RT Avi Chawla (@_avichawla)I built my own ChatGPT from scratch, and you can too.Karpathy's nanochat is a single, clean, minimal, and hackable codebase to build a modern LLM.By setting this up, you'll learn how to:> train a tokenizer from the ground up> pre-training: master next-word prediction> mid-training: teach the model to hold conversations> sft: fine-tune on high-quality dialogue datasets> evaluate and log every step of the processI've done this on a LightningAI studio, and you can reproduce everythin ...

RT Nick Szabo (@NickSzabo4)@SeanParnellUSA You're just parroting word-for-word what Hegseth said. An LLM has more originality. ...

[music] My name is Samir and I'm the head of AI engineering at the browser company of New York. And today I'm going to talk a little bit about how we transitioned from building ARC to DIA and the lessons we learned in building an AI browser. But first, a little about the browser company.So we started with a mission to rethink how people use the internet. At its core, we believe that the browser is one of the most important pieces of software in your life and it wasn't getting the attention it deserved. Simp ...

BREAKING:🥇 Grok Code Fast 1 Market Leader#1 Overall on OpenRouter508B tokens processed, 39% market shareMost-used model by developers globally#1 in Token Usage & Market ShareMost popular LLM for English usageGrok Code Fast 1 isn’t just strong on paper it’s the most deployed and trusted model in production right now. ...

转自：新华财经 •澳新银行：马来西亚2026年GDP料增4.5%，林吉特有望升至4.00 •美国银行：商品与服务通胀背离，美联储2026年1月或维持利率不变 •阿波罗资管：美联储警惕2026年滞胀风险，货币政策陷入两难 【机构分析】 •澳新银行预计马来西亚2026年GDP增长4.5%，受益于强劲内需、AI带动的电子出口及稳健。财政政策 聚焦税收改革与支出克制，货币政策料保持稳定，林吉特有望走强，年底美元兑林吉特或达4.00。 •马来亚银行证券预测，菲律宾比索或于2026年下半年走软，主因美元重拾强势及国内负面因素持续拖 累。防洪资金腐败丑闻正抑制菲政府支出与经济增长，并打击外资信心，加剧资本外流和本地资产压 力。增长乏力或迫使菲律宾央行在2026年底前额外降息50个基点，削弱比索利差优势，降低套利吸引 力。 •美国银行指出，关税推高商品通胀，而医保因素或令服务通胀趋缓可能促使美联储1月按兵不动。 •美国银行指出，印度凭借低廉数据成本、超7亿年轻网民及电信运营商免费AI订阅策略，已成为全球 LLM普及率最高、最活跃的AI消费市场，并正成为"代理AI"技术的关键试验田；但本土初创企业面临 国际巨头加剧的竞争压力。 ...

Core Viewpoint - The recent launch of GWM Worlds and GWM Robotics by Runway pushes video generation towards an interactive "world simulation" paradigm, reigniting discussions on the definition and scope of "world models" as interfaces for creation and interaction, simulators for training and evaluation, or cognitive frameworks for reasoning and decision-making [1]. Group 1: Evolution of World Models - Over the past two years, world models have evolved to be considered on par with LLMs in the AGI landscape, transitioning from a narrow definition focused on reinforcement learning to a broader understanding that includes generative modeling [4]. - Initially, world models were seen as internal environment models for agents, predicting future states based on current conditions and actions, allowing for internal simulation and decision-making [5]. - The engineering perspective defined world models as a combination of three capabilities: compressing high-dimensional perception into usable representations, predicting future states over time, and utilizing predictions for planning and decision-making [6]. - By 2024, the understanding of world models expanded to encompass general world evolution modeling, with a trend from language generation to image generation, and ultimately to 3D and world generation [6]. - The boundaries of the world model concept have become more ambiguous, with ongoing debates about the nature of representations, the incorporation of physical laws, and the organization of input relationships [6]. Group 2: Industry Layout and Trends - Major companies are investing in world models, questioning whether they are enhancing their "data engines" or building new frameworks for "spatiotemporal cognition" [3]. - In February 2024, OpenAI referred to the video generation model Sora as "world simulators," emphasizing their ability to learn the three-dimensional structure and physical laws of the real world [6]. - Concurrently, LeCun introduced V-JEPA, which focuses on predicting masked video segments in abstract representation space, allowing for higher training efficiency by discarding unpredictable information [6]. - The current discourse has shifted from whether to develop world models to how to model them, with debates on whether to abstract from pixel levels or to directly operate in abstract spaces [7]. - There is a recognition that existing approaches may only capture partial physical laws, indicating a need for representations of isolated objects and a priori laws of change across space and time to achieve a coherent world model [7]. Group 3: Definition and Ambiguity of World Models - By 2025, world models are positioned alongside LLMs, with companies like Google DeepMind, Meta, and Nvidia shifting focus from pure LLMs to world models, aiming for "Physical AI + superintelligence" due to stagnation in LLM advancements [8]. - The distinction between world models and existing generative AI lies in the former's goal to construct internal representations of environments that include physical, temporal, and spatial dimensions for planning and decision-making [9]. - The term "world model" has become ambiguous, referring to latent states within systems, game-like simulators for training agents, or any content pipeline capable of generating navigable 3D scenes [9]. - An analysis from Entropy Town in November 2025 categorized world models into three technical routes: interface, simulator, and cognitive framework, highlighting the ongoing ambiguity in the field [9].

First LLM contact from space 🛰️ using our highly efficient open source Gemma models! Huge congrats to @PhilipJohnston and the @Starcloud_Inc_ team!Philip Johnston (@PhilipJohnston):We just trained the first LLM in space using an @Nvidia H100 on Starcloud-1! 🚀We are also the first to run a version of @Google's Gemini in space!This is a significant step on the road to moving almost all compute to space, to stop draining the energy resources of Earth and https://t.co/csc9MjDPco ...

Grok Rankings Update December 12Grok Code Fast 1 (The Market Dominator)This model has recaptured the number one overall spot, securing its position as the high-volume, cost-efficient leader for all developer agents.#1 Overall Position on OpenRouter Leaderboard with 880 billion tokens, nearly double the nearest competitor#1 in Categories Token Share at 36.8 percent dominance#1 in Languages Token Share with 16.3 percent#1 Most Popular LLM for English by overall usage volume#1 on Kilo Code Leaderboard top app# ...

Hey, I'm Tanish from Langchain. Today I'm going to go through how to use Open Router's new broadcast feature with Langchain to send traces to Langmith. The cool thing about broadcast is it stores destination information, which in this case is Langmith server side.So the only thing that you need to worry about in your code is your open router API key. Let's go through how to set this up. So let me walk you through a quick code snippet.This uses lang chain's init chat model in order to initialize a model and ...

Voice is one of the most natural ways to interact with AI. And as the models are getting better, I'm excited about new use cases and interaction patterns that it's going to unlock, especially in industries like education and customer service. It's surprisingly easy to get started building a voice agent.And so let's go through that in this video. I'm Tannushri and I'm going to show you how to build a voice agent, specifically a French tutor with this framework called Pipecat. going to walk through how it wor ...