Saul Chrem (@schrem) profile picture

Founder @ https://xitter.cf/t.co/O4TUYuS1Gc Emprendedor @Endeavor_global

Joined June 2010
Photo by Saul Chrem (@schrem)Photo by Saul Chrem (@schrem)Photo by Saul Chrem (@schrem)Photo by Saul Chrem (@schrem)Photo by Saul Chrem (@schrem)
Saul Chrem retweeted
Claude has discovered a previously unknown enzyme system hidden in the DNA of bacteriophages. Beside the enzyme’s gene sits a long array of repeating DNA—a structure that looks somewhat similar to CRISPR. We don’t yet understand what this system does, but only a handful of known systems share its features, and all of them are able to cut, copy, and paste DNA. Historically, the discovery of such programmable systems has helped revolutionize medicine. CRISPR, for instance, is now the foundation of genetic medicines. But it will take much more work to learn what this system does, and whether it can be put to similar use. Read more: anthropic.com/news/claude-di…
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Nueva frontera se abre
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: anthropic.com/news/model-har…
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Saul Chrem retweeted
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: nature.com/articles/s44284-0…
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Here's Stripe's letter to investors explaining its acquisition of OpenRouter (LEAKED)
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Saul Chrem retweeted
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Saul Chrem retweeted
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.
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Saul Chrem retweeted
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. youtu.be/A12AVongNN4
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Saul Chrem retweeted
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. youtube.com/watch?v=IJ9FyX2x…
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Saul Chrem retweeted
"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."
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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).
Anthropic's @_sholtodouglas describes the concept of a software-only singularity: "It's one where the models are far better at digital tasks than they are at physical ones. And so we see rapid change in the digital world with models, and relatively little change in the physical world. So information and software changes dramatically, and this ends up having some pretty weird effects." "It means that maybe like the drivers of what have been the last couple decades of progress in the economy turn around." "And I think we'll see that flow on into the physical world but at a delay. So you get much better at doing chip design. You get much better at training AI models. AI models get a lot faster. Chips get a lot better. The general economy gets a lot more efficient because the sort of information and message parsing that is much of the rest of the economy ends up becoming much more efficient." "But at the same time you don't yet have robots providing limitless physical abundance. Science probably progresses really fast up to the degree that you need interaction with labs or larger particle colliders or something like this. And then you go, 'Okay well I need to build the robots.'"
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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)
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Replying to @DudespostingWs
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.
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Saul Chrem retweeted
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.
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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
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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%
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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
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No estamos tan mal :)
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
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(1/8) One guy’s experiment drove Peru to become the world’s #1 blueberry exporter and #2 overall producer. 🫐 This story is crazy:
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