Brazilian alternative asset manager Patria and minority partner Ashmore agreed to sell one of Colombia’s largest solar parks to power generator Isagen for an undisclosed amount

BREAKING: HELL YEAH! CNN boldly defends their reporter Kristen Holmes after the White House brought up her kids to smear her after she dared ask Trump about his alleged mistress. This is how you stand up to this demented bully... “Kristen Holmes is one of the most respected and accomplished journalists covering the White House. This afternoon, she did her job and asked the President of the United States a tough, relevant, and newsworthy question on behalf of the American people," CNN said in an official statement. "Public officials are free to challenge reporting they disagree with, but personal attacks on journalists for asking questions are beneath the office and inconsistent with the principles of a free press. We stand firmly behind Kristen and reject these attacks in the strongest possible terms," the network added. Yesterday, Holmes asked Trump about a viral speech from Senator Jon Ossoff in which he stated that Trump "doesn’t want to do the job” and would rather “build his ballroom and travel with Natalie on their apparently defenseless flying palace gifted by the emir of Qatar." The reference to Harp — the "executive assistant" who many are now speculating might be Trump's mistress — enraged the President and he lashed out at Holmes, repeatedly telling her to be "quiet," calling her "disrespectful" and "fake news." The memo went out to MAGA land to back up Trump's attack, and the official White House Rapid Response account on X targeted Holmes, writing: "@KristenhCNN: Someday, your children will come across your disgusting and inhumane question. They will be sickened and embarrassed to have a parent be so callous and vindictive. It’s quite troubling." We commend CNN for standing up in the face of this disgusting smear campaign against an upstanding reporter. There is really only one big takeaway here: reporters need to start asking more questions about Natalie Harp. Please ❤️ and share to urge the press to ask more questions about Natalie!

I think some of the biggest uses of AI will come from work we currently don't bother doing. Too expensive. Not enough time. A potentially useful idea, but nobody available to investigate it. I recognize this from working in finance. You finish a reporting cycle knowing there are questions you would have liked to spend another day on. Then the next deadline arrives. You deal with what needs doing and leave the rest, even though a closer look might have been worthwhile. I wonder how that changes when investigating those questions costs very little. We would still need to check the work and take responsibility for the conclusions. But we could afford to ask more questions, follow up on smaller discrepancies and revisit assumptions that otherwise sit untouched for another year. The Jevons paradox argument is familiar by now. What interests me is what businesses actually choose to do with the lower cost. Reviewing every transaction instead of a sample, for example, or investigating a problem that was previously too small to justify someone's time. My bet is that there is a lot of demand we cannot measure yet because doing the work has never made financial sense. Reading the Anthropic Institute's paper on economic scenarios for AI brought me back to this. Its extreme scenario is extraordinary, but I found the middle one interesting enough: a substantially larger economy while most work still happens without AI. These are scenarios, of course. I wouldn't build a portfolio around a particular GDP number for 2030. What I take from it is that AI doesn't have to replace entire professions to have a very large economic effect. Then there is research, which is probably where my imagination runs furthest ahead of what I can put into a financial model. What could an engineering team attempt if it could explore many more designs before choosing which ones to build? How much could a small research team accomplish with analytical resources it could never previously afford? Physical testing would still take time and money. Plenty of ideas would fail. I just suspect we would attempt more, and eventually some of those attempts would produce things worth having. This is a big part of why I keep coming back to AI infrastructure. Broadcom and Marvell, memory, networking, power and cooling. We research these businesses separately, but they are serving different parts of the same system. If AI takes on more useful work across the economy, that system has to support it. The difficult part is figuring out who earns attractive returns from supplying it. Customers can get tremendous value from AI while an infrastructure shareholder earns very little. Our Flex analysis is a recent example of why I can like the business and still want a better entry price. But I remain extremely bullish on the broader opportunity. I suspect that, a few years from now, businesses will routinely do work they wouldn't even consider starting today. I'd rather judge the infrastructure opportunity against that possibility than assume today's chatbot habits tell us how much capacity we will eventually need.

ChatGPT 6 Astra is the holy grail It is by far the greatest AI model ever made, and can seriously be considered AGI It one-shot the top down multiplayer extraction shooter for me. All graphics, sounds, gameplay done through Blender and Unity Here's the most impressive part: 1. It's faster than GPT 5.6 sol 2. It did this in less credits 3. It did it with significantly higher quality Cheaper per task, faster, significantly better. Basically no trade offs. The trifecta. The holy grail. It's picking up all my existing work and just destroying it. Crushing everything in its way. Finding novel insights, solutions no model has ever thought of. Here's how I'd use it if I were you: Include as much context, with as little direction as you can when prompting the model What I mean by that is, let the model decide how to take action. Let the model breathe. Let it be autonomous. Let is find novel solutions. Let's face it, the model is smarter than us. Us telling the model how to do work is stupid. It's like me telling Michael Jordan how to take shots. Ask more questions than directions you give. You should be asking the model 10x more questions than directions. Questions should be: • How do you approach this problem? • What are different solutions we can use? • What are things I'm not thinking about here? I call it reverse prompting. You should be relentlessly reverse prompting What an incredible time to be alive