正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

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

99,283 subscribers

18,201 次观看 • 21 天前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

$INFQ GPU + CPU + QPU Frontier models are hitting saturation on tokens (data volume) and context windows (memory). $INFQ CTO walks through how they and $NVDA are working together to add a Quantum Processing Unit to the chain as the next inflection point.

$INFQ GPU + CPU + QPU Frontier models are hitting saturation on tokens (data volume) and context windows (memory). $INFQ CTO walks through how they and $NVDA are working together to add a Quantum Processing Unit to the chain as the next inflection point.

YeahDave

57,181 次观看 • 3 个月前

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

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

Tsarathustra

218,794 次观看 • 1 年前

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"

Haider.

141,291 次观看 • 5 个月前

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!

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!

Lee Robinson

1,449,578 次观看 • 11 个月前

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

Dwarkesh Patel

78,277 次观看 • 5 个月前

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!

Carnage

748,504 次观看 • 1 个月前

BITCOIN SMASHING ATH!!! FLYING TO INSANE LEVELS and we are launching the BULLMANIA TERMINAL 1. See which tokens are bullish in a bullish trend 2. See tokens that flipped bullish today 3. BUY WITH SIZE 4. PROFIT Features: 1. USD, BTC, SPX pairs 2. Many timeframes 3. Discord group included 4. Weekly TA included from me personally BUY AT A DISCOUNT:

BITCOIN SMASHING ATH!!! FLYING TO INSANE LEVELS and we are launching the BULLMANIA TERMINAL 1. See which tokens are bullish in a bullish trend 2. See tokens that flipped bullish today 3. BUY WITH SIZE 4. PROFIT Features: 1. USD, BTC, SPX pairs 2. Many timeframes 3. Discord group included 4. Weekly TA included from me personally BUY AT A DISCOUNT:

Ivan on Tech 🍳📈💰 Head Trader @ Bullmania

120,575 次观看 • 1 年前

GPT-5.6 Sol Ultra lost to GPT-5.5 on physics at 3× the cost! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: - A monster truck backflipping onto a parked car - A stunt car jumping six buses into a brick wall - A train derailing off a broken bridge into the water Outputs: GPT-5.6 Sol Ultra: 32.9K tokens, $0.33 Opus 4.8: 9.2K tokens, $0.24 GPT-5.5: 12.4K tokens, $0.11 Grok 4.5: 7.0K tokens, $0.08 Sol Ultra draws just like GPT-5.5, only with more detail and it shows clearest on the bus jump where the two look almost identical. In the other tests Sol Ultra came out worse than its predecessor and the physics really got weaker. We think GPT-5.5 took the truck flip and the train outright. With Sol Ultra you basically get GPT-5.5 with weaker physics and a nicer picture for 3x the price. Newborn Grok 4.5 failed two of the three tests and only came good on the train

GPT-5.6 Sol Ultra lost to GPT-5.5 on physics at 3× the cost! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: - A monster truck backflipping onto a parked car - A stunt car jumping six buses into a brick wall - A train derailing off a broken bridge into the water Outputs: GPT-5.6 Sol Ultra: 32.9K tokens, $0.33 Opus 4.8: 9.2K tokens, $0.24 GPT-5.5: 12.4K tokens, $0.11 Grok 4.5: 7.0K tokens, $0.08 Sol Ultra draws just like GPT-5.5, only with more detail and it shows clearest on the bus jump where the two look almost identical. In the other tests Sol Ultra came out worse than its predecessor and the physics really got weaker. We think GPT-5.5 took the truck flip and the train outright. With Sol Ultra you basically get GPT-5.5 with weaker physics and a nicer picture for 3x the price. Newborn Grok 4.5 failed two of the three tests and only came good on the train

atomic.chat

897,956 次观看 • 1 个月前

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. ⚡️

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. ⚡️

Privacy Cash

57,094 次观看 • 6 个月前

Found a way to run Kimi K3 for FREE with daily credits on 😳 it's a 2.8T parameter model. 1M context. beats Fable 5 on coding benchmarks. what you get for $0: -2.8 trillion parameters -1M context window -beats Fable 5 on SWE Marathon (42 vs 35) -beats Fable 5 on Program Bench (77.8) -topped BrowseComp (91.2) -native vision understanding What this replaces: -Paid frontier API costs for coding agents -Fable 5 at $30/M output tokens -GPT-5.6 Sol at $25/M output tokens how to use it (2 min): 1. Go to 2. Sign up with email 3. Start using K3 — it's the default model Also available on desktop app, CLI, and mobile. Important: Free daily credits reset. Full open weights dropping July 27. Rate limits apply on free tier. use this before the free tier gets crowded

Found a way to run Kimi K3 for FREE with daily credits on 😳 it's a 2.8T parameter model. 1M context. beats Fable 5 on coding benchmarks. what you get for $0: -2.8 trillion parameters -1M context window -beats Fable 5 on SWE Marathon (42 vs 35) -beats Fable 5 on Program Bench (77.8) -topped BrowseComp (91.2) -native vision understanding What this replaces: -Paid frontier API costs for coding agents -Fable 5 at $30/M output tokens -GPT-5.6 Sol at $25/M output tokens how to use it (2 min): 1. Go to 2. Sign up with email 3. Start using K3 — it's the default model Also available on desktop app, CLI, and mobile. Important: Free daily credits reset. Full open weights dropping July 27. Rate limits apply on free tier. use this before the free tier gets crowded

painn

253,746 次观看 • 1 个月前

Created this lil color generator over the weekend. There are so many algorithms to choose from for generating beautiful color palettes. Also, You can get the output as CSS, JSON, or Tailwind tokens. Check it out with sound on :)

Created this lil color generator over the weekend. There are so many algorithms to choose from for generating beautiful color palettes. Also, You can get the output as CSS, JSON, or Tailwind tokens. Check it out with sound on :)

vijay verma

182,325 次观看 • 1 年前

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

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

Nelly;

17,576 次观看 • 2 个月前

MiniCPM-V 4.5 achieves an average score of 77.0 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. With only 8B parameters, it surpasses widely used proprietary models like GPT-4o-latest, Gemini-2.0 Pro, and strong open-source models like Qwen2.5-VL 72B powered by a new unified 3D-Resampler over images and videos, MiniCPM-V 4.5 can now achieve 96x compression rate for video tokens, where 6 448x448 video frames can be jointly compressed into 64 video tokens (normally 1,536 tokens for most MLLMs). vibe coding a Video Chat AI app with MiniCPM-V-4.5 in anycoder

MiniCPM-V 4.5 achieves an average score of 77.0 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. With only 8B parameters, it surpasses widely used proprietary models like GPT-4o-latest, Gemini-2.0 Pro, and strong open-source models like Qwen2.5-VL 72B powered by a new unified 3D-Resampler over images and videos, MiniCPM-V 4.5 can now achieve 96x compression rate for video tokens, where 6 448x448 video frames can be jointly compressed into 64 video tokens (normally 1,536 tokens for most MLLMs). vibe coding a Video Chat AI app with MiniCPM-V-4.5 in anycoder

AK

19,088 次观看 • 11 个月前

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.

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.

Dep

99,354 次观看 • 1 个月前

🏁 150 Million Tokens Burned – Are We Finished Yet? 🏁 Ken and Russel are back with a bold twist, joined by a dancer waving the finish flag. But don’t be fooled — this is far from the end! 💃🏁 💥 Another 50 million tokens have been burned, bringing the total to an astonishing 150 million tokens — worth $60 million! The Asvoria token burn is rewriting the rules of value. 👉 Transaction link: ⚡ "Are we finished yet?" You’ll have to keep watching to find out. Trust us, it’s only getting better from here. 🚀 Follow us to see what happens next! #AsvoriaBurn #TokenBurn #CryptoRevolution #BlockchainMagic #Tokenomics #FinishLineBurn #TokenValue

🏁 150 Million Tokens Burned – Are We Finished Yet? 🏁 Ken and Russel are back with a bold twist, joined by a dancer waving the finish flag. But don’t be fooled — this is far from the end! 💃🏁 💥 Another 50 million tokens have been burned, bringing the total to an astonishing 150 million tokens — worth $60 million! The Asvoria token burn is rewriting the rules of value. 👉 Transaction link: ⚡ "Are we finished yet?" You’ll have to keep watching to find out. Trust us, it’s only getting better from here. 🚀 Follow us to see what happens next! #AsvoriaBurn #TokenBurn #CryptoRevolution #BlockchainMagic #Tokenomics #FinishLineBurn #TokenValue

Asvoria $ASV/SOL

12,342 次观看 • 1 年前

Introducing "Linea Insights" 📺 A series where Linea's cryptography team shares what we are building and the thinking behind it. Following our announcement at Ethproofs, Olivier Bégassat walks us through the shift from direct EVM arithmetization to RISC-V.

Introducing "Linea Insights" 📺 A series where Linea's cryptography team shares what we are building and the thinking behind it. Following our announcement at Ethproofs, Olivier Bégassat walks us through the shift from direct EVM arithmetization to RISC-V.

Linea.eth

85,361 次观看 • 4 个月前

When you hold a token, you're exposed to every market that touches it. Until now, there was no way to quickly understand what that actually looks like. Introducing token pages on DefiLlama. Understand how tokens are being used in DeFi: - Risks across lending markets - Usage by protocol - Liquidation levels - Yields & borrow rates Enter a token in our search bar to find its page.

When you hold a token, you're exposed to every market that touches it. Until now, there was no way to quickly understand what that actually looks like. Introducing token pages on DefiLlama. Understand how tokens are being used in DeFi: - Risks across lending markets - Usage by protocol - Liquidation levels - Yields & borrow rates Enter a token in our search bar to find its page.

DefiLlama.com

42,066 次观看 • 4 个月前

Liquid AI (Liquid AI) CEO Ramin Hasani (Ramin) says: "We're bringing the cost of tokens to zero." "The axis was maximizing intelligence at all costs." Foundation models need to optimize across 3 axes: 1.) Intelligence and capability 2.) Efficiency and cost 3.) Substrate: where the intelligence actually runs "Efficiency is not an afterthought. Energy is not abundant." "If you maximize intelligence at all costs, that axis alone is not going to get you to the place that you want to go." "Efficiency and cost of intelligence as a first-class citizen, and not an afterthought." "Where does this intelligence system go? AI is majorly getting hosted in data centers, but you could also bring intelligence on phones, on laptops, on airplanes, on cars." "Imagine if it's not tokens that are actually important. It's just the outcome that actually matters." "Not just thinking about foundation models as the token machines that are generating money and revenue for the foundation model companies that are useless tokens, and getting them into the place where they can actually unlock true value for enterprises."

Liquid AI (Liquid AI) CEO Ramin Hasani (Ramin) says: "We're bringing the cost of tokens to zero." "The axis was maximizing intelligence at all costs." Foundation models need to optimize across 3 axes: 1.) Intelligence and capability 2.) Efficiency and cost 3.) Substrate: where the intelligence actually runs "Efficiency is not an afterthought. Energy is not abundant." "If you maximize intelligence at all costs, that axis alone is not going to get you to the place that you want to go." "Efficiency and cost of intelligence as a first-class citizen, and not an afterthought." "Where does this intelligence system go? AI is majorly getting hosted in data centers, but you could also bring intelligence on phones, on laptops, on airplanes, on cars." "Imagine if it's not tokens that are actually important. It's just the outcome that actually matters." "Not just thinking about foundation models as the token machines that are generating money and revenue for the foundation model companies that are useless tokens, and getting them into the place where they can actually unlock true value for enterprises."

Molly O’Shea

12,816 次观看 • 1 个月前

5 patterns in Text Arena's price–performance Pareto frontier since 2023: 1. GPT-4-level quality is now ~500x lower cost. - From a ~$50 blended price per million tokens in 2023 to ~$0.10 today. 2. The higher-price end is both better and lower-priced since 2023. - The leading Arena score has climbed ~170 points (1,330 → 1,500). While the price of the higher-end frontier models dropped from ~$50 to ~$20 per million tokens. 3. The low-cost end gained the most. - Under $0.20 per million tokens, the best available model went from ~1,000 Arena score in 2023 to ~1,440 today. 4. The low-cost/top performance gap has nearly closed. - In 2023, sub-$0.20 models trailed the leader by ~350 Arena points. Today, ~60. 5. The cast has rotated quite a bit. - - OpenAI set the 2023–24 benchmark. - AI at Meta strengthened the low-cost end in 2024. - Google DeepMind drove the 2025 jump. - Anthropic holds the peak in 2026. - xAI and Chinese labs like DeepSeek AI, Z.ai, Kimi.ai, Xiaomi MiMo, and Qwen are continuing to push the mid-price frontier.

5 patterns in Text Arena's price–performance Pareto frontier since 2023: 1. GPT-4-level quality is now ~500x lower cost. - From a ~$50 blended price per million tokens in 2023 to ~$0.10 today. 2. The higher-price end is both better and lower-priced since 2023. - The leading Arena score has climbed ~170 points (1,330 → 1,500). While the price of the higher-end frontier models dropped from ~$50 to ~$20 per million tokens. 3. The low-cost end gained the most. - Under $0.20 per million tokens, the best available model went from ~1,000 Arena score in 2023 to ~1,440 today. 4. The low-cost/top performance gap has nearly closed. - In 2023, sub-$0.20 models trailed the leader by ~350 Arena points. Today, ~60. 5. The cast has rotated quite a bit. - - OpenAI set the 2023–24 benchmark. - AI at Meta strengthened the low-cost end in 2024. - Google DeepMind drove the 2025 jump. - Anthropic holds the peak in 2026. - xAI and Chinese labs like DeepSeek AI, Z.ai, Kimi.ai, Xiaomi MiMo, and Qwen are continuing to push the mid-price frontier.

Arena.ai

58,133 次观看 • 3 个月前

🆕 We’re excited to share that Stake Baskets are now available in Crypto Basket! Earn up to 11% by staking multiple tokens at once ✔️ 3 staking baskets available at launch, with more to come ✔️ Easily diversify your earnings with top tokens Try it now: ℹ️ Visit the FAQ for more information 👉

🆕 We’re excited to share that Stake Baskets are now available in Crypto Basket! Earn up to 11% by staking multiple tokens at once ✔️ 3 staking baskets available at launch, with more to come ✔️ Easily diversify your earnings with top tokens Try it now: ℹ️ Visit the FAQ for more information 👉

Crypto.com

25,416 次观看 • 10 个月前