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551 posts

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@realpalebone

"crazed scientist baron" @MNX_fi @ManifoldMarkets

san francisco Katılım Eylül 2019
961 Takip Edilen314 Takipçiler
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Brett Winton
Brett Winton@wintonARK·
on AI and water use. You could ask a frontier model 4 questions per day every day for a year. Or you could eat a single solitary almond.
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Smirkley
Smirkley@Smirkley·
A 5-gallon water cooler jug of water produces about $132 in economic output from data centers, but only about 2 cents from almonds.
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Arram
Arram@arram·
Asked Claude: 'There's a meme called the "fix everything easily switch". What policies do you think are the best candidates for being a real fix everything switch in the US? Give me your top ten, your confidence, your reasoning, and why a given policy has not been implemented.'
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Paul Graham
Paul Graham@paulg·
Sure you can earn a billion dollars. I've been teaching people how to do it for 20 years. The way you do it is to start a company that grows fast. You don't have to do anything bad to make a company grow fast. You just have to make something people want. paulgraham.com/ace.html
Marco Foster@MarcoFoster_

AOC: “There’s a certain level of wealth and accumulation that is unearned. You can’t earn a billion dollars. You just can’t earn that. You can get market power, you can break rules, you can abuse labor laws, you can pay people less than what they’re worth, but you can’t earn that”

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Steve Harvey
Steve Harvey@IAmSteveHarvey·
When they sleep on you… tuck em in💯
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Dustin Gouker
Dustin Gouker@DustinGouker·
I don’t think it’s an exaggeration to call this the greatest piece of prediction market content created to date.
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Garett Jones
Garett Jones@GarettJones·
Garett Jones tweet media
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Ben
Ben@BenShindel·
5/12 of the 2026 Thiel Fellows have a @ManifoldMarkets account and I believe at least 2 are active users!
Thiel Fellowship@thielfellowship

Welcome 2026 Thiel Fellows! WHO ARE THEY? Victor Boyd: Birmingham, AL - @VictorWBoyd Cavalla is on a mission to get anything anywhere in under 5 hours. Starting by building autonomous forklifts, through to developing hypersonic highways. Samuel Carvalho: Recife, Brazil - @samuelclcc Praso is building the new infrastructure for wholesale commerce — powering procurement, credit, and workflow tools for SMBs across underserved areas in Brazil. Nick Dobroshinsky: Sammamish, WA - @NDobroshinsky EveryTicker is democratizing institutional-grade financial research across the entire U.S. stock market, including the thousands of smaller companies Wall Street ignores. Ishan Gupta: Kanpur, India - @ishangpta Juicebox is building an AI recruiter that helps companies make better hiring decisions. Agents that understand real skills and move hiring from guesswork to true meritocracy. Antoni Kiszka: Strzyżowice, Poland - @antoni_kiszka Derpetual is building the infrastructure to create a market for any asset — with leverage. Milan Lustig: Cold Spring Harbor, NY - @HighPriestOfSWO Opt32 is building modern compute infrastructure to put AI onboard objects in the physical world — from robots to cars and drones. Galen Mead: Chapel Hill, NC - @g413n Standard Intelligence is building aligned general learners, pretraining large models to actively explore and learn from the Internet. Aubrey Niederhoffer: New York, NY - @needaubrey Swoop is building the super app for Africa, starting with food delivery in Nigeria and expanding into financial services across the continent. Harry O'Connor: Cork, Ireland - @HarryOC493 Sentient Machines is a research lab building foundational models for robotics that generalize across tasks and environments. Alex Shieh: Salem, NH - @alexkshieh The Antifraud Company is a fraud bounty hunter defending American taxpayers with AI and investigative journalism. Claire Wang: Los Angeles, CA - @clairebookworm Claire is building biologically accurate simulations of entire nervous systems, starting with C. elegans. Developing a simulated brain that researchers can communicate with helps lay the foundation for brain-computer interface (BCI) technology. Kyler Wang: Portland, OR - @kylerywang Action is an artificial intelligence company in stealth.

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Derek Thompson
Derek Thompson@DKThomp·
In Mere Christianity, CS Lewis has an awesome opening riff about how most people know the difference between right and wrong, but they justify acting immorally by appealing to "special exception." They know they shouldn't hit a friend, but what if that friend was being so mean? They know they shouldn't steal a seat a bus, but what if that person got up and created a moment's confusion and then the seat was up for grabs? Etc. When I read this section, I thought a lot about contemporary politics and the way that people justify their politics, not by appealing to higher principles, but rather by appealing to "special exception" to argue that their admitted indecency is justifiable in context. A lot of MAGA vice is justified by special exception. Trump's defenders rarely defend his crookedness directly. They don't say "it's wonderful to use trade policy to enrich the Oval Office, it's really awesome." They say: Well, look, it doesn't really matter, because the left is so dangerous, Biden maybe did something similar 3 years ago, Democrats would do the same in power, and so forth. I heard something similar in that NYT conversation everybody's talking about. You even see it in the headline: ‘The Rich Don’t Play by the Rules. So Why Should I?’ Why, hello, special exception. When you start arguing that stealing food and French paintings is justifiable in the context of political protest in an age of prevailing distrust, you're similarly not arguing *for* any kind of a universal principle. Nobody actually wants 300 million people stealing fruit from the grocery store. Nobody actually wants every Louvre visitor trying to rip a Manet off the walls. These virtues don't scale. (Because they're not virtuous!) Sap that I am, I want us to get to a place where politics is about fighting for what is right and decent, not about justifying what sort of indecent behavior might be somewhat understandable or technically justifiable given the other side's vice or the prevailing levels of indecency. The point is to build the kind of goodness that scales. nytimes.com/2026/04/22/opi…
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Kelsey Piper
Kelsey Piper@KelseyTuoc·
We should ban partisan gerrymandering nationwide. It's bad for our country. Republicans control Congress and should introduce the bill. Democrats will support it.
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Jeffrey Emanuel
Jeffrey Emanuel@doodlestein·
Here’s my recipe. I spin up around 5 Gemini-cli instances in the same project and start them all out with this prompt: First read ALL of the AGENTS .md file and README .md file super carefully and understand ALL of both! Then use your code investigation agent mode to fully understand the code, and technical architecture and purpose of the project. —- Then I queue up this one: I want you to sort of randomly explore the code files in this project, choosing code files to deeply investigate and understand and trace their functionality and execution flows through the related code files which they import or which they are imported by. Once you understand the purpose of the code in the larger context of the workflows, I want you to do a super careful, methodical, and critical check with "fresh eyes" to find any obvious bugs, problems, errors, issues, silly mistakes, etc. and then systematically and meticulously and intelligently correct them. Be sure to comply with ALL rules in AGENTS .md and ensure that any code you write or revise conforms to the best practices referenced in the AGENTS .md file. —- Then when that all finishes I also do this: Ok can you now turn your attention to reviewing the code written by your fellow agents and checking for any issues, bugs, errors, problems, inefficiencies, security problems, reliability issues, etc. and carefully diagnose their underlying root causes using first-principle analysis and then fix or revise them if necessary? Don't restrict yourself to the latest commits, cast a wider net and go super deep! —- Then I kill them and start new ones with the same recipe. Keep doing that round after round of that until they come up clean!
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Jeffrey Emanuel
Jeffrey Emanuel@doodlestein·
Ok, Gemini3 was already weirdly good at finding subtle bugs. But I can now say that Gemini3.1 is even more ridiculously capable of this. I’ve done something like 100 passes of my standard prompt for this sort of thing and it has found so many issues across a bunch of projects.
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Clash Report
Clash Report@clashreport·
Pete Hegseth quoted a fake Bible verse from Pulp Fiction during a Pentagon sermon.
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Peter Wildeford🇺🇸🚀
Peter Wildeford🇺🇸🚀@peterwildeford·
Jensen here is frustrating and wrong. The man wrote off billions so of course he opposes controls. 1. Mythos is a ~10T parameter model trained on Nvidia Blackwell. Despite Jensen's best efforts, China doesn't have Blackwell chips thanks to export controls. Huawei's best chip delivers 1/3 the per-chip performance, at 2.5x the power cost, with yields >12x worse. Jensen calling Mythos "fairly mundane capacity" that's "abundantly available in China" is just plainly false. 2. Dwarkesh is right that the compute ratio matters geopolitically. Maintaining a capability lead during the critical window — even 12-18 months — is the whole point of controls. The difference between China running a thousand vs. a million offensive AI agents is huge. Jensen dodges this entirely. 3. Jensen can't simultaneously argue "controls failed because China innovated anyway" (DeepSeek) AND "we must sell to China or they'll leave our ecosystem." If they'll innovate regardless, selling chips doesn't buy the loyalty he claims. 4. Jensen's ecosystem stickiness point (x86, Arm) is his strongest argument, but it cuts against him: the world is already locked into CUDA. Selling Nvidia chips to China doesn't deepen that - it just gives China better hardware while they build Huawei alternatives regardless.
Dwarkesh Patel@dwarkesh_sp

Distilled recap of the back-and-forth with Jensen on export controls: Dwarkesh: Wouldn’t selling Nvidia chips to China enable them to train models like Claude Mythos with cyber offensive capabilities that would be threats to American companies and national security? Jensen: First of all, Mythos was trained on fairly mundane capacity and a fairly mundane amount of it by an extraordinary company. The amount of capacity and the type of compute it was trained on is abundantly available in China. Dwarkesh: With that, could they eventually train a model like Mythos? Yes. But the question is, because we have more FLOPs, American labs are able to get to this level of capabilities first. Furthermore, even if they trained a model like this, the ability to deploy it at scale matters. If you had a cyber hacker, it's much more dangerous if they have a million of them versus a thousand of them. Jensen: Your premise is just wrong. The fact of the matter is their AI development is going just fine. The best AI researchers in the world, because they are limited in compute, also come up with extremely smart algorithms. DeepSeek is not an inconsequential advance. The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation. Dwarkesh: Currently, you can have a model like DeepSeek that can run on any accelerator if it's open source. Why would that stop being the case in the future? Jensen: Suppose it optimizes for Huawei. Suppose it optimizes for their architecture. It would put others at a disadvantage. As AI diffuses out into the rest of the world, their standards and their tech stack will become superior to ours because their models are open. Dwarkesh: Tesla sold extremely good electric vehicles to China for a long time. iPhones are sold in China. They didn't cause some lock-in. China will still make their version of EVs, and they're dominating, or smartphones, they're dominating. Jensen: We are not a car. The fact that I can buy this car brand one day and use another car brand another day is easy. Computing is not like that. There's a reason why x86 still exists. There's a reason why Arm is so sticky. These ecosystems are hard to replace. Dwarkesh: It's just hard to imagine that there's a long-term lock-in to the Chinese ecosystem, even if they have this slightly better open-source model for a while. American labs port across accelerators constantly. Anthropic's models are run on GPUs, they're run on Trainium, they're run on TPUs. There are so many things you can do, from distilling to a model that's well fit for your chips. Jensen: China is the largest contributor to open source software in the world. China's the largest contributor to open models in the world. Today it's built on the American tech stack, Nvidia’s. Fact. All five layers of the tech stack for AI are important. The United States ought to go win all five of them. in a few years time, I'm making you the prediction that when we want American technology to be diffused around the world—out to India, out to the Middle East, out to Africa, out to Southeast Asia—on that day, I will tell you exactly about today's conversation, about how your policy ... caused the United States to concede the second largest market in the world for no good reason at all.

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