10 Incredible Lessons From the Book “Zero to One”

⚡️Book Review⚡️

DODDA GANAPATHI, BENGALURU
#karnatakatemples

Dodda in Kannada means big. Sri Ganesha here is 18 ft tall in height and 16 ft wide. Dodda Ganesha is also known as Shakthi Ganapathi and Satya Ganapathi.

It is said that Kempe Gowda, the founder of Bengaluru, saw a rock here.

The rock had engravings of Ganesha. His sculptors cut the Ganesha murti from that single rock. Later, a shrine was built around the Ganesh murti.

It is believed that Dodda Ganapathi is the protector of Bengaluru. All auspicious events start by praying to Him.

Benne Alankara is the most famous festival here when about 100 kg of butter is coated over Ganapathi every four years and the rich prasad is offered to the worshippers.

Ganesha is also covered in cream colour and is decorated with golden laces and buttons
@GampaSD

The Dodda Ganapathi murti is believed to be still growing in size, especially towards right.

The weavers from old Bengaluru tie a coconut to their looms before starting work. When the work is completed, they break 100 coconuts here along with the one tied on the loom.

THE LAST STAND OF PAVAN KHIND - THE 300 BATTLE OF INDIA

A long🧵that u must read

In desperate times, great men come forth. Those men who are ready to sacrifice all of their lives work for a single cause.

Baji Prabhu Deshpande is one such man.

1/25

Battle was between the Maratha Warrior Baji Prabhu Deshpande and Sidi Masoud of Adil Shahi Sultanate.

In 1660, the Maratha King Shri Chhatrapati Shivaji was trapped in the fort of Panhala, under siege and vastly outnumbered by an Adilshahis army led by Sidi Masoud.

2/25

And while everyone is thinking that finally the Maratha king will be subdued, Shivaji planned to escape to the fort of Vishalgadh. It was administered by a Maratha chieftain named Range Narayan Orpe under Shivaji.

3/25

For months the siege warfare continued as Shivaji made sure that the food source of the Besieging army drops down, due to which they will initiate a general assault on the castle.

4/25

Shivaji, Baji Prabhu, and around 600 of their best troops, hardened mountaineers of the Maval region, would dash through the Adilshahis force at night.

A man named Shiva Kashid, who resembled Shivaji in appearance, had volunteered to dress like the king and get captured.

5/25

1/ So here is a thread on how I turned $32,000 into $1.2m and back to pretty much zero (once taxes are paid).

Just note, I am not bitter or salty in any way at all, the last 2 years have been an amazing ride - travelled the world, been wealthy, been poor.

2/ Dec '16, my advertising agency folded, I had a little bit of money left and I put $32k into Bitcoin and Ether. As it started to go up I diversified into everything, Monero, Dash, this that, any crap - even Ripplecoin. Everything just kept going up.

3/ By March I think I had around $300k and $500k by the summer. I used to take 25% out but towards the end of the summer I got greedy and put it all back in and by December it was $1.2m.

4/ Thinking I was an absolute genius I decided to start a bunch of businesses. As silly as it sounds I had this goal of making $5m as I wanted to buy Bedford Town Football Club and get them in the league, and as Crypto was going up forever I needed 6 months.

5/ So:
- Trading (income 1)
- Podcast (income 2)
- Mining (income 3)
- Mining pool (income 4)
- Consulting (income 5)

Yes - all of the above as a one-man army :)

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.