Education on SWINGS

Understanding what is a Swing is crucial in order to understand Market Structure.

It shows us the direction of the market and while it may seem obvious at first sight of the market, many still have trouble defining what a swing is.

॥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:'.

शमशान में जब महर्षि दधीचि के मांसपिंड का दाह संस्कार हो रहा था तो उनकी पत्नी अपने पति का वियोग सहन नहीं कर पायी और पास में ही स्थित विशाल पीपल वृक्ष के कोटर में अपने तीन वर्ष के बालक को रख के स्वयं चिता पे बैठ कर सती हो गयी ।इस प्रकार ऋषी दधीचि और उनकी पत्नी की मुक्ति हो गयी।

परन्तु पीपल के कोटर में रखा बालक भूख प्यास से तड़पने लगा। जब कुछ नहीं मिला तो वो कोटर में पड़े पीपल के गोदों (फल) को खाकर बड़ा होने लगा। कालान्तर में पीपल के फलों और पत्तों को खाकर बालक का जीवन किसी प्रकार सुरक्षित रहा।

एक दिन देवर्षि नारद वहां से गुजर रहे थे ।नारद ने पीपल के कोटर में बालक को देख कर उसका परिचय मांगा -
नारद बोले - बालक तुम कौन हो?
बालक - यही तो मैं भी जानना चहता हूँ ।
नारद - तुम्हारे जनक कौन हैं?
बालक - यही तो मैं भी जानना चाहता हूँ ।

तब नारद ने आँखें बन्द कर ध्यान लगाया ।

तत्पश्चात आश्चर्यचकित हो कर बालक को बताया कि 'हे बालक! तुम महान दानी महर्षि दधीचि के पुत्र हो । तुम्हारे पिता की अस्थियों का वज्रास्त्र बनाकर ही देवताओं ने असुरों पर विजय पायी थी।तुम्हारे पिता की मृत्यु मात्र 31 वर्ष की वय में ही हो गयी थी'।

बालक - मेरे पिता की अकाल मृत्यु का क्या कारण था?
नारद - तुम्हारे पिता पर शनिदेव की महादशा थी।
बालक - मेरे उपर आयी विपत्ति का कारण क्या था?
नारद - शनिदेव की महादशा।
इतना बताकर देवर्षि नारद ने पीपल के पत्तों और गोदों को खाकर बड़े हुए उस बालक का नाम पिप्पलाद रखा और उसे दीक्षित किया।

1/ The first 18 months of starting a company is often life or death. I must've made 5 different companies that each failed within 9 mo. 😭 Each time the company failed I figured out what I could do better. Eventually startup #6 got to $40K/mo by month 18. Here’s what I learned...

2/ Stay focused! Ignore things that are a waste of time: meetups & conferences, meetings with no clear agenda, fundraising if you're not fundraising, reading lots of tech media articles, etc. Every week should feel like significant progress in the first year.

3/ Your first 5 hires will be the difference between life or death. Choose carefully. Be picky. Many of the things we do at the company still are a result of those early hires' legacy. Have fun as a tight knit team. It will change & evolve as you get bigger so enjoy this moment.

4/ Growth may be flat for the first 9 months. It's gonna be okay. Almost every company has experienced this: Airbnb had to sell cereal in-between, Slack failed as a gaming company first, Tesla sold only 147 cars after 6 years! You probably won't be an overnight success either.

5/ In the beginning, do customer support yourself. You will learn a lot about why your product sucks. I did 5,000+ support tickets when it was the two of us. Delight customers & fix things fast while you learn. It will help you build an amazing intuition about your customers.

ARULMIGU SRI PARTHASARATHY SWAMY KSHETRAM, CHENNAI (TN)

Divya Desha where Perumal appears with moustache of a Sarathi (charioteer) to conduct Arjun’s Rath in Kurukshetra war.
Here, Venkatkrishna-Parthasarthy is present with his entire family.

He is flanked by Sri Rukmini, Sri Balarama, Satyaki, son Pradyumn and grandson Anirudha.
It is said that on the request of Raja Sumathirajan, Sri Perumal appeared and stayed here as Arjun’s charioteer (Partha-sarathi).

He is without chakra as he had promised not to wield weapons in Mahabharata war.
The face has scars caused by Bhishma’s arrows on Arjun. Prabhu took the arrows on himself to protect Arjun.

The Naivedyam offered here does not have spices. Ghee is used in it to cure Prabhu’s wounds. Sarkarai Pongal is famous here.

It is said that Rishi Bhrigu found Devi Lakshmi (as Vedvalli) in Alli flower in the mandir pushkarini. Later, Sri Ranganatha came here to marry her.

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.