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Grok 4.20 deploys 4 agents, runs internal debate, peer review & consensus… and it's STILL faster than every other model

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tetsuo

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76,080 просмотров • 6 месяцев назад •via X (Twitter)

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Some guy ran 4 AI agents. 4 sports. $500 each. One week. > NERVE (tennis): $500 → $3,200 (+540%) > PHANTOM (NBA): $500 → $2,928 (+486%) > FROST (hockey): $500 → $2,474 (+395%) > SIEGE (soccer): $500 → $2,182 (+336%) The edge? While ESPN shows you the score with a 15 second delay, these agents see it in 500ms and trade before the market moves. $2,000 in. $10,784 out. The worst agent still returned 218%. THEY DON'T PREDICT BETTER. THEY JUST MOVE FASTER THAN EVERYONE ELSE. credits to Archive

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CHATGPT STILL CAN’T BUILD SITES THAT LOOK LIKE THIS Only Claude is building websites that actually feel expensive right now. Scroll through the clip. Particle systems. Liquid transitions. Cinematic depth. Every frame feels like a high-budget trailer. Claude produces this level of motion and atmosphere. ChatGPT still outputs flat layouts and basic interactions that look dated the moment they load. The gap is no longer small. One model delivers premium digital experiences. The other still struggles to clear “clean and modern.” Agencies and freelancers who switched already charge more and deliver faster. The ones still prompting the wrong model keep losing the high-ticket clients.

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Day 17 of why you need to use Jupiter Mobile. FAST AS F*** Hate getting nuked on entries and exits? Slower execution means more slippage and worse prices. Every swap on Jupiter Mobile runs on Ultra V3, with sub‑second precision, 0–1 block execution, and 3x more trades landed, all while paying ~10x lower fees than other wallets. The team has been obsessing over latency, routing, and execution, so your trades hit faster and cleaner, every time. Just Use Jupiter (Mobile).

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500 agents checking each other isn't 500 checks. It's 124,750. That's the swarm running above. Parallel, open-source fed, cross-verifying. Nobody prices the cross. Every pair is one comparison. The count grows on the square. You added one agent and bought 499. And all of them read the same repo. One README, 500 times. You pay per reader, not per document. Grok 4.6 bills $2 per million in, $6 out. Cheap per token. Not cheap per 124,750. Grok Build spawns eight sub-agents per prompt. 500 means an orchestrator you wrote yourself. Nobody audits their own orchestrator. xAI lost $2.47 billion last quarter. Revenue was $818 million. Anthropic pays them $1.25 billion a month for compute. Your swarm runs on rented losses. Consensus never needed everybody. One shared read. Three checkers. One arbiter. Redundancy is not verification. Don't let this rot in your bookmarks. Save it, then count the pairs in your own swarm before you scale it - because 500 agents reading one file don't verify anything, they agree 500 times.

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Tesla and SpaceX are about to need chips faster than the world can produce. The answer? Terafab. A chip factory in Texas so huge it will dwarf landmarks like the Pentagon and Apple Park, with a goal of producing more than 1 terawatt of compute every year. The point is to stop future Tesla and SpaceX growth from being limited by whoever can supply them. Elon says Terafab will eventually be worth more than any other building on Earth. Tesla SpaceX / Writer: Annette, Grok Imagine Designer: Janné

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MiniMax is the James Bond of AI agents. It uses the world's first open-weight model (MiniMax-M1), and it squeezes every bit of power from it. The agent takes a prompt and does more than any other agent in the market right now: 1. It can do Deep Research 2. It can write code 3. It can design web pages 4. It can build 3D models I built 5 different experiences using MiniMax and recorded them for you:

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JUST IN: Meta AI introduces Voicebox, an all-in-one generative speech model. Voicebox is an impressive breakthrough! It could do for speech what other models like GPT-3 and Stable Diffusion have done for text and images. Some key details: - Voicebox can synthesize speech across 6 languages - It's a general-purpose model that can perform tasks it wasn't trained on. It can perform noise removal, content editing, style conversion, and more - Supports in-context text-to-speech synthesis and cross-lingual style transfer - It's 20x faster than current models and outperforms single-purpose models through in-context learning paper: blog:

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