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Ana Sayfaya Dön

Solid decisions.

R_Reels🔞

747,559 subscribers

26,298,569 görüntüleme • 1 yıl önce •via X (Twitter)

Anya Rossi• Live Now

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Slick profil fotoğrafı

Slick1 yıl önce

This man is a drug dealer 100%

ThatYotaGuy.eth (Pre-Merge OG) profil fotoğrafı

ThatYotaGuy.eth (Pre-Merge OG)1 yıl önce

GTA 6 is looking so realistic

Universe Crave profil fotoğrafı

Universe Crave1 yıl önce

A 16-year-old Mexican teenager was murdered... His friends brought his coffin to the place where he always played football and made him score one last goal.

The Random Guy profil fotoğrafı

The Random Guy1 yıl önce

This scene k!lls me every time 😂

Salibessi profil fotoğrafı

Salibessi1 yıl önce

My only reaction to this

The Random Guy profil fotoğrafı

The Random Guy1 yıl önce

Behind the scenes of John Wick's intense bike fight sequence 🔥

Prudy🐐🥊 profil fotoğrafı

Prudy🐐🥊1 yıl önce

Why he carrying that stuff

Crazy Vibes profil fotoğrafı

Crazy Vibes1 yıl önce

He just try to help her mother 😂

Crazy Vibes profil fotoğrafı

Crazy Vibes1 yıl önce

He didn't even inside home 😄

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GEMINI 3 LAUNCH IS HERE I got a SNEAK PEEK at Gemini 3 with Logan Kilpatrick (Google Deepmind), and it might be the most POWERFUL vibe-coding tool on the planet. A little breakdown: 1. Anyone can build 3D and casual games now You can vibecode full, playable 3D video games generated in minutes. Actual games with physics, characters, controls, and loops you can remix instantly. Pure insanity. I can see founders and brands spinning up games on the fly to ride trends and drive growth. 2. Intelligent apps are becoming the default We built apps where reasoning, memory, and multi-step planning were baked in from the start. Once you’re building apps with ACTUAL intelligence baked in, there’s a whole wave of new opportunities that weren’t possible before. 3. Gemini acts like a creative partner You describe the idea, Gemini fills in the gaps, challenges decisions, proposes alternatives, and iterates in real time. 4. Vibe coding hits a new level Gemini 3 can generate assets, code, game logic, UI, and narrative in one flow. Tools like Claude and Cursor feel fast. This feels like the next layer, the one where a single builder can compete with full teams. Logan Kilpatrick and I pushed Google Gemini 3 hard, and the outputs were solid. A few times we had to give it a few extra prompts but it took feedback really well. I think 1 year ago, a lot of people discounted Google in the AI arms race. Can you discount them anymore? Doubt it. After this, it feels like they at best leading, at worst leading. What do you think of Google's AI efforts/Gemini 3 My biggest takeaway was how it just felt like Gemini 3 had a little more vibe coding horsepower than anything I’ve used.

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Elon Musk didn't have a background in mechanical engineering or rocket science when he founded Tesla and SpaceX. He didn't. He was once asked how he packed so much knowledge into his brain so quickly. His answer: "It is important to view knowledge as sort of a semantic tree — make sure you understand the fundamental principles, i.e. the trunk and big branches, before you get into the leaves/details or there is nothing for them to hang on to." Most of us do it backwards. We go straight for the leaves - the tactics, the hacks, the step-by-step methods - before we've built any trunk to hang them on. The information doesn't stick. -We read a book, forget it within a week. -We take a course, can't apply it a month later. -We collect knowledge without ever building understanding. Musk builds the trunk first. The science backs this up. Neuroplasticity is the brain's ability to rewire itself and works like a tree. Learning something new is a series of attempts, failures, and adjustments. Neural connections that result in success grow stronger. Unproductive connections eventually break off like dead branches. This is why understanding fundamentals isn't just academically satisfying, it's mechanically how the brain learns best. When you have a solid trunk, new information has somewhere to attach. Without it, everything slides off. Here's what that looks like in practice: Instead of learning how to build a rocket engine, Musk learned why rockets work the way they do - the physics, the materials science, the thermodynamics. Once those principles were in place, the specific engineering decisions became far easier to evaluate, question, and improve upon. Instead of memorizing investing methods, Charlie Munger built what he calls a "latticework of theory" from psychology, history, mathematics, physics, philosophy, and biology and then used that latticework to make better decisions across all of them. This is the difference between linear and residual knowledge. A method works once, for one problem. A principle works hundreds of times, across dozens of contexts you haven't even encountered yet. Harrington Emerson, the American efficiency engineer, put it plainly: "As to methods, there may be a million and then some, but principles are few. The man who grasps principles can successfully select his own methods. The man who tries methods, ignoring principles, is sure to have trouble." So the next time you sit down to learn something, whether it's a new skill, a new industry, or a new discipline, resist the pull of the tactics. Ask instead: -What are the trunk and big branches here? -What are the first principles that, once understood, make everything else easier to figure out? That's how residual knowledge works.

Elon Musk didn't have a background in mechanical engineering or rocket science when he founded Tesla and SpaceX. He didn't. He was once asked how he packed so much knowledge into his brain so quickly. His answer: "It is important to view knowledge as sort of a semantic tree — make sure you understand the fundamental principles, i.e. the trunk and big branches, before you get into the leaves/details or there is nothing for them to hang on to." Most of us do it backwards. We go straight for the leaves - the tactics, the hacks, the step-by-step methods - before we've built any trunk to hang them on. The information doesn't stick. -We read a book, forget it within a week. -We take a course, can't apply it a month later. -We collect knowledge without ever building understanding. Musk builds the trunk first. The science backs this up. Neuroplasticity is the brain's ability to rewire itself and works like a tree. Learning something new is a series of attempts, failures, and adjustments. Neural connections that result in success grow stronger. Unproductive connections eventually break off like dead branches. This is why understanding fundamentals isn't just academically satisfying, it's mechanically how the brain learns best. When you have a solid trunk, new information has somewhere to attach. Without it, everything slides off. Here's what that looks like in practice: Instead of learning how to build a rocket engine, Musk learned why rockets work the way they do - the physics, the materials science, the thermodynamics. Once those principles were in place, the specific engineering decisions became far easier to evaluate, question, and improve upon. Instead of memorizing investing methods, Charlie Munger built what he calls a "latticework of theory" from psychology, history, mathematics, physics, philosophy, and biology and then used that latticework to make better decisions across all of them. This is the difference between linear and residual knowledge. A method works once, for one problem. A principle works hundreds of times, across dozens of contexts you haven't even encountered yet. Harrington Emerson, the American efficiency engineer, put it plainly: "As to methods, there may be a million and then some, but principles are few. The man who grasps principles can successfully select his own methods. The man who tries methods, ignoring principles, is sure to have trouble." So the next time you sit down to learn something, whether it's a new skill, a new industry, or a new discipline, resist the pull of the tactics. Ask instead: -What are the trunk and big branches here? -What are the first principles that, once understood, make everything else easier to figure out? That's how residual knowledge works.

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