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Build the ultimate money printer! 💸✨ Plink Lab Trigger chain reactions, multiply balls, and watch billions explode! Pure visual spectacle, so addictive you can't stop. 🎰🔥 🎮 Check it out on Steam👇 #PlinkLab #IncrementalGame #IndieGame

14,547 次观看 • 1 个月前 •via X (Twitter)

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i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.

Sudo su

39,574 次观看 • 2 个月前

Everyone ranks L2s by TVL. But it’s the laziest scoreboard in crypto. It tells you where money is sitting, not who’s actually winning. Here’s what I look for 👇🏻 ◢ TVL = Vanity Metric Parked capital is not a business. A chain can hold billions in deposits and still bleed money every month covering its own costs. TVL measures where liquidity is camping today, not whether the chain earns, profits, or survives a real bear. for years we ranked these networks by the one number that says the least about their survival. ◢ The Pie Is Shrinking Here’s the part nobody says out loud. Total L2 revenue has been falling, not growing. After Dencun crushed data costs in 2024, fees collapsed with them, and the money these chains collectively earn dropped hard from its peak. So, this stopped being a land grab a while ago. It’s now a fight for share of a market that’s actively getting smaller, which changes everything about who actually wins. ◢ Rented Users Don't Count In a shrinking market, only one question matters: can you get users without paying for them. Incentives, airdrops, points farms, none of that is growth, it’s renting volume by the day. The second the rewards stop, the mercenaries leave and the chain goes silent. We already watched it happen to 50+ rollups that are functionally dead now, billions in “TVL” that vanished the moment emissions dried up. ◢ Distribution Beat Tech The one chain that actually solved this didn’t out-engineer anyone. The tech is good, not magic. What it really did was plug into a userbase it already owned and route millions of verified accounts onto the chain by default, at zero acquisition cost. Distribution beat technology, again. It's the most repeated lesson in this entire industry and still the one founders refuse to learn. ◢ The Real Ranking Stack the three majors up by who earns, who profits, and who keeps their users, instead of who has the most money parked on them, and the ranking flips from the one everyone repeats. I put them side by side in the video with the revenue, the profit, the users, and a flat verdict on each. So, in a market that’s contracting, would you rather own the chain with the most capital sitting on it, or the one that still earns when every incentive in crypto finally switches off?

Onur 🍌🦍

17,447 次观看 • 2 个月前

Wall Street burns billions trying to predict Bitcoin. A 28-year-old self-taught coder in Warsaw made $377,000 by not even trying. He'd lost money on three trading bots before this one. Each looked perfect on paper, then started losing money the moment he ran it for real market. So he built bot that doesn't trust itself. His wallet: The truth is simple: you can't predict the next five minutes of Bitcoin. It's a coin flip. Anyone selling you a "prediction" is selling you nothing. So he stopped predicting. The bot hunts the moments the crowd is wrong instead. Here's the part that makes the money, and it's the opposite of what everyone builds. Any strategy can be made to look amazing on past data. On a 5-minute chart, most of them are just lucky, not real - and they stop working fast. So the bot treats every strategy it finds as fake until it proves otherwise. Each one has to pass a hard test: > test it on old data → test it on data it's never seen → try to break it on purpose → cut it down to the one thing that matters → run it forward → keep it only if it still works Last round, 10 of its 12 "winning" strategies turned out to be fake. It kept the 2 that actually worked and dropped the rest. And it never stops - building, testing, and dumping strategies around the clock. What worked yesterday can stop working today, so the second one starts losing, it gets cut before it costs you a thing. The result: $433,000 across 2,955 trades All his old bots tried to be right. This one just tries to catch itself being wrong - and that's why it's still alive. Bookmark this article below - it's the breakdown that explains why your last bot died. It's pruning and trading right now. Copy its wallet and skip to the edges that survived:

cvxv666

27,854 次观看 • 1 个月前

Wow. Classic Jensen style, he ended the Nvidia vs. custom ASIC competition for good. 🫡 The level of confidence with which he explains. 🎯 He was answering to UBS research analyst question on how custom ASICs will affect NVIDIA or how they are going to compete with custom ASIC. Basically he says - Your custom chip is a science project in a world where NVIDIA is building revenue-generating AI factories. While the competition is still desperately trying to copy Nvidia's last generation, their roadmap is already at the limits of physics. They don't just sell silicon; they deliver the entire, impossibly complex platform that the industry has already surrendered to and standardized on. When you must bet hundreds of billions on your company's future, there is no alternative—they are the only platform to build on, and everyone knows it. Full transcript below. Question by the Analyst - "Jensen, I wanted to ask about customer ASIC. And I ask because if, you know, we listen to some of the same CSPs that you put up on that slide, and we listen to some of the companies who are making custom ASICs, some of the deployment numbers sound pretty big. So, I wanted to just hear your position, how you're going to compete with custom ASICs, how they can possibly compete with you, and maybe how some of your conversations with these same customers would sort of form your view in terms of how competitive custom ASIC will be to you." Jensen's response "First of all, just because something gets built doesn't mean it's great. Number two, if it's not great, all of those companies are run by great CEOs who are really good at math. And because these are AI factories, it affects your revenues, not just your costs. It affects your revenues, not just your cost. It's a different calculus. Every company only has so much power. You just have to ask them. Every single company only has so much power, and within that power, you have to maximize your revenues, not just your cost. So this is a new game. This is not a data center game, this is an AI factory game. So when the time comes, that simple calculus, as I was using yesterday, that simple math that I was showing yesterday, still has to be done. Which is the reason why so many projects are started and so many are not taken into production. Because there's always another alternative. We are the other alternative, and that alternative is excellent. Not normal excellent, as you know. Everybody's still trying to catch up to Hopper. I haven't seen a competitor to Hopper yet. And here we're talking about 40x more. And so our roadmap is at the limits of what's possible. Not to mention we're really good at it and completely dedicated to it. A lot of people have a lot of businesses to do. I've got this one business to do. And we're all in on this. 35,000 people doing one job. Been doing it for a long time. The depth of capability, the scope of technology, as you saw yesterday, is pretty incredible. And it's not about building a chip; it's building an AI factory. We're talking about scale up, scale out. We're talking about networking and switches and software. We're talking about systems, and these system architectures are insane. Even the systems themselves. Notice, 100% of the computer industry, 100% of the computer industry has standardized on NVIDIA's system. Why? Because try to build an alternative. Building the alternative is not even thinkable because look at how much investment we put into building this one. And so even the system's hard. What people used to think, system is just sheet metal. Hardly sheet metal. 600,000 parts, it's hardly sheet metal. And so all of the technology is hard. We're pushing every single dimension to the limit because we're talking about so much money. The world is going to lay down hundreds of billions of dollars of investment in the next just a couple of two, three years. Let's do the thought experiment. Let's say you want to stand up a data center and you want it to be fully operational in two years' time. When do you have to place the PO on that? Today. So let's suppose you have to place a hundred billion dollar PO on something. What architecture would you place it on, literally based on everything you have today? There's only one. You can't reasonably build out giant infrastructures with hundreds of billions of dollars behind it, hoping to turn it back on and to get the ROIC on it, unless you have the confidence that we are able to provide you. And we can provide you complete confidence. And singularly so. We're the only technology company where if I had to go place a hundred billion dollars on an AI factory... Oh, that's interesting, I did. Literally the only company who's willing to place hundred billion dollar POs across the industry to go build it out. And you guys know, that's our, that's the depth of our supply chain. And we are, and we have. Give me another. Give me another one that has that depth and that length. And now to the point where we've got to go and work with the supply chain upstream and downstream to prepare the world for hundreds of billions of dollars working towards trillions of dollars of AI infrastructure build-out. Our partnerships with power companies and all of the cooling companies, the Vertivs, the Schneiders, our partnerships with BlackRock... The partnership network necessary to prepare the world to go build out trillions of dollars of AI infrastructure, that's undergoing as we speak. What architecture and what ASIC chip do you go select? That doesn't even make sense. It's a weird conversation even. And so, I think that one, the game is quite large. The investment level, therefore the risk level, is quite high. And so the certainty that you're selecting the best is quite important. And the certainty that you can execute, vital. We are the company you can build on top of. We're the company, we're the platform that you can build your AI infrastructure on. And we're the company that you can build your AI infrastructure strategy on. And so, I think it includes chips, but it's much, much more than that." --- From 'GTC Financial Analyst Q&A' session (full link in comment)

Rohan Paul

208,032 次观看 • 9 个月前