Saul Chrem

Founder @ Emprendedor @Endeavor_global

[Saul Chrem @schrem]

Nueva frontera se abre

[Anthropic @AnthropicAI]

Today, we're kicking off the first phase of the research preview for Model Hardware Standard (MHS): a new standard for AI agents to safely operate physical equipment in scientific research and advanced manufacturing.

Read more: https://t.co/XQ2y9EW7Af

[Yohan @yohaniddawela]

For six months, Google Maps sent a slice of drivers in ten American cities down deliberately slower routes.

About 30 seconds slower on average, and under 2% of trips were touched. The experiment ran on roughly 100 of the most congested road segments in each city, switching on and off day by day so each city acted as its own control. The results are out now in Nature Cities, from Google Research with collaborators at Berkeley and Stanford.

The reasoning behind it is old and slightly counterintuitive. Every navigation app does the same thing: it finds you the fastest route right now, for you alone. When millions of phones do that at once they all pour onto the same handful of arteries, and those arteries stop being fast. Transport economists have been writing about the gap between what's good for one driver and what's good for the network since the 1950s. Nobody had tested a fix at city scale with real drivers, because you'd need to steer routing for a large share of a city's traffic, and about three companies on Earth are in that position.

So Google put a penalty on the worst segments during their worst hours, which nudged the routing engine towards alternatives of similar road class and comparable travel time, then measured what happened across the whole network on weekdays between 7am and 8pm.

On the targeted segments, traffic moved roughly 2% faster in the median city. Los Angeles got 4.56%, Atlanta 3.30%. Fuel burn on those stretches dropped between 0.5% and 1%. Across every road that saw a change in traffic, which covers around 80% of each city's driving, speeds rose 0.35%, reaching 0.5% during the morning and evening peaks. Total travel time on affected trips fell 0.69%. Their Bayesian model put the probability that the speed effect was genuinely positive at 99.8%.

That adds up to more than 1,000 tonnes of CO2-equivalent saved per year, per city, in most of the places studied. Cars and vans account for around 10% of global carbon emissions, and the average driver spends about 2.6 years of their life behind the wheel, so a fraction of a percent across an entire road network is a serious amount of fuel.

Per driver, the saving comes to about 0.25% of an average journey, roughly one fortieth of the normal day-to-day wobble in how long the same commute takes. Nobody in those ten cities noticed anything, in either direction, including the people sent the long way round.

The authors are careful about what they haven't shown. They measured the immediate effect, not what happens once drivers work out the freed-up route is quicker and pile back onto it, which is the standard way road improvements get eaten. Their penalty scheme was deliberately crude, so the ceiling on the approach is unknown. And the underlying Google Maps data is commercially confidential, so nobody outside can check the numbers.

The finding sitting underneath all this is that a private company's routing algorithm has become a piece of transport infrastructure, tunable in the way a traffic light or a congestion charge is tunable. The obvious follow-up experiments are the ones a city government would want to run, not the ones a mapping company would.

link to full article: https://t.co/RlRlDLCSER

[Eric Newcomer @EricNewcomer]

Here's Stripe's letter to investors explaining its acquisition of OpenRouter (LEAKED)

[jack @jack]

https://t.co/WFWSz5jEpc

[Anthropic @AnthropicAI]

New Anthropic research: A global workspace in language models.

Of everything happening in your brain right now, only a tiny fraction is consciously accessible—thoughts you can describe, hold in mind, and reason with.

We found a strikingly similar divide inside Claude.

[Mira Murati @miramurati]

Today we're sharing our work on interaction models. A new class of model trained from scratch to handle real-time interaction natively, instead of gluing it onto a turn-based one.
https://t.co/MoS5s4cm60

[Claude @claudeai]

Bronze: Maieutic by Paula Vasquez-Henriquez from Chile

An educational coding tool that requires you to think before you type. Students can't write code until they can explain what they're building and why.

https://t.co/heZ6zSo9fs

[Saul Chrem @schrem]

@Jason I’m in

[a16z @a16z]

"Speed wins."

"You have to be willing to commit to being fast. You can't have long bureaucratic processes. You can't have a risk-averse posture."

@pmarca explains the OODA loop — and why the fastest operator controls the narrative in business, media, and politics:

"There's a framework called the OODA loop, originally developed for fighter pilots and later for broader military strategy."

"It stands for observe, orient, decide, act. It's basically the decision-making cycle."

"If speed is the thing that matters, then the person who gets through that cycle the fastest is the one who's going to win."

"If you can have a sustainably faster OODA loop processing cycle than the next guy — think about what happens… You operate and make a decision within an hour. The other guy is still inside his own OODA loop when you make your decision. He's only halfway through his process and now has to start over. You've changed the parameters of what's going on."

"This is also a big explanation for what's happened in traditional media."

"The New York Times has its own OODA loop, and it's like 24 hours to go through its process."

[Sholto Douglas @_sholtodouglas]

Default case right now is a software only singularity, we need to scale robots and automated labs dramatically in 28/29, or the physical world will fall far behind the digital one - and the US won’t be competitive unless we put in the investment now (fab, solar panel, actuator supply chains).

[Yoshua Bengio @Yoshua_Bengio]

Today we’re releasing the International AI Safety Report 2026: the most comprehensive evidence-based assessment of AI capabilities, emerging risks, and safety measures to date. 🧵

(1/17)

[Global Statistics @Globalstats11]

More info

Croatian freediver Vitomir Maričić held his breath underwater for 29 minutes and 3 seconds, setting a new Guinness World Record for the longest breath held voluntarily underwater. The attempt took place in a 3-meter-deep pool at the Bristol Hotel in Opatija, Croatia, in front of five official judges and around 100 spectators.

His record not only shattered the previous mark by nearly five minutes, but also surpassed the breath-holding abilities of many marine mammals, including bottlenose dolphins and rivaling harbor seals. For context, the average person can hold their breath for just 30 to 90 seconds, and even elite freedivers typically max out around 10 to 11 minutes without oxygen assistance.

To achieve this, Maričić used a technique called denitrogenation, breathing pure oxygen for 10 minutes before the attempt. This supercharged his blood with oxygen and flushed out nitrogen, allowing his body to store nearly five times more oxygen than normal. While this method is permitted under Guinness rules for oxygen-assisted breath holds, it still demands extreme physical and mental control.

FYI, I focus on adding context to social media posts that often lack it. If you're interested in seeing posts with added clarity and background, consider following me.

[tobi lutke @tobi]

I really like the term “context engineering” over prompt engineering.

It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM.

[World of Statistics @stats_feed]

Average 5G download speed (Mbps):

🇵🇪 Peru - 48.2
🇮🇩 Indonesia - 59.7
🇦🇷 Argentina - 59.9
🇵🇱 Poland - 80.1
🇨🇿 Czechia - 91.3
🇹🇭 Thailand - 99.7
🇮🇹 Italy - 121.2
🇬🇧 UK - 124.4
🇵🇭 Philippines - 136.5
🇺🇸 USA - 138.2
🇳🇱 Netherlands - 140.1
🇭🇺 Hungary - 141.5
🇩🇪 Germany - 143.2
🇨🇦 Canada - 144.1
🇵🇷 Puerto Rico - 146.3
🇷🇴 Romania - 154.9
🇬🇷 Greece - 155.2
🇯🇵 Japan - 156.5
🇨🇱 Chile - 158.4
🇪🇸 Spain - 160.9
🇮🇪 Ireland - 166.9
🇧🇪 Belgium - 171.4
🇦🇹 Austria - 177.2
🇦🇺 Australia - 182.3
🇲🇽 Mexico - 188.5
🇨🇭 Switzerland - 189.5
🇿🇦 South Africa - 191.2
🇻🇳 Vietnam - 217.8
🇫🇷 France - 222.6
🇫🇮 Finland - 224.4
🇸🇦 Saudi Arabia - 227.4
🇵🇹 Portugal - 234.2
🇭🇷 Croatia - 239.3
🇳🇴 Norway - 244.9
🇮🇱 Israel - 261.3
🇩🇰 Denmark - 273.4
🇸���� Sweden - 274.6
🇦🇪 UAE - 298.4
🇧🇬 Bulgaria - 300.4
🇮🇳 India - 301.6
🇲🇾 Malaysia - 322.7
🇧🇷 Brazil - 346.4
🇸🇬 Singapore - 376.8
🇰🇷 South Korea - 432.5

[World of Statistics @stats_feed]

Obesity rate (% of adult population):

🇳🇷 Nauru: 61%
🇨🇰 Cook Islands: 55.9%
🇰🇮 Kiribati: 46%
🇰🇼 Kuwait: 37.9%
🇺🇸 USA: 36.2%
🇸🇦 Saudi Arabia: 35.4%
🇹🇷 Turkey: 32.1%
🇪🇬 Egypt: 32%
🇦🇪 UAE: 31.7%
🇳🇿 New Zealand: 30.8%
🇨🇦 Canada: 29.4%
🇦🇺 Australia: 29%
🇲🇽 México: 28.9%
🇿🇦 South Africa: 28.3%
🇦🇷 Argentina: 28.3%
🇬🇧 UK: 27.8%
🇮🇱 Israel: 26.1%
🇻🇪 Venezuela: 25.6%
🇮🇪 Ireland: 25.3%
🇬🇷 Greece: 24.9%
🇪🇸 Spain: 23.8%
🇷🇺 Russia: 23.1%
🇵🇱 Poland: 23.1%
🇳🇴 Norway: 23.1%
🇩🇪 Germany: 22.3%
🇫🇮 Finland: 22.2%
🇧🇷 Brazil: 22.1%
🇧🇪 Belgium: 22.1%
🇫🇷 France: 21.6%
🇸🇪 Sweden: 20.6%
🇳🇱 Netherlands: 20.4%
🇦🇹 Austria: 20.1%
🇮🇹 Italy: 19.9%
🇩🇰 Denmark: 19.7%
🇨🇭 Switzerland: 19.5%
🇸🇨 Seychelles: 14%
🇹🇭 Thailand: 10%
🇳🇬 Nigeria: 8.9%
🇵🇰 Pakistan: 8.6%
🇰🇪 Kenya: 7.1%
🇮🇩 Indonesia: 6.9%
🇨🇳 China: 6.2%
🇦🇫 Afghanistan: 5.5%
🇰🇷 South Korea: 4.7%
🇯🇵 Japan: 4.3%
🇮🇳 India: 3.9%
🇧🇩 Bangladesh: 3.6%
🇻🇳 Vietnam: 2.1%

[World of Statistics @stats_feed]

Diabetes rates:

1. 🇵🇰Pakistan: 30.8%
2. 🇰🇼Kuwait: 24.9%
8. 🇪🇬Egypt: 20.9%
10. 🇶🇦Qatar: 19.5%
12. 🇲🇾Malaysia: 19%
14. 🇸🇦Saudi Arabia: 18.7%
17. 🇲🇽Mexico: 16.9%
26. 🇹🇷Turkey: 14.5%
27. 🇧🇩Bangladesh: 14.2%
48. 🇱🇰Sri Lanka: 11.3%
53. 🇿🇦South Africa: 10.8%
54. 🇮🇶Iraq: 10.7%
55. 🇺🇸United States: 10.7%
56. 🇮🇩Indonesia: 10.6%
57. 🇨🇳China: 10.6%
60. 🇪🇸Spain: 10.3%
63. 🇹🇭Thailand: 9.7%
64. 🇮🇳India: 9.6%
71. 🇮🇷Iran: 9.1%
74. 🇵🇹Portugal: 9.1%
78. 🇧🇷Brazil: 8.8%
83. 🇳🇵Nepal: 8.7%
86. 🇰🇵North Korea: 8.6%
96. 🇨🇦Canada: 7.7%
107. 🇵🇭Philippines: 7.1%
113. 🇰🇷South Korea: 6.8%
116. 🇯🇵Japan: 6.6%
127. 🇦🇺Australia: 6.4%
129. 🇮🇹Italy: 6.4%
132. 🇬🇧United Kingdom: 6.3%
135. 🇳🇿New Zealand: 6.2%
139. 🇻🇳Vietnam: 6.1%
153. 🇷🇺Russia: 5.6%
162. 🇦🇷Argentina: 5.4%
165. 🇫🇷France: 5.3%
169. 🇪🇹Ethiopia: 5%
183. 🇰🇪Kenya: 4%
186. 🇳🇬Nigeria: 3.6%

% of people age 20-79 who have type 1 or type 2 diabetes

[Saul Chrem @schrem]

No estamos tan mal :)

[World of Statistics @stats_feed]

Number of ex-presidents & prime ministers imprisoned since 1900:

🇮🇹 Italy: 10
🇵🇰 Pakistan: 8
🇰🇷 South Korea: 7
🇭🇺 Hungary: 6
🇧🇷 Brazil: 5
🇲🇷 Mauritania: 5
🇲🇱 Mali: 5
🇦🇷 Argentina: 5
🇧🇩 Bangladesh: 5
🇮🇶 Iraq: 5
🇯🇵 Japan: 5
🇻🇪 Venezuela: 5
🇩🇿 Algeria: 4
🇧🇬 Bulgaria: 4
🇺🇦 Ukraine: 4
🇬🇷 Greece: 4
🇵🇪 Peru: 4
🇬🇹 Guatemala: 4
🇦🇱 Albania: 4
🇷🇴 Romania: 3
🇲🇳 Mongolia: 3
🇹🇷 Turkey: 3
🇳🇪 Niger: 3
🇫🇷 France: 3
🇰🇬 Kyrgyzstan: 3

[Latinometrics @LatamData]

(1/8) One guy’s experiment drove Peru to become the world’s #1 blueberry exporter and #2 overall producer.

🫐 This story is crazy:

[Peter H. Diamandis, MD @PeterDiamandis]

Who’s up for an extra few centuries of life?

A team of scientists in Spain have succeeded in mapping the genome of a jellyfish known for its ability to cheat death by rebirthing itself
https://t.co/cLOxFrU0Jj

[Haseeb Qureshi ＞|＜ @hosseeb]

Just dropped part 1 of a new podcast: @naval and I ask @VitalikButerin all the questions you had about Ethereum but were too afraid to ask.

https://t.co/tIT4N3ysJK