus of this song uses the shlokas taken from Sundarkand of Ramayana.

It is a series of Sanskrit shlokas recited by Jambavant to Hanuman to remind Him of his true potential.

1. धीवर प्रसार शौर्य भरा: The brave persevering one, your bravery is taking you forward.

2. उतसारा स्थिरा घम्भीरा: The one who is leaping higher and higher, who is firm and stable and seriously determined.

3. ुग्रामा असामा शौर्या भावा: He is strong, and without an equal in the ability/mentality to fight

4. रौद्रमा नवा भीतिर्मा: His anger will cause new fears in his foes.

5.विजिटरीपुरु धीरधारा, कलोथरा शिखरा कठोरा: This is a complex expression seen only in Indic language poetry. The poet is stating that Shivudu is experiencing the intensity of climbing a tough peak, and likening

it to the feeling in a hard battle, when you see your enemy defeated, and blood flowing like a rivulet. This is classical Veera rasa.

6.कुलकु थारथिलीथा गम्भीरा, जाया विराट वीरा: His rough body itself is like a sharp weapon (because he is determined to win). Hail this complete

hero of the world.

7.विलयगागनथाला भिकारा, गरज्जद्धरा गारा: The hero is destructive in the air/sky as well (because he can leap at an enemy from a great height). He can defeat the enemy (simply) with his fearsome roar of war.

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.

This post is pretty bizarre, but it manages to hit on so many false beliefs that I've seen hurt junior data scientists that it deserves some explicit

(1) The notion that R is well-suited to "building web applications" seems totally out of left field. I don't feel like most R loyalists think this is a good idea, but it's worth calling out that no normal company will be glad you wrote your entire web app in R.

(2) It is true that Python had some issues historically with the 2-to-3 transition, but it's not such a big deal these days. On the flip side, I have found interesting R code that doesn't run in modern R interpreters because of changes in core operations (e.g. assignment syntax).

(3) "Most of the time we only need a latest, working interpreter with the latest packages to run the code" -- this is where things get real and reveal some things that hurt data scientists. If this sentence is true, it's likely because you don't share code with coworkers.

(3) Really is a broader issue in data science: people only think of what they need to do their work if no one else existed and code was never maintained. Junior data scientists almost always operate on projects they start from scratch and don't have to maintain for long.