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Same task. Two AI models. Two receipts. Opus 5: $4.20. Fable 5: $9.60. The cheaper one won. Two days ago Anthropic shipped Claude Opus 5, and the economics of every agent you run quietly changed. Fable 5 is the model people reach for when the job actually matters. Opus 5 just ran the same job at half the cost and beat it. That is not a rounding error. On CursorBench 3.2 it matches Fable's top score within half a percent, at half the cost per task. And the ceiling moved with the floor. ARC-AGI-3, the hardest reasoning benchmark going, sat frozen near 7.8% for months. Opus 5 posted 30.2%. Artificial Analysis now ranks it the number 2 model in the world and the strongest reasoning model you can actually buy. The token price? Unchanged. Same five in, twenty-five out per million as the last Opus. You pay the old sticker and get close to double the work per dollar. Now put that against a business that runs agents for money. Every task you billed a client $9 to serve now costs you $4. Every margin you quoted last month just widened, and you did not touch a line of code. Lead-gen, support, coding, all of it got cheaper to run overnight. Anthropic already made the call for its own users. The platform router sends traffic to Opus 5 by default now. The builders on it got the upgrade for free. The ones still wired to last quarter's stack are burning double on every call and filing it under infrastructure. That edge lasts exactly as long as it takes your competitors to read these same numbers. The full head to head, the cost tests, and where Fable still earns its price are in the video. Exactly how to repoint your agents without breaking what works is in the article below.

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OpenAI co-founder Andrej Karpathy just told everyone shipping "AI agents" they're a decade too early. And he's the last person you'd expect to say it. He uses Claude and Codex every day. That's what makes this brutal: "The year of agents" is actually the DECADE of agents. AGI is ~10 years out. Most of what we sell as "reasoning" is a model reciting patterns it memorized off the internet. His frame flips how you should build: "We're not building animals. We're building ghosts." Animals earn intelligence through evolution. LLMs fake it by imitating human text. Different species. Why no agent replaces a real employee yet: no continual learning (tell it something, it forgets) weak computer use not multimodal enough too much memory, not enough generalization That last one is the whole game. Humans reason BECAUSE our memory is bad. Models memorize so well they never learn to think. The builders who get WHY agents fail in 2026 are the ones shipping the agents that actually work (and get paid). The rest are automating boilerplate. 23 minutes. Watch before you build your next agent. 🎥 @karpathy on the @dwarkesh_sp podcast

@0xkkai The most surprising part isn't that the quality improved. It's how quickly our definition of "good enough" becomes obsolete. Every new generation recalibrates expectations almost overnight.

41,000 people starred Karpathy's pattern for notes. nobody thought to apply it to code. here's what came out someone shared their screen and opened something i'd never seen before. it looked like a galaxy. thousands of colored dots connected by lines. i thought it was a visualization demo then he said "this is my codebase. claude mapped the entire thing in under a minute. every file, every function, every connection" i didn't say anything for about 10 seconds he clicked on a random dot. claude instantly explained what that file does, what depends on it, and what breaks if you touch it. with evidence. from the actual code then he asked claude "what's the weakest part of this project?" and claude pointed to a cluster of files connected to almost everything else. "if anything goes wrong, it'll start here." he checked. claude was right and that's when it hit me. this is the exact same pattern from karpathy's paper. take raw sources, compile them into structured knowledge, never go back to the raw files. 41,000 people starred it for notes and documents. nobody told me it works on code too but it makes even more sense for code. your codebase is just another pile of raw files. functions, classes, configs scattered across hundreds of folders. no developer holds the full picture in their head. not even the one who wrote it claude does. in under a minute i spent weeks studying karpathy's pattern and wrote a full breakdown. every prompt, every command, the complete architecture, the token math behind why it saves 70-90% on repeat queries. the article is in the first reply on the video you can see the system running live i test things like this on myself and share the results. follow @0xkkai if you want to see what's next

@Pixel_Neuron The most surprising part isn't that the quality improved. It's how quickly our definition of "good enough" becomes obsolete. Every new generation recalibrates expectations almost overnight.

"$10K/month, one skill, one niche" has been the internet's favorite sentence for a decade. AI just gave it a new engine. The interesting version of this isn't the playbook. It's watching which niches are actually seeing AI compound output versus just automating busywork.

@cipgerx The most surprising part isn't that the quality improved. It's how quickly our definition of "good enough" becomes obsolete. Every new generation recalibrates expectations almost overnight.

I SAID THE NEXT SEEDANCE 2.0 RENDER WOULD MAKE THE LAST ONE LOOK LIKE A ROUGH DRAFT. 120,000 PEOPLE SAW THAT POST. HERE'S THE PROOF A mechanical dragon made of gears and bone, coiling through clouds above a city that shouldn't exist. Two humans on a cliff edge for scale. Then the camera pushes in on its face I stopped the clip at the seven-second mark and sat there for a full minute The texture on every single scale. The clockwork spine twisting through fog. The golden eyes tracking something below the cloud line. A bridge built from the skeleton of something ancient connecting two floating islands Seventeen seconds. One person. No VFX house. No render farm. No team A creature supervisor at a studio told me last week that a single dragon shot at this detail level takes four artists, three weeks, and a six-figure software license before anyone touches lighting This was a Seedance 2.0 prompt and three reference documents Last time I posted a dragon, everyone argued about whether AI video had arrived. That conversation is over. This is what arrived looks like The renders I'm sitting on make this one look early. Every week the ceiling moves. If you showed up for the last one, you already know why you're still here

@pulmencr The biggest shift isn't that AI writes code. It's that it lets experts work directly in the language of their domain instead of translating every idea into implementation details first. That's a fundamentally different way of building software.

I fed a blackbird's song into Claude Opus 5 and asked it to build a real-time 3D spectral visualizer This is what came back, and it looks like a spaceship interface Red spheres pulsing across a 3D acoustic network, lighting up every time the bird hits a specific frequency A live radar chart tracking frequency modulation and spectral flatness the instant each note happens A 3D trajectory showing how pure or noisy every single note is as it shifts over time I didn't write one line of the math. I described what I wanted, and Opus 5 handled the entire signal processing pipeline in one shot Turns out that simple whistle was hiding layered harmonics and shifting spectral patterns this whole time

@0xGenAi What stands out isn't the cable or the lasers - it's the incentive structure. When tiny reductions in latency translate directly into profit, engineering starts competing with physics. That's why markets keep pushing against physical limits.

Google DeepMind CEO Demis Hassabis: "We're only a few years away from AGI." He believes we'll look back at 2026-2027 as the moment the AGI era truly began. In this interview, he explains why. 00:44 - AGI is only a few years away 04:09 - The "Einstein Test" for true AGI 10:42 - AI could be 100× bigger than the Industrial Revolution 25:03 - Agents are becoming part of everyday software 38:06 - The biggest mistake AI builders make today This interview will give you a much clearer picture of where frontier AI is heading. Watch it today.

@velesxbt What stands out isn't the cable or the lasers - it's the incentive structure. When tiny reductions in latency translate directly into profit, engineering starts competing with physics. That's why markets keep pushing against physical limits.

Wall Street once paid $300 million to drill through two mountains and save four milliseconds. Then it replaced the cable with microwave towers to save one more. Then it replaced the towers with lasers. A blink still takes a hundred milliseconds. Nobody involved can perceive the difference. Everyone involved is a billionaire. Michael Lewis wrote the book. He also went on 60 Minutes and delivered one sentence. "The United States stock market, the most iconic market in global capitalism, is rigged." Then he explained how. The cable was built by a company called Spread Networks between 2007 and 2010. Eight hundred and twenty five miles in a straight line from Aurora, Illinois to Carteret, New Jersey. Aurora is where CME futures live. Carteret is where Nasdaq lives. If you can see a price move in Chicago and route your order to New Jersey four milliseconds faster than the next guy, you can buy the stock the next guy is about to buy and sell it back to him a fraction of a cent higher. On billions of shares a day, a fraction of a cent is a business. It paid for itself in twelve months. It was obsolete in thirty six. Brad Katsuyama, a Canadian trader at RBC, was the first person on the buy side to figure out what was actually happening. He would try to buy 10,000 shares of Microsoft. The moment his order left his desk, Microsoft would move. Every single time. He hired an engineer named Ronan Ryan to walk his signal from his terminal to the exchange. Ronan came back and told him a story that was funny in the way a dark joke is funny. His order was hitting one exchange first. Algorithms on that exchange were reading his intent and racing his own signal to the other twelve exchanges to buy the shares before he arrived. He was funding the inventory of the guy front-running him. He was tipping the guy for the service. Katsuyama did the only thing you can do when the market is rigged against you. He built his own market. He called it IEX. The whole thing hinged on a 38-mile spool of fiber optic cable coiled inside a shoebox. The shoebox added 350 microseconds of delay to every order. Long enough to make front-running impossible. Short enough that no human would ever feel it. On Wall Street they call it a speed bump. The exchanges it competed with tried to sue it out of existence. Your order is not a request. It is a signal. In a market built for speed, every signal is a leak. Every leak has a price. And the price is paid by whoever moves slowest, which is, always, you. The book is $15. The interview is free. The mountains are still hollow.

@Di_Krass_ Technologies change quickly, but mathematical truths don't. That's why investing in fundamentals keeps paying dividends while tools, frameworks and model names come and go.

Gilbert Strang, MIT mathematics professor: "MIT paid me for 60 years to teach the math nobody respected. Matrix was a dirty word. Now every neural network runs on it. It's the plain math nobody wanted to learn, until it turned out to run the future." the subject he spent his life on was once dismissed - mathematicians looked down on linear algebra as beneath them. then it quietly became the engine of the entire AI era. every neural network, every model like ChatGPT, is just matrices multiplying matrices - the exact math Strang made simple enough for anyone to learn. same story i keep telling: the foundation is never the flashy part. it's the plain math nobody wanted to teach, until it turned out to run the future. "this is an unforgettable day for me," he said. the last lecture of the man who taught the machines their native language.

@carbonyxxx This is true far beyond quantum mechanics. Some of the most important ideas in science and AI feel deeply counterintuitive at first. Progress often starts when you're willing to trust evidence before intuition catches up.

Ramamurti Shankar, Yale physics professor: "Yale pays me $400K to teach quantum mechanics - and I open every course by telling students it will never make sense. Reality doesn't run on your intuition. Trust the math or stay wrong." most teachers try to make the hard part feel intuitive. Shankar does the opposite: he warns the room it cannot be made to make sense. "there's no way to make it reasonable. it's not a reasonable world out there. I can only tell you what it is." the quantum world doesn't run on everyday logic. "this is not daily life. strange things happen." your intuition isn't just weak here - it's the wrong tool. at that scale you don't get certainty, you get odds. a particle has no definite position waiting to be found - only a wave of probabilities. the outcome isn't there until you look. "it makes sense to me because I've seen it," he tells them. "I have no clue how it sounds to you." he won't promise it'll feel right. only that it's true.

Same task. Two AI models. Two receipts. Opus 5: $4.20. Fable 5: $9.60. The cheaper one won. Two days ago Anthropic shipped Claude Opus 5, and the economics of every agent you run quietly changed. Fable 5 is the model people reach for when the job actually matters. Opus 5 just ran the same job at half the cost and beat it. That is not a rounding error. On CursorBench 3.2 it matches Fable's top score within half a percent, at half the cost per task. And the ceiling moved with the floor. ARC-AGI-3, the hardest reasoning benchmark going, sat frozen near 7.8% for months. Opus 5 posted 30.2%. Artificial Analysis now ranks it the number 2 model in the world and the strongest reasoning model you can actually buy. The token price? Unchanged. Same five in, twenty-five out per million as the last Opus. You pay the old sticker and get close to double the work per dollar. Now put that against a business that runs agents for money. Every task you billed a client $9 to serve now costs you $4. Every margin you quoted last month just widened, and you did not touch a line of code. Lead-gen, support, coding, all of it got cheaper to run overnight. Anthropic already made the call for its own users. The platform router sends traffic to Opus 5 by default now. The builders on it got the upgrade for free. The ones still wired to last quarter's stack are burning double on every call and filing it under infrastructure. That edge lasts exactly as long as it takes your competitors to read these same numbers. The full head to head, the cost tests, and where Fable still earns its price are in the video. Exactly how to repoint your agents without breaking what works is in the article below.

Anthropic just shipped a frontier model at half the price of their best one. Every AI agent you run got cheaper today, and the work you were rationing to weaker models mostly doesn't need to be. Here's the exact routing play. x.com/i/article/2080…

@0xSolty I think the market is starting to reward depth over credentials again. Understanding the fundamentals makes it much easier to adapt as architectures, models and tools change every few months.

My friend applied to 150 tech jobs in two years. No MIT. No Oxford. Last month Anthropic offered him $559,000. I asked him how he does it with zero background. He sent me the exact lecture that taught him how these models actually work. A free Stanford course on diffusion and large vision models, the same tech behind every AI video and image tool people are paying for. I watched it last night. Halfway through, I realized making this stuff is nowhere near as complicated as the gurus selling $500 courses want you to think. Bookmark this. The people who actually watch it will be months ahead of everyone still guessing.

@Neuron_404 Every institution exists because enough people agree on the same story. If AI becomes exceptionally good at creating, adapting and distributing those stories, its influence won't come from replacing people - it'll come from shaping the incentives that people respond to.

Yuval Noah Harari, historian and author of Sapiens: "AIs are hacking the code of human civilization." In his Oxford lecture he explains why: money, laws, religion, whole states are all made of one thing - words. For thousands of years that code was ours alone. "The cows could not open a bank account. The horses could not hire a lawyer." The danger was never a robot with a gun. It's a machine that can write the stories - money, laws, beliefs - better than you, and we may end up living inside fictions no human ever wrote. Bookmark - you'll want to come back to this one.