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Introducing 1millioncontext! Visualize what 1 million tokens actually looks like. It also walks through the journey from GPT-3 with 2k tokens to now, where many models are 1M+ context.

Hassan

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18,201 просмотров • 21 дней назад •via X (Twitter)

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Kai-Fu Lee says GPU supply constraints are forcing Chinese AI companies to innovate, meaning they can train a frontier model for $3 million contrasted with GPT-5's $1 billion, and deliver inference costs of 10c/million tokens, 1/30th of what an American company charges

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Google Jeff Dean says bigger context windows alone are not enough What matters is staged retrieval: lightweight mechanisms that narrow a trillion tokens down to 10 million, then to the million you actually need "you don't need a trillion at once, you need the right million"

Google Jeff Dean says bigger context windows alone are not enough What matters is staged retrieval: lightweight mechanisms that narrow a trillion tokens down to 10 million, then to the million you actually need "you don't need a trillion at once, you need the right million"

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Introducing Cursor Learn! My first course is a six-part video series on AI foundations. It's designed for beginners to learn concepts like tokens, context, and agents. If you prefer to read, there's quizzes + AI models to try out. You can watch it in ~1 hour, 100% free!

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The value produced by models is getting so much better so fast that old hardware is actually getting *more* expensive to rent. 3 years ago, the best model you could run on a H100 chip was GPT-4. Now, you can run GPT-5.4 on it, which is smaller and cheaper to run while producing much more valuable tokens. w. Dylan Patel

The value produced by models is getting so much better so fast that old hardware is actually getting more expensive to rent. 3 years ago, the best model you could run on a H100 chip was GPT-4. Now, you can run GPT-5.4 on it, which is smaller and cheaper to run while producing much more valuable tokens. w. Dylan Patel

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78,277 просмотров • 5 месяцев назад

Prime Intellect engineer: "everyone's bragging about a million-token context. here's what they don't tell you. at 256k tokens GPT-5.5 scores 80% on retrieval. push it to a million and it drops to 36%. the model accepts the context, it just can't reason across it. people call it context rot." in a 20-minute talk he explains why bigger context windows won't save your agents. continual learning + training on your own traces + real environments - that's the fix. Watch the talk, then save!

Prime Intellect engineer: "everyone's bragging about a million-token context. here's what they don't tell you. at 256k tokens GPT-5.5 scores 80% on retrieval. push it to a million and it drops to 36%. the model accepts the context, it just can't reason across it. people call it context rot." in a 20-minute talk he explains why bigger context windows won't save your agents. continual learning + training on your own traces + real environments - that's the fix. Watch the talk, then save!

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Introducing private bridging. You can now bridge privately from Solana → Ethereum, BNB, Base, and Bitcoin. In most cases, you’ll receive ~1–2% more tokens compared to Houdini. Watch the unedited video below to see how fast the bridge actually is. ⚡️

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vijay verma

182,325 просмотров • 1 год назад

I plugged MiniMax (official) M3 into Claude Code in under 5 minutes and gave it a real task. most models need hand-holding every few steps. M3 ran 147 iterations on its own. what it actually did: → took a broken CUDA kernel → ran it for 24 hours, autonomously → pushed hardware efficiency from 7.6% to 71.3% → shipped a 9.4x speedup I didn't touch it once across that whole window. the rest of the spec sheet: → 1M token context window → native multimodal from day one → fully open weights pricing: → $20/month for 1.7B tokens → $50/month for 5.1B tokens → text, image, speech, and music all share one pool usually with this kind of capability the bill catches up fast. M3 keeps both lines low at the same time. that's the part that surprised me. and if this is what M3 does at launch, the full rollout is going to be one to watch. what would you build with 1M context? #MiniMax #MiniMaxM3 #ClaudeCode #LLM

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Rockstar spent 7 years, 2,000+ developers, and $500 million to build Red Dead Redemption 2. Kimi K3 rebuilt the same scene in 10 hours. One prompt. 4 million tokens. It's open source. 2.8 trillion parameters. A 1 million token context window. It beats Claude Opus 4.8 and GPT-5.6. It sits on par with Fable 5. Fable 5 results, at Sonnet pricing. The wall between you and a AAA game used to be a studio. Now it's a prompt.

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