Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

Next up on First Pass: A conversation with Simon Mo from Inferact on Kimi K3. We discuss: - The basics on the K3 model (it's huge!) - Why open weight models are important - The engineering complexity of working with a model this large - Innovative licensing models And more!

Jamin Ball

92,391 subscribers

13,572 görüntüleme • 15 gün önce •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

vLLM runs on half a million GPUs at any given moment. Most people have never heard of it. Simon Mo, co-founder and CEO of Inferact and lead maintainer of vLLM, sits down with a16z’s Matt Bornstein and Elena Burger to discuss what it takes to actually run open models in production, the advantages of open models, Simon’s mission at Inferact, and more. 00:00 Intro 01:46 When open source became critical infrastructure 08:55 Day zero model releases, and the drama behind them 14:59 What Kimi K3 actually buys you 18:56 Why open model licenses are changing 22:24 The pharmaceutical analogy for funding model training 26:16 If GPUs got 99% cheaper 29:08 Why vLLM, OpenRouter, and Ollama all started before ChatGPT 35:42 Building a company on an open source project 39:48 The inventor of RoPE removing RoPE Simon Mo Matt Bornstein Elena

vLLM runs on half a million GPUs at any given moment. Most people have never heard of it. Simon Mo, co-founder and CEO of Inferact and lead maintainer of vLLM, sits down with a16z’s Matt Bornstein and Elena Burger to discuss what it takes to actually run open models in production, the advantages of open models, Simon’s mission at Inferact, and more. 00:00 Intro 01:46 When open source became critical infrastructure 08:55 Day zero model releases, and the drama behind them 14:59 What Kimi K3 actually buys you 18:56 Why open model licenses are changing 22:24 The pharmaceutical analogy for funding model training 26:16 If GPUs got 99% cheaper 29:08 Why vLLM, OpenRouter, and Ollama all started before ChatGPT 35:42 Building a company on an open source project 39:48 The inventor of RoPE removing RoPE Simon Mo Matt Bornstein Elena

a16z

206,021 görüntüleme • 7 gün önce

Inferact Co-founder Simon Mo on the trajectory of open and closed models: "What really differentiates open-weight models from closed-weight models, right? In the end, there's not much differentiation. It's more about the distribution strategy and go-to-market strategy... Capability-wise, I don't really see a big gap, not even today." "It's not about just source data. It's not about where to get the data from. Rather, it's who can build the best environment and who can make the most optimization and algorithmic choices to leverage all this learning from this environment." "So the next year is all gonna be about that. It's about how open-weight model labs are differentiating and really getting the model to meet the real world." "Project out in a year, there's not gonna be any difference." Simon Mo

Inferact Co-founder Simon Mo on the trajectory of open and closed models: "What really differentiates open-weight models from closed-weight models, right? In the end, there's not much differentiation. It's more about the distribution strategy and go-to-market strategy... Capability-wise, I don't really see a big gap, not even today." "It's not about just source data. It's not about where to get the data from. Rather, it's who can build the best environment and who can make the most optimization and algorithmic choices to leverage all this learning from this environment." "So the next year is all gonna be about that. It's about how open-weight model labs are differentiating and really getting the model to meet the real world." "Project out in a year, there's not gonna be any difference." Simon Mo

a16z

33,782 görüntüleme • 7 gün önce

With Kimi K3 Day-0 on vLLM: Open Frontier Intelligence for Everyone 🚀 At 2.8 trillion parameters, Moonshot AI's Kimi K3 is one of the most powerful open-weight models ever released. Starting today, you can serve it on vLLM the moment the weights are public. What K3 brings: 🧠 2.8T-parameter Mixture-of-Experts (16 of 896 experts active per token) 📚 1M-token context window 🛠️ Native multimodal understanding, including vision ⚡ Kimi Delta Attention: a hybrid of linear and full attention that makes million-token context affordable Huge thank you to Kimi.ai AI for the model release and partnership, Inferact for leading the vLLM optimizations, and to our partners at NVIDIA, AMD, and the broader vLLM community. 1/6

With Kimi K3 Day-0 on vLLM: Open Frontier Intelligence for Everyone 🚀 At 2.8 trillion parameters, Moonshot AI's Kimi K3 is one of the most powerful open-weight models ever released. Starting today, you can serve it on vLLM the moment the weights are public. What K3 brings: 🧠 2.8T-parameter Mixture-of-Experts (16 of 896 experts active per token) 📚 1M-token context window 🛠️ Native multimodal understanding, including vision ⚡ Kimi Delta Attention: a hybrid of linear and full attention that makes million-token context affordable Huge thank you to Kimi.ai AI for the model release and partnership, Inferact for leading the vLLM optimizations, and to our partners at NVIDIA, AMD, and the broader vLLM community. 1/6

vLLM

102,101 görüntüleme • 17 gün önce

New podcast with Florian Brand on all things open models. More on Kimi K3, Qwen 3.8, GLM-5.2, Xi's WAIC speech, distillation, the open-closed Gap, and what's next. Chapters: 00:00 Welcome & context 04:38 Living with / using Kimi K3 08:53 GLM 5.2’s continued role 12:47 How are the Chinese models this good? 17:41 Data, environments, and a tour of the Chinese labs 19:47 Roundup of Chinese providers: Qwen, DeepSeek, MiniMax… 24:08 The US open-model ecosystem 30:25 Frontier vs. near-frontier, and the cybersecurity case against bans 34:58 Distillation and the Ben Thompson debate 44:12 Predictions and a frontier tier list 48:36 Wrap-up Hoping to keep doing a few more of these on Interconnects AI. Crucial times in AI, we're working hard to share our expertise.

Nathan Lambert

19,379 görüntüleme • 22 gün önce

📣 Kimi.ai's Kimi K3, an open-weight model, is now generally available and rolling out in GitHub Copilot. The model shows frontier-level abilities on agentic coding with highly cost-effective pricing. It is hosted by Fireworks. Learn more. 👇

📣 Kimi.ai's Kimi K3, an open-weight model, is now generally available and rolling out in GitHub Copilot. The model shows frontier-level abilities on agentic coding with highly cost-effective pricing. It is hosted by Fireworks. Learn more. 👇

GitHub

229,681 görüntüleme • 7 gün önce

2 years ago, Anthropic CEO Dario Amodei warned the U.S. Congress: “The scaling of open-source models is going down a very dangerous path…” Today, Kimi K3 is already competing with frontier models. Seeing what this model can do, I think we’re getting closer to open-source models reaching Mythos-level capabilities with little to no guardrails. That’s a pretty scary thought.

2 years ago, Anthropic CEO Dario Amodei warned the U.S. Congress: “The scaling of open-source models is going down a very dangerous path…” Today, Kimi K3 is already competing with frontier models. Seeing what this model can do, I think we’re getting closer to open-source models reaching Mythos-level capabilities with little to no guardrails. That’s a pretty scary thought.

m0h

147,748 görüntüleme • 26 gün önce

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

30,392 görüntüleme • 10 gün önce

Benchmark’s Peter Fenton says 90%+ of tokens could come from open weight models in the next 18-24 months, pressuring frontier model margins. Full convo at 12p PT/3p ET on the livestream, plus Aravind Srinivas on Perplexity’s new orchestrator model and Jeffrey Morgan on why enterprises are moving toward AI models they can download and control

Benchmark’s Peter Fenton says 90%+ of tokens could come from open weight models in the next 18-24 months, pressuring frontier model margins. Full convo at 12p PT/3p ET on the livestream, plus Aravind Srinivas on Perplexity’s new orchestrator model and Jeffrey Morgan on why enterprises are moving toward AI models they can download and control

Deirdre Bosa

63,906 görüntüleme • 1 ay önce

Kimi K3 just generated a Red Dead Redemption 2-style open-world game... 🤯 A few months ago this would've sounded impossible. Now open-weight models are building explorable worlds with terrain, gameplay systems, and impressive visual fidelity from a single prompt.

Kimi K3 just generated a Red Dead Redemption 2-style open-world game... 🤯 A few months ago this would've sounded impossible. Now open-weight models are building explorable worlds with terrain, gameplay systems, and impressive visual fidelity from a single prompt.

WorldofAI

94,126 görüntüleme • 28 gün önce

Kimi K3 is insane🤯 It built an Animal Crossing-style game, and it generated a fully playable experience with the cozy aesthetic, interactions, and gameplay loop in a single shot. Open-weight models are starting to rival the best closed models for game generation. The pace of progress is getting ridiculous. 🌱🎮

Kimi K3 is insane🤯 It built an Animal Crossing-style game, and it generated a fully playable experience with the cozy aesthetic, interactions, and gameplay loop in a single shot. Open-weight models are starting to rival the best closed models for game generation. The pace of progress is getting ridiculous. 🌱🎮

WorldofAI

449,392 görüntüleme • 28 gün önce

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Santiago

164,162 görüntüleme • 2 yıl önce

Kimi K3 is the first open source model to earn the BridgeMind stamp of approval. One shot Horror House game. One shot full-stack app. Beating Fable 5 in early BridgeBench arena matches. #3 on the Intelligence Index. Moonshot AI just caught the frontier. Full Kimi K3 review below.

Kimi K3 is the first open source model to earn the BridgeMind stamp of approval. One shot Horror House game. One shot full-stack app. Beating Fable 5 in early BridgeBench arena matches. #3 on the Intelligence Index. Moonshot AI just caught the frontier. Full Kimi K3 review below.

BridgeMind

31,676 görüntüleme • 28 gün önce

Thanks to CNN for having me on to discuss Kimi K3. I told Paula Newton K3’s large size, limited uptime, and the erosion of the U.S. software moat are all redefining the U.S.-China AI race: We are in an industrial systems competition to build and install vast compute.

Thanks to CNN for having me on to discuss Kimi K3. I told Paula Newton K3’s large size, limited uptime, and the erosion of the U.S. software moat are all redefining the U.S.-China AI race: We are in an industrial systems competition to build and install vast compute.

Ryan Fedasiuk

14,606 görüntüleme • 27 gün önce

SGLang day-0 speed on Kimi K3: 423 tok/s (measured on gsm8k), plus RL support ready in Miles RadixArk! How the largest open-source model runs this fast: we natively implemented and deeply optimized K3’s new architecture with fused KDA decode kernels, DP attention, DSpark, PD disagg, and KDA-aware prefix caching. We've passed Kimi Vendor Verifier and are ready for production! Thanks to Kimi.ai, NVIDIA, AMD, KVCache.AI, Modal, and Baseten for building this with us, and to Google Cloud, Nebius Token Factory, fal, DigitalOcean, Runpod, DeepInfra and GMI Cloud for serving K3 on SGLang. Blog, cookbook, benchmarks in the comments. P.S. This demo video? Kimi K3 made it itself. Play the game 👇

SGLang day-0 speed on Kimi K3: 423 tok/s (measured on gsm8k), plus RL support ready in Miles RadixArk! How the largest open-source model runs this fast: we natively implemented and deeply optimized K3’s new architecture with fused KDA decode kernels, DP attention, DSpark, PD disagg, and KDA-aware prefix caching. We've passed Kimi Vendor Verifier and are ready for production! Thanks to Kimi.ai, NVIDIA, AMD, KVCache.AI, Modal, and Baseten for building this with us, and to Google Cloud, Nebius Token Factory, fal, DigitalOcean, Runpod, DeepInfra and GMI Cloud for serving K3 on SGLang. Blog, cookbook, benchmarks in the comments. P.S. This demo video? Kimi K3 made it itself. Play the game 👇

LMSYS Org

201,687 görüntüleme • 17 gün önce

𝐃𝐞𝐬𝐭𝐫𝐚 𝐎𝐂𝐀𝐈 (𝐎𝐧𝐞-𝐂𝐥𝐢𝐜𝐤 𝐀𝐈) 𝐌𝐨𝐝𝐞𝐥𝐬 - $DSYNC 𝐄𝐚𝐫𝐥𝐲 𝐀𝐜𝐜𝐞𝐬𝐬 𝐏𝐫𝐞𝐯𝐢𝐞𝐰 We are training and building a wide range of AI models for the Destra OCAI platform. Today, we will be showcasing an early preview of one of the initial models we are working on—a bullish/bearish prediction model designed to 𝐟𝐨𝐫𝐞𝐜𝐚𝐬𝐭 𝐭𝐡𝐞 𝐦𝐚𝐫𝐤𝐞𝐭 𝐝𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧 𝐨𝐟 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐭𝐨𝐤𝐞𝐧𝐬. This is just one of the initial models we are working on; as we progress, the models will become more complex and intricate. In this behind-the-scenes video, we demonstrate how the model predicts the future market trend of the BTC-USD pair with an 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐨𝐟 𝟔𝟓%. This is the barebones structure of the models we are training, which will soon be available to users with a single click through the Destra OCAI platform. The OCAI platform includes a wide array of Destra Exclusive AI models, specifically designed to simplify life for crypto audiences, that are envisioned and built from scratch at Destra Labs.

𝐃𝐞𝐬𝐭𝐫𝐚 𝐎𝐂𝐀𝐈 (𝐎𝐧𝐞-𝐂𝐥𝐢𝐜𝐤 𝐀𝐈) 𝐌𝐨𝐝𝐞𝐥𝐬 - $DSYNC 𝐄𝐚𝐫𝐥𝐲 𝐀𝐜𝐜𝐞𝐬𝐬 𝐏𝐫𝐞𝐯𝐢𝐞𝐰 We are training and building a wide range of AI models for the Destra OCAI platform. Today, we will be showcasing an early preview of one of the initial models we are working on—a bullish/bearish prediction model designed to 𝐟𝐨𝐫𝐞𝐜𝐚𝐬𝐭 𝐭𝐡𝐞 𝐦𝐚𝐫𝐤𝐞𝐭 𝐝𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧 𝐨𝐟 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐭𝐨𝐤𝐞𝐧𝐬. This is just one of the initial models we are working on; as we progress, the models will become more complex and intricate. In this behind-the-scenes video, we demonstrate how the model predicts the future market trend of the BTC-USD pair with an 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐨𝐟 𝟔𝟓%. This is the barebones structure of the models we are training, which will soon be available to users with a single click through the Destra OCAI platform. The OCAI platform includes a wide array of Destra Exclusive AI models, specifically designed to simplify life for crypto audiences, that are envisioned and built from scratch at Destra Labs.

Destra Network

37,149 görüntüleme • 2 yıl önce

Chinese AI models are wiping billions off Big Tech right now. Google just lost $200 billion in a single day, and the model it needed to fight back still isn't ready. Gemini 3.5 Pro, Google's most powerful model, is months behind schedule. Alphabet stock dropped 4.4% that same day. The Deepseek moment is happening again, and the new model is FAR bigger. On the same day Google's delay leaked, a Beijing lab called Moonshot released Kimi K3. It is the largest open model ever built, with 2.8 trillion parameters. It took the number one spot on the Frontend Code Arena, a live coding leaderboard, passing Anthropic's best model. And Moonshot is giving it away for free on July 27. The genius part: Anyone with enough computers can download it and run a frontier level AI without paying a cent to a US company. A single task on Kimi K3 costs about 94 cents. The same work on some American models costs nearly double. So why would a company keep paying premium prices for a model it can now get for free? The entire US AI business is built on selling access to models that cost billions to train. If a free Chinese version does most of the same work, that pricing power starts to crack. And Kimi is close to the best. On one closely watched intelligence ranking it scored 57, just behind the top American models GPT-5.6 Sol and Fable 5, and ahead of Claude Opus 4.8. Bank of America told clients that Kimi proves Chinese labs can keep making big leaps even with limited chips. And the founder of Moonshot, Yang Zhilin, learned to build AI as a researcher INSIDE Google. Google literally wrote the 2017 paper that made all of these models possible. Now the people who studied its work are using it to destroy Google, and handing it out for free. What happens next: Kimi K3's weights go public on July 27. Google reports earnings on July 22, and everyone will be asking the same question about Gemini. If free models keep topping the charts, every valuation built on paid AI access has to be rewritten. What do you think?

Chinese AI models are wiping billions off Big Tech right now. Google just lost $200 billion in a single day, and the model it needed to fight back still isn't ready. Gemini 3.5 Pro, Google's most powerful model, is months behind schedule. Alphabet stock dropped 4.4% that same day. The Deepseek moment is happening again, and the new model is FAR bigger. On the same day Google's delay leaked, a Beijing lab called Moonshot released Kimi K3. It is the largest open model ever built, with 2.8 trillion parameters. It took the number one spot on the Frontend Code Arena, a live coding leaderboard, passing Anthropic's best model. And Moonshot is giving it away for free on July 27. The genius part: Anyone with enough computers can download it and run a frontier level AI without paying a cent to a US company. A single task on Kimi K3 costs about 94 cents. The same work on some American models costs nearly double. So why would a company keep paying premium prices for a model it can now get for free? The entire US AI business is built on selling access to models that cost billions to train. If a free Chinese version does most of the same work, that pricing power starts to crack. And Kimi is close to the best. On one closely watched intelligence ranking it scored 57, just behind the top American models GPT-5.6 Sol and Fable 5, and ahead of Claude Opus 4.8. Bank of America told clients that Kimi proves Chinese labs can keep making big leaps even with limited chips. And the founder of Moonshot, Yang Zhilin, learned to build AI as a researcher INSIDE Google. Google literally wrote the 2017 paper that made all of these models possible. Now the people who studied its work are using it to destroy Google, and handing it out for free. What happens next: Kimi K3's weights go public on July 27. Google reports earnings on July 22, and everyone will be asking the same question about Gemini. If free models keep topping the charts, every valuation built on paid AI access has to be rewritten. What do you think?

Ricardo

47,790 görüntüleme • 24 gün önce

We compared 1-bit Kimi K3 to Claude Opus 5 and GPT 5.6. We gave 4 models the same prompt: Create a glass aquarium whose side panel develops a visible crack and then bursts. 1-bit Kimi K3 GGUF ran locally on 4x B200s at 36 tok/s. GitHub repo:

We compared 1-bit Kimi K3 to Claude Opus 5 and GPT 5.6. We gave 4 models the same prompt: Create a glass aquarium whose side panel develops a visible crack and then bursts. 1-bit Kimi K3 GGUF ran locally on 4x B200s at 36 tok/s. GitHub repo:

Unsloth AI

413,091 görüntüleme • 15 gün önce

THIS MIGHT BE THE OPPENHEIMER MOMENT FOR OPEN-SOURCE AI. Kimi K3 packs 2.8T parameters, a 1M context window, native multimodality, and serious agentic coding performance. made with kimi k3

THIS MIGHT BE THE OPPENHEIMER MOMENT FOR OPEN-SOURCE AI. Kimi K3 packs 2.8T parameters, a 1M context window, native multimodality, and serious agentic coding performance. made with kimi k3

Farhan

14,099 görüntüleme • 28 gün önce

Just tested Kimi.ai K3 against GPT-5.6 and Grok 4.5 on the same task: build a Subway Surfers game. Kimi K3 was my favorite. Better UI, smoother gameplay, and the overall experience felt more polished. GPT-5.6 was solid too, while Grok 4.5 fell behind on the UI. The interesting part is that all three needed at least one follow-up prompt. Kimi also took a bit longer to generate, but the extra time paid off. Ran Kimi K3 through Nebius Token Factory. Low inference cost + a really capable open source model is a great combo.

Just tested Kimi.ai K3 against GPT-5.6 and Grok 4.5 on the same task: build a Subway Surfers game. Kimi K3 was my favorite. Better UI, smoother gameplay, and the overall experience felt more polished. GPT-5.6 was solid too, while Grok 4.5 fell behind on the UI. The interesting part is that all three needed at least one follow-up prompt. Kimi also took a bit longer to generate, but the extra time paid off. Ran Kimi K3 through Nebius Token Factory. Low inference cost + a really capable open source model is a great combo.

Arindam Majumder 𝕏

46,107 görüntüleme • 17 gün önce

WOW: Kimi K3 just killed the AI subscription era 🇨🇳 On July 27, Moonshot publishes the full K3 weights under a modified MIT license. ▫️ 2.8 trillion parameters ▫️ 1M token context ▫️ Open weights, yours to run "The frontier becomes a file." The closed labs charge you monthly for access. Moonshot just made the frontier a download. Does open weight end the subscription model, or do the big labs still win on polish?

WOW: Kimi K3 just killed the AI subscription era 🇨🇳 On July 27, Moonshot publishes the full K3 weights under a modified MIT license. ▫️ 2.8 trillion parameters ▫️ 1M token context ▫️ Open weights, yours to run "The frontier becomes a file." The closed labs charge you monthly for access. Moonshot just made the frontier a download. Does open weight end the subscription model, or do the big labs still win on polish?

Ruben

94,908 görüntüleme • 24 gün önce