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Instead of watching 1 hour of Netflix tonight, watch this ex-Google Chief Scientist Jeff Dean’s lecture. It’s the clearest explanation I’ve seen of the full AI engineering stack - from building LLMs from scratch all the way to one human coordinating 100 agents. The best part is that it’s useful whether you’ve never touched a model or you’ve been shipping agent systems every day for the past year. Bookmark it & watch the whole lecture this weekend, because it might end up being the most valuable thing you learn all week.

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INSTEAD OF WATCHING AN HOUR OF NETFLIX TONIGHT. This 1-hour Stanford lecture by Joel Peterson will teach you more about negotiation and getting what you want than most people learn in years. Bookmark it and give it an hour, no matter what.

Yes, the rumors are true: The more powerful Notebook experience is now 100% rolled out to ALL Pro users. You might be asking... How exactly do I use it? Here are a few queries to try (ofc dependent on your sources). Hot Tip: bookmark this post! Free users too 😉 For Learning: "Create 5 quizzes of ascending difficulty focusing on a different topic from my sources" "Turn my raw lecture notes into a formatted PDF study guide complete with a 5-question practice quiz." "Add all of the URLs from this class syllabus as sources" For Business: "Calculate revenue growth across each product line and plot the trends" "Read our marketing brief, search the web for competitors, and recommend how we should tune our messaging" For Personal: "I uploaded a bunch of receipts from our kitchen renovation, make a spreadsheet to track all the work and costs" "Help me analyze this property purchase. Look up recent market performance in the neighborhood, predict trends, and make a spreadsheet to model all the data you find"

The panel asked the questions. This post captures one CISO's perspective on the answers. Docker CISO Mark Lechner shares why governing agents where devs work, starting from trusted software, and designing for containment beats chasing perfect prevention.

Taking name suggestions for @Charles_Leclerc and @LewisHamilton's band 🎶✍️ Watch Series 2 Episode 1 of Passenger Princess, now live on @ameliadimz's YouTube channel 📺 #F1#

A 24-YEAR-OLD MED STUDENT BUILT AN OBSIDIAN VAULT THAT KNOWS HIS MEDICAL DEGREE BETTER THAN HE DOES He started the way most people do — one outline note called "Emergência," branching out into ER protocols, pediatric cases, infection pathways, cardiac systems. Nothing organized, just wherever the next lecture took him. Two years in, the outline had turned into something else entirely. Hundreds of interconnected notes, color-coded by system, dense enough that zooming out on the graph looks less like a notes app and more like a star field. He filmed it once, just to show his friends. It's the third time this exact "watch my vault turn into a galaxy" clip has gone viral this month. But the graph was never the point, even if it's what got the views. Two weeks before a cardiology exam, he pointed Claude at the vault and asked it to find every note connected to arrhythmias — scattered across four different semesters, written in four different note-taking phases, never once cross-referenced by him. Fifteen seconds later, he had the entire topic laid out with sources, including a contradiction between two notes from a year apart that he'd never caught. That's the difference between a vault and a graveyard with good lighting. A graph this size isn't something a human rereads before an exam. It needs a system that reads it for him, on schedule, surfacing what's actually connected instead of what just looks impressive zoomed out. He didn't build a study system. He built something that catches what he's already forgotten, before the exam catches it for him.

This is Moonshot AI/Kimi K3's founder & CEO Yang Zhilin at GTC 2026 The 34-year-old researcher studied at Tsinghua, then earned his Carnegie Mellon Ph.D. Then he worked with Jason Weston at Meta AI and Quoc Le at Google Brain. His work also connected him to Yoshua Bengio and Yann LeCun on the topics of reasoning and representation. Eventually, Yang returned to China and co-founded Moonshot AI in Beijing a few years later. His earlier Transformer-XL and XLNet work addressed how language models forget distant context. Moonshot AI carried that research focus forward through Kimi K2’s 1T-parameter mixture-of-experts design. XLNet, was later cited more than 10,000 times and is still his best-known academic paper. Kimi K3 expands the formula with native vision, 1M-token context, and only 16 active experts. His career has become a textbook example of how quickly global research networks can reshape the way countries compete in AI. ---- From "Kimi AI" YouTube channel, (full video link in comment)

Hey Mark, you're worth around $35 million while continuing to land major Hollywood roles. You could donate $25-30 million tomorrow and still live an extremely comfortable life for the rest of your days. At a conservative 4% safe withdrawal rate, even a remaining $5-10 million would generate roughly $200,000-$400,000 a year in passive income, several times what the average family earns. On top of that, you own multiple luxury properties, including a New York penthouse reportedly worth around $7 million, and you're still taking on film roles that pay millions. Lecturing others while sitting on tens of millions yourself is peak Hollywood... As for Elon, his charitable giving has far exceeded your entire net worth tenfold. And as for the idea that he can simply "solve world hunger" by writing one giant cheque only demonstrates your fundamental misunderstanding of how global hunger actually works. It's not a problem that disappears because one wealthy person transfers money into the system. You need a basic economics lesson and a mirror, because your hypocrisy is matched only by your lack of self-awareness.