1/ This is the Strava heatmap of running around the world. It's truly fascinating, and gets to the core of how certain places, particularly European cities, are designed for outdoor activity while others aren't. Let's look more closely
2/ Let's look at the Bay Area and New York City to get a glimpse of US cities. The Bay, despite being sprawled out, has running routes connecting the entire peninsula down to South Bay.
NYC is denser, but people run everywhere - park, coast, bridges, even in the middle of town.
3/ Cities in Europe - Paris, Barcelona and London, for example - follow similar patterns - several core runnable boulevards with many tributaries that encompass the entire landscape.
4/ "European" cities outside Europe, like Tel Aviv and Istanbul are Tel Aviv are similar. Beautiful coastal runs with smaller lanes branching out into the city. Gorgeous, well planned places.
5/ Israel, in my experience, is a place known for being well spread out and not having clusters of big cities. The data backs this up, and Israel is one of the only countries I found which seem entirely runnable, not just in pockets.
6/ This gets me to my core point. I've always been annoyed by Indian cities because, regrettably, they're made up of pockets of activity and un-runnable roads - like ill-fitting puzzle pieces.
7/ We see little islands of planned / closed neighborhoods or lakes that are far removed from each other. The spines of the city aren't pedestrian-friendly at all.
8/ If you think density is to blame, that's not true. Dense Asian cities can be very well connected. Take a look at Tokyo, Shanghai or Singapore for example. Even in comparison to their European counterparts, there's less prominent spines and just a web of interconnected roads.
9/ Even though Strava data is biased towards countries with more smartphones, the story it paints, empirically, rings true. I wish more Indian cities strived to be more runnable.
My favorite city though was this one: with one singular long continuous run by the river. Guess?
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HUGE Immigration News! The birthright citizenship executive order Trump signed today means children of non-citizens or non-permanent residents will not be US citizens.
Children of Indians who are in the 100+yr green card wait WILL NOT be US citizens.
I think the claim that I am somehow happy about this is preposterous. This is a terrible injustice to millions of legal immigrants who have gainfully contributed to the US for decades, held back solely because of their country of birth.
HUGE: DeepSeek R1 from China reaches o1 level performance! Most important paper of 2025.
They show that using pure RL using GRPO can teach models to reason better and better with more tokens: the first at this scale.
Here are the 4 top research learnings from the paper:
1/5
1. Distilling more powerful models like R1 on smaller base models (Qwen / Llama) outperforms large scale RL training on those base models themselves (like QwQ-32B).
2/5
2. The upper bound of model performance requires even more powerful base models and larger scale RL to break out of the plateau.
3/5
— It's been 2yrs since the priority date has moved past Feb '22
— Indian EB-1 petitions stayed flat this year, at ~12k
— The backlog moved forward by ~200/mo.
The EB-1 backlog is basically stuck.
Let's break it down..
1/12
What is the EB-1?
Indians under the standard EB-2/3 green card category have a 100+yr long wait. This is because of the 7% country cap by category.
This was the initial misconstrued old remark by Sriram that was taken out of context and set off this whole H-1B drama.
2/12
What is the EB-1?
The EB-1 is one of the few faster options. 3 categories: extraordinary ability, researchers and multinational managers.
Those in the EB-2/3 line can transfer their priority date while "upgrading" to an EB-1. This CAN stagnate the line from moving forward.
The Superhuman AI for Poker is a timeless research paper.
Poker, unlike Go/Chess, is an imperfect info game. Pluribus, made by now OpenAI engineer, beat 5 pros decisively in 10,000 poker hands, winning 48 milli big blinds/game.
Made a mini demo on rock-paper-scissors too:
1/5
Like humans, Pluribus confirms that limping, calling the “big blind” rather than folding or raising, is suboptimal.
However it disagrees with the wisdom that “donk betting” (starting a round by betting when one ended the previous betting round with a call) is a mistake.
2/5
Pluribus uses Monte Carlo counterfactual regret minimization (MCCFR) which samples actions and adjusts their probabilities, unlike MCTS for Go/ Chess.
It uses <128GB memory on 3 Intel Haswell CPUs and takes 20s/hand, half the time of a human.