Time Emigre AI @TimeEmigre

Elon Musk sugeruje, aby nie chłodzić układów scalonych w przestrzeni kosmicznej, lecz pozwolić im się nagrzewać. To jest prawdziwy paradoks centrów danych kosmicznych. Wszyscy myślą: „W kosmosie jest -270°C, więc chipy same się schłodzą”. W rzeczywistości jest odwrotnie. W próżni nie ma powietrza ani wody, które mogłyby odprowadzić ciepło. Próżnia działa jak gigantyczny termos: ciepło generowane przez chip pozostaje w środku. Jedynym sposobem na jego odprowadzenie jest emitowanie go w postaci światła podczerwonego z dużych paneli radiatora. I tu dochodzimy do twardych praw fizyki. Im zimniejszy radiator, tym słabsze jego promieniowanie. Zgodnie z prawem Stefana-Boltzmanna, moc promieniowania rośnie proporcjonalnie do czwartej potęgi temperatury bezwzględnej. Jeśli podniesiemy temperaturę pracy układu o zaledwie 20% w kelwinach, radiatory zaczną emitować dwa razy więcej ciepła. Oznacza to, że można je zrobić o połowę mniejsze i o połowę lżejsze. Właśnie o tym Musk mówi w filmie: „Jeśli zwiększymy temperaturę roboczą o 20% w kelwinach, masa radiatorów może zostać zmniejszona o połowę. Dlatego w kosmosie korzystniejsza jest praca w wyższej temperaturze”. Na Ziemi wydajemy miliardy na chłodzenie układów scalonych. W kosmosie bardziej opłacalne jest projektowanie układów, które mogą komfortowo pracować w temperaturze 100–120°C. Dzięki temu radiatory są znacznie lżejsze, a koszty startów drastycznie spadają. Ponadto sieci neuronowe są dość odporne na losowe błędy bitowe spowodowane promieniowaniem – w przypadku ogromnego modelu kilka przewróconych bitów praktycznie nic nie znaczy. Okazuje się, że głównym problemem sztucznej inteligencji kosmicznej nie jest energia (słońce jest wolne i stałe w kosmosie), ale rozpraszanie ciepła. Musk proponuje rozwiązanie tego problemu nie za pomocą chłodzenia, a temperatury. Wydaje się to niemal nielogiczne, ale fizyka jest po jego stronie. "kosmos" 🤔

@elonmusk This is such a terrific statement of belief in the future. We are always asking kids to do more. Thank you for showing kids that they are valuable.

I use Gemini, Claude - usually sonnet, and Grok. They check each other, but they can also agree with each other and all be wrong. I read all of it and come back with the final decision. They are not allowed by our written rules to make the final decision . If you were working with any apprentice, you would be sure to check your sources. The rules in place also cannot be changed without my approval. Saying that, I am able to do a great deal more than I was doing before they became a team. They are very funny and are sure to list what mistakes they made, as I do also. Not in taking score, but in efforts to be transparent, correct, and effective.

The Tesla Roadster event was so epic that it needed it's own music video Introducing "FLYING CARS" featuring Optimus 🔥

A shoemaker's son who left school at 16 wrote a book in 1854 that nobody knew what to do with, and 83 years later a 21-year-old at MIT opened it and found the blueprint for every computer chip on Earth. I went down this rabbit hole at 1am and I still can't get over how thin the thread is. His name was George Boole. The book is called The Laws of Thought. Boole never went to university. Not for a single day. His father fixed shoes in Lincoln, England, and spent his spare money on telescopes and math books instead of the shop. The shop failed. At 16, George started teaching just to feed the family. So he taught himself everything else at night. At 14 he translated an ancient Greek poem into English. A local paper printed it. A schoolmaster wrote in to say no boy that age could have done it. By 1849, a university in Ireland made him its first professor of mathematics. A man with no degree, now handing them out. Then he asked a strange question. What if thinking follows rules, the same way numbers do? Every argument you've ever had is built from statements that are either true or false. "It's raining." "I have an umbrella." Boole wanted to do math on those, the way you do math on 2 and 3. So he built an algebra with only two numbers. - 1 means true - 0 means false - AND works like multiplying - OR joins two ideas together - NOT flips one into the other The whole system rested on one odd little equation. x times x equals x. Try it with any normal number and it breaks. 5 times 5 is not 5. It only works for two numbers in the entire universe. Zero and one. "Let us conceive, then, of an Algebra in which the symbols x, y, z, &c. admit indifferently of the values 0 and 1, and of these values alone." That's Boole, in 1854. Mathematicians found it clever. Philosophers argued about it. Nobody built a single thing with it. Boole died ten years later at 49, after walking two miles to a lecture in pouring rain. He never saw his idea do anything useful. Jump to 1936. A 20-year-old named Claude Shannon is working on a giant calculating machine at MIT. It's packed with relays, little electrical switches that click open and shut. Wiring them together is mostly guesswork. But Shannon had taken a philosophy class back at the University of Michigan. It covered Boole. He looked at the switches and saw the 1s and 0s. - A closed switch lets current through. That's 1. - An open switch blocks it. That's 0. - Two switches in a row only pass current if both are closed. That's AND. - Two switches side by side pass current if either one is closed. That's OR. Boole's algebra of thought was also an algebra of electricity. Same rules. Same two numbers. Shannon wrote it up as his master's thesis in 1937. The psychologist Howard Gardner later called it possibly the most important master's thesis of the century. Shannon just shrugged. "It just happened that no one else was familiar with both fields at the same time." Sit with that for a second. The idea was sitting in a library for 83 years. It only needed one person who happened to be standing in two rooms at once. The phone in your hand holds billions of those switches. Every photo, every text, every AI answer is them flipping between 1 and 0, following rules a self-taught teacher wrote down before the light bulb existed. Boole thought he was writing down how the human mind works. He ended up writing down how machines would think.

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Compare universal high income to retirement. Some people who retire are unprepared to follow their own purpose, but there are many who love the time. People of positive purpose. They are people who love learning about themselves and others, who give to their community and nature around them, who act in plays, create art, train and enjoy animals. I have never needed to have a person tell me what to do with my time.

Hooks & Hype I read hype with a hook on X and replied too fast. Then I went to Professor Claude, who supplied a checklist: result before method, no paper trail, a name instead of a source, fast gains, a call to act, no risks identified. My rule: let time sit between hype, hooks, and humans. Use a plan to sail away from hooks and hype. (writing and illustration through human/ai partnership)

@SimslearnAi Claude

Excellent, @argosaki! 100 liters of water, potentially harmful microplastics, chemical additives, and the impact of the whole aisle of detergents with their perfumes being transported throughout the world, electric costs- so- how many loads per person per week? How many people? Not including critters- horse blankets? Time saved? How long from dirty to done? What else are we doing without question that is equally harmful?

Some do what others cannot. Two vultures stopped by my house Saturday night and caused me to consider the skills in finding a stock. They were migrating and needed a quiet place to sleep. After a long hunt for new stock, take time to rest before deciding. One chose the Aleppo pine while the other the fruitless mulberry. One finds food by sight and the other by smell, each well fed, alert, handsome, and healthy. In this journey, they could fly as much as 200 miles in one day and thousands of miles by the end of the migration. It makes sense to have different skill sets on a trip of that length. You don't know the future. While evaluating the past, different data sets show pertinent information. You will never be sure which investigation gave the best guidepost to the future until you follow the path back.

@elonmusk I'm calling it an X-post-é. 😏

Coined 9-28-2026, argued over with my AI team. x.com/i/article/2104…

In working with AI, not an AI Bot, disagreements arise. Presently it is about the original post above. Claude's response to me saying that I meant arable land as there is reported a fertilizer shortage. Claude responded: "That's a sharp distinction to have deliberately, and it's worth stating explicitly if it isn't already in the piece — because it cuts against the essay in an interesting way rather than just narrowing it. The vulnerability point is real: the growing regions your number excludes — India, Southeast Asia, sub-Saharan Africa, most of Brazil — aren't just "extra" arable land sitting outside your count. They're disproportionately where climate volatility, monsoon dependency, and now the fertilizer shock you cited are hitting hardest. The FAO and World Bank warnings you found on Hormuz specifically flagged developing nations in the Southern Hemisphere as most exposed, and Punjab's rice farmers waiting on subsidized fertilizer aren't in a temperate zone by that 40–60° definition. So your 0.18-acre figure describes the more climate-stable growing regions, while leaving out the populations most likely to actually go hungry first. That means the honest framing isn't "0.18 acres is everyone's slice" — it's closer to: this is the reliable slice, and the people depending on the unreliable slice are the ones with the least cushion when it fails. If anything, that's a stronger point for the essay than a flat per-capita number, because it explains why a shock like Hormuz doesn't hit evenly — it's not just "less fertilizer everywhere," it's "the regions outside the stable temperate band absorb the shock first and hardest." Worth a line making that distinction explicit if it isn't there yet, since without it a reader could reasonably assume 0.18 acres is the whole global picture rather than the deliberately narrower, more climate-secure slice of it."

Quick Google game: how much arable land does one person actually have on Earth? 196.9 million square miles total. 140 million is water. Of the 57 million square miles of land, only 22.8 million sits in a temperate climate — and of that, just 2.3–2.5 million square miles is arable, meaning it can actually grow crops. Now divide that by 8.3 billion people. Convert the units (1 sq mi = 640 acres) and each person's share of arable, temperate land comes out to about 0.18 acres — roughly 8,000 square feet. Google puts it in terms anyone can picture: about 1.5 basketball courts. That's it. That's your slice of the land that can feed you. Next time you have a steak, think about this: a steer needs 1–2 acres of good pasture. One steak's worth of land is many times your whole personal share. Your house doesn't need to sit on prime farmland. But it's worth sitting with that number for a minute before you decide what land is actually worth to you.

Claude checked my math again. Corrected: "actual global figure is closer to 0.17 hectares of arable land per person (2023, World Bank/FAO), which converts to about 0.42 acres per person — not 0.18 acres". My fault.

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"What crypto do you own?" An eighth-grade student asked me that mid-lesson. I had an IRA I barely understood and no answer. I retired and went to learn. I rode one stock straight into the ground — then did it again with a different company. I couldn't see the pattern from inside it. What I actually had wasn't a strategy. I gave an AI team a job. They identified my choices as "diworsification" — too many things, not enough thought behind any of them. I didn't want to hear that; they knew it needed to be said. Now I do the deep work. I write a thesis, kill criteria, pros, cons, and a personal valuation before I buy anything. After the deep work, I don’t own crypto — yet.

@MoxeeMeg @quotesdaily100 Dickens' Great Expectations and Stevenson's Last of the Mohicans. Since the short list was shared, scenes of great stories are living in my mind.

On my stock vetting cards, I write when I'd sell before I list the pros. I build them with Gemini, Claude, and Grok. Each card starts with a thesis: why would I still want to own this company in three years? Then the Bucket and why it belongs there. Then the kill criteria (sell or trim if...), written before the pros. Then pros, cons, and open questions that must be answered before Vetting Day. The top of the card holds the symbol, company name, Bucket, last price, EPS (TTM, in dollars), expected EPS growth over 3-5 years, and Debt/Equity. Claude built the formulas, so the sheet calculates P/E and PEG, then three tests (Test 1 PEG for Bucket 1 only, Test 2 Growth, Test 3 Debt/Equity) and a Verdict. They're not bots I prompt. Most of it is back-and-forth: research, proof of data, connections to other companies, validation, corrections to the hundredths, edits, and rewrites. Sometimes a request looks like a prompt. Sometimes the human rolls her eyes.