Sai Chintala

Assistant Professor of Marketing (SMU)

[Sai Chintala @saichandc]

@gauravsabnis Everytime on the way to Catskills from Ithaca, we would pass through Delhi and take a pic of the Delhi signboard.

[Billie Jean King @BillieJeanKing]

This is it!

I'm graduating from college today! 🎓💛🖤

#Classof2026 @CalStateLA

[Historywali @historywali]

Eat all mangoes. Eat every mango you can. The small, juicy, fibrous, nameless ones you pluck off that old tree down the gully. The planted-a-Dussheri-seed-but-never-grafted Dussheri-ish mango from your masi’s garden in Bhopal.
Blushing Sindhuras. Sweet Kesars. Yes eat as many of those beautiful Alphonsos from Devgad packed carefully in cardboard boxes as you can. But also eat Pairi, Neelam, Ratna. And Sindu - a cross between the Ratna & the Alphonso which is slowly gaining more ground because climate change has wreaked havoc on the finicky Alphonso - ask farmers, yields have been down for a few years now, talk to farmers.

Eat with the season.
Eat the early mangoes from the south - Mankurad, Badami, Banganapalli, Imam Pasad in late April-May.
Eat your middle-India mangoes - Kesar, Bombay Green, and also Malgova and Mallika which are late season bloomers from the south, and your Himsagar, Gulabkhaas, beauties from Malda and Murshidabad - try to lay your hands on a Champa, Saranga, or Kohitoor in May-June!
And go both hands in, into piles of Amrapali, Chausa, Malihabadi Dussheris, Langdas in July.

This is just the tip of the mango iceberg, there are so many more loved & delicious varieties - India has near 1,500 varieties of mangoes.
Why would you eat just one? Of course have your favourites but also look at our beautiful biodiversity - please cherish it! Eat widely. Eat greedily. Eat because these mangoes are so delicious. Eat in RESISTANCE TO LOSS, eat like these mangoes might disappear because some of them already are!

[Alex Imas @alexolegimas]

Good lord what a video. One of the coolest things I've seen all year.

h/t @pontus_rendahl

[Yohan @yohaniddawela]

A single GPU can now calculate hundreds of global weather scenarios in under 60 seconds. The exact same task requires a supercomputer and hours of brute-force physics.

Google DeepMind recently released WeatherNext 2. The model beats the previous state-of-the-art system on 99.9% of weather variables across a 15-day forecast window. It achieves this massive jump in accuracy using a new modelling approach called a Functional Generative Network.

Meteorologists categorise weather data into two buckets:

1. Marginals are isolated data points, like the precise temperature at a specific location or the wind speed at a certain altitude.
2. Joints are the massive, interconnected systems that form when all those individual elements interact.

The researchers hid the joint systems from the model during training. They only taught it the isolated marginals. When they turned it on, the model skillfully predicted the massive, complex systems anyway.

The architecture forces an 87-million-dimensional output distribution through a 32-dimensional mathematical bottleneck. To survive this severe constraint and still produce accurate individual data points, the neural network has no choice but to learn the underlying physics linking everything together. It figures out the weather because that’s the most efficient way to solve the maths.

The practical results are immediate. The model gives forecasters a full 24-hour advantage in tropical cyclone tracking compared to the previous leading system. It maps extreme wind speeds and heatwaves with unprecedented precision.

We’re watching a pretty big shift in predictive capabilities. The machine is deducing the structural reality of planetary weather from isolated fragments of data.

[John Myles White @johnmyleswhite]

This is a remarkably good example of how an A/B test might violate SUTVA assumptions and end up with huge network effects that make the test results useless. One customer in the “small test” group sees the price change, rage posts about it and suddenly the whole world is in test.

[Namita Bhandare @namitabhandare]

At Kranti, India’s first residential school for the daughters of sex workers, 80% go on to higher education. This year Mahek has got into Columbia University!
In Mind the Gap, do read: Not rescue but revolution: Inside Kranti’s radical classroom
https://t.co/ujKVzbQjSU

[The Paperclip @Paperclip_In]

This is a photograph of Albert Einstein with an unassuming Indian man you probably haven’t heard enough about. He spent his life working on one idea: women should be able to live with dignity and make their own choices. Thread.

1/14

[Reads with Ravi @readswithravi]

Leonardo da Vinci on excellence:

[WIRED @WIRED]

In an email to customers, Amazon announced that it would be ending service for Kindle devices older than the 2012 edition. Those devices will lose access to the Kindle Store. https://t.co/9XuUik6say

[Barton Gellman @bartongellman]

It's one thing to say older devices don't support some new feature or service or operating system. But to make the deliberate choice to brick a device that's working perfectly well, in order to force your customer to buy a new one, is a hostile act of predatory capitalism.

[Revathi @revathitweets]

IT TAKES A LOT TO BE REVATHI…!!!

While the whole country is celebrating the landmark judgement of death penalty for the NINE policemen in the Santhanakulam custodial torture and murder of Jeyaraj and Bennix, you should know about Revathi.

Revathi was a constable in Santhanakulam police station in 2020 when the gruesome incident took place. She was the key witness and the sole reason for all the arrogant officers getting punished today.

The police men involved in the brutality were all big men, Revathi was a small time constable. But she stood unperturbed.

When Magistrate Bharathidasan, who initially investigated this case, arrived at the Santhanakulam police station he had no clue that constable Revathi will help close the case.

"Sir, I will tell you everything, every detail, the truth that is being hidden. But I am the mother of two young girls... can you guarantee the safety of my children and my job?", she had asked the magistrate.

Revathi was on night duty when the cruel incident took place. She witnessed the brutality being inflicted on Jeyaraj and Bennix and narrated every single detail.

She told how SI Balakrishnan, inspector Sridhar and SI Ragukanes, kept beating the father and son with whatever they found, and how they also stomped on their private parts with their shoes.

She remembered their screams. She saw how the officers took pause only to sip alcohol while the victims withered in pain.

When the father and son were semi-conscious, unable to bear it, Revathi asked Jayaraj if he needed anything. She gave him coffee which the officials spilt it immediately.

Revathi couldn’t stand the brutality but being a woman constable there was only so much she could do. She then offered water to the victims.

The so called policemen then stripped Bennix naked, tied his hands and legs separately, and beat him up. They did the same to Jeyaraj. Revathi couldn’t bear the pain of their screams, she left the place.

According to the postmortem report, their entire back was skinned, iron rods were inserted and they bled from their rectums.

When the case was being discussed by the media, when even the CM of Tamil Nadu tried to brush it away, Revathi knew the truth.

The police officials, cleaned up the station of any DNA evidences, erased the CCTV footage and warned everyone to shut-up and not try to become heroes.

But Revathi didn’t just narrate the entire ordeal in exact detail…she even helped the investigating officials obtain other crucial information.

Despite everything being cleaned, Revathi gathered DNA of the victims in crevices of the walls and floors, on furniture and other objects.

She was questioned, threatened, intimidated, bribed and even abused. She stood by justice. She had to go against her colleagues for justice of comman man.

In the time when police force is often looked upon with reasonable suspicion, there are people in uniform like Revathi.

Today court could pronounce death sentence to nine police officers - only because one woman decided she will stand by truth!

Kudos to Revathi, an amazing woman and an absolutely fabulous police officer.

The world is a better place because of her courage.
Salute ma’am 🙏🏽🙏🏽🙏🏽

[Sann @san_x_m]

His name is Sanjiv Chaturvedi.

IFS 2002 batch. Engineer from MNNIT Allahabad.

In Haryana he exposed fake plantation schemes. Illegal tree felling. Misuse of government funds.

He was transferred 12 times in 7 years.

In 2012 he became Chief Vigilance Officer at AIIMS Delhi.

In two years he investigated 200 corruption cases.

Rs 3,750 crore irregularity in campus expansion. Fake medicines being sold inside hospital premises. Corrupt officials at every level.

CBI cases were registered against senior bureaucrats.

In August 2014 he was transferred out of AIIMS.

The health minister who transferred him later became party president of the ruling party.

In 2015 he won the Ramon Magsaysay Award.

He donated the entire prize money to AIIMS for treatment of poor patients.

AIIMS returned his cheque.

He has been in non-field postings for 9 years since then.

16 judges have recused themselves from hearing his cases.

No government in India wants him posted in their state.

That is how you know he is doing his job right.

[Sann @san_x_m]

His name was Manjunath Shanmugam.

He was an IIM Lucknow graduate.

He got a job with Indian Oil Corporation as a sales officer.

His territory was Uttar Pradesh.

He found that petrol pump dealers were adulterating fuel and cheating customers.

He reported it. He sealed the pumps.

On November 19 2005 a petrol pump owner shot him dead outside his office.

He was 27 years old.

The killers were convicted. Sentenced to life imprisonment.

His parents did not get compensation for 15 years.

His college created the Manjunath Shanmugam Trust in his name to fight corruption.

Some men die because they refused to look the other way.

India forgets them too quickly.

[Alvin Foo @alvinfoo]

"𝗔𝗳𝘁𝗲𝗿 𝗜 𝗯𝗲𝗰𝗮𝗺𝗲 𝗽𝗿𝗲𝘀𝗶𝗱𝗲𝗻𝘁, 𝗜 𝗮𝘀𝗸𝗲𝗱 𝗺𝘆 𝗲𝘀𝗰𝗼𝗿𝘁 𝘁𝗼 𝗴𝗼 𝘁𝗼 𝗮 𝗿𝗲𝘀𝘁𝗮𝘂𝗿𝗮𝗻𝘁 𝗳𝗼𝗿 𝗹𝘂𝗻𝗰𝗵. 𝗪𝗲 𝘀𝗮𝘁 𝗱𝗼𝘄𝗻 𝗮𝗻𝗱 𝗲𝗮𝗰𝗵 𝗼𝗳 𝘂𝘀 𝗮𝘀𝗸𝗲𝗱 𝘄𝗵𝗮𝘁 𝘄𝗲 𝘄𝗮𝗻𝘁𝗲𝗱.

On the front table, there was a man waiting to be served. When he was served, I said to one of my soldiers: go and ask that gentleman to join us. The soldier went and conveyed my invitation to him. The man got up, took his plate and 𝘀𝗮𝘁 𝗱𝗼𝘄𝗻 𝗿𝗶𝗴𝗵𝘁 𝗻𝗲𝘅𝘁 𝘁𝗼 𝗺𝗲.

While he ate his 𝗵𝗮𝗻𝗱𝘀 𝘁𝗿𝗲𝗺𝗯𝗹𝗲𝗱 𝗰𝗼𝗻𝘀𝘁𝗮𝗻𝘁𝗹𝘆 and he did not lift his head from his food. When we finished, he said goodbye without looking at me, I shook his hand and he left.

The soldier told me:

Madiba that man 𝗺𝘂𝘀𝘁 𝗵𝗮𝘃𝗲 𝗯𝗲𝗲𝗻 𝘃𝗲𝗿𝘆 𝗶𝗹𝗹, seeing as his hands didn't stop shaking while he ate.-

𝗔𝗯𝘀𝗼𝗹𝘂𝘁𝗲𝗹𝘆 𝗻𝗼! 𝘁𝗵𝗲 𝗿𝗲𝗮𝘀𝗼𝗻 𝗳𝗼𝗿 𝗵𝗶𝘀 𝘁𝗿𝗲𝗺𝗯𝗹𝗶𝗻𝗴 𝗶𝘀 𝗮𝗻𝗼𝘁𝗵𝗲𝗿.

Then I told him:

That man was the warden of the prison where I stayed. After he tortured me, I screamed and cried asking for some water and he came humiliated me, laughed at me and instead of giving me water, he urinated in my head.

He is not sick, he was afraid that I, now president of South Africa, would send him to prison and do to him what he did to me. But I'm not like that, this conduct is not part of my character, nor of my ethics.

′′𝙈𝙞𝙣𝙙𝙨 𝙩𝙝𝙖𝙩 𝙨𝙚𝙚𝙠 𝙧𝙚𝙫𝙚𝙣𝙜𝙚 𝙙𝙚𝙨𝙩𝙧𝙤𝙮 𝙨𝙩𝙖𝙩𝙚𝙨, 𝙬𝙝𝙞𝙡𝙚 𝙩𝙝𝙤𝙨𝙚 𝙩𝙝𝙖𝙩 𝙨𝙚𝙚𝙠 𝙧𝙚𝙘𝙤𝙣𝙘𝙞𝙡𝙞𝙖𝙩𝙞𝙤𝙣 𝙗𝙪𝙞𝙡𝙙 𝙣𝙖𝙩𝙞𝙤𝙣𝙨. Walking out the door to my freedom, I knew that if I didn't leave all the anger, hatred and resentment behind me, I would still be a prisoner."

[P. Sainath @PSainath_org]

1/6. At age 86, P. V. Chinnathambi still runs a unique library in the hilly forests of Idukki in Kerala. He has run that library – 2,000 books, all classics – for 15 years now. The books are borrowed, read and returned by poor Muthuvan Adivasis. All this in Edamalakudi, Kerala’s only tribal panchayat and perhaps its – relatively speaking – lowest literacy spot and least educated region. PARI story link https://t.co/VvjratRss7

[Joy Bhattacharjya @joybhattacharj]

"He died as the victim of his own principles, the principle of non-violence. He died because in time of disorder and general irritation in his country, he refused armed protection for himself.
It was his unshakable belief that the use of force is an evil in itself, that therefore it must be avoided by those who are striving for supreme justice to his belief. With his belief in his heart and mind, he has led a great nation on to its liberation. He has demonstrated that a powerful human following can be assembled not only through the cunning game of the usual political manoeuvres and trickery but through the cogent example of a morally superior conduct of life.
The admiration for Mahatma Gandhi in all countries of the world rests on that recognition."
Albert Einstein

[Karen Tumulty @ktumulty]

My late cousin, who I adored and miss every day, once said to me: Never make fun of someone for mispronouncing a word. It means they learned it by reading.

[Eric Seufert @eric_seufert]

How does Uber find you a ride home from the airport?

In a blog post, Uber outlines three of the models it uses to match riders with drivers at the airport. These models partially form a standalone product called Uber Airports that exclusively supports the airport rideshare use case, which is commercially significant: airport rides account for 15% of global mobility bookings.

The blog post identifies a few reasons why airports present challenges for optimizing rideshare matching:
- Demand is inconsistent / erratic, following the schedule of arriving flights
- Drivers often have to idle in cell phone lots as they wait for matches and can't immediately move to pickup
- At many airports, rideshare pickup is not located at passenger pickup, and riders may have trouble finding it, causing delays (Boston, Austin, and San Francisco are great examples of this)

Uber's goal with Uber Airports is to provide adequate and appropriate supply at airports so that drivers aren't left waiting unnecessarily (which could discourage them from relocating to the airport). The three models outlined in the post are:

- ETR (Estimated Time to Request), which predicts the amount of time a driver will wait in a FIFO queue to receive a ride request. This unified model was trained on ATR (actual time to request) with various features like queue length, weather conditions, etc. in an end-to-end approach, replacing an older, ensemble-based model. The blog post notes that several queues exist based on vehicle types (eg., UberX vs. Uber Black).

- EPH (Earnings Per Hour), which predicts how much money a driver can make by "repositioning" (driving) to the airport. This model takes historical earnings as well as arrival / departure schedules and real-time marketplace dynamics as inputs and uses a Deep GMM (Gaussian Mixture Model) to predict the variance and mean of a distribution of earnings potential. The overall mean of this EPH distribution is surfaced to the driver in the app.

- Driver Deficit, which uses time-series data to forecast any deficits in driver availability at the airport in five-minute intervals for the next 30 minutes. This model uses a Transformer-encoder architecture that can learn long-term dependencies across time-series feature vectors without requiring an autoregressive forward pass. The model applies Convolution layers to align the input sequence lengths with the output, which is a time-series vector of forecasted driver deficits in five-minute intervals. This information is used to make driver summoning decisions.

I was surprised to learn that airport trips make up such a meaningful percentage of revenue for Uber. Given the value to the company, it makes sense that it would dedicate so many resources to efficiently managing airport ride matching. Full blog post linked below.

[From the Hills of Nagaland. @Nagaland_India]

Dedicated to every racist individual in the World