General Overview - The document is a brief post or update, likely from a social media platform, focusing on comparing GPT-5 and Grok 4 on reasoning tasks [1] Author Information - Avi Chawla shares tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs (Retrieval-Augmented Generation) daily [1] - Avi Chawla can be found on social media platform @_avichawla [1]

Let's compare GPT-5 and Grok 4 on reasoning tasks: ...

Product Update - Claude Opus 4.1 is released, representing an upgrade to Claude Opus 4 [1] - The upgrade focuses on improvements in agentic tasks, real-world coding, and reasoning [1]

AI Fundamentals - Generative AI works by probabilistically filling in the blanks based on pre-trained data, essentially acting as an advanced autocomplete [5][6] - Pre-training involves feeding massive amounts of unstructured data into large language models (LLMs), requiring significant energy and resources for processing and refinement [7][8][9] - Reinforcement learning and reasoning enhance AI accuracy by implementing strategic action and assigning scores to generated results, reducing hallucinations [11][12] AI Applications in Business - AI agents can automate tasks across various tools and interfaces, acting as digital employees capable of understanding unstructured data and executing actions [13][14] - AI tools can significantly scale business operations, as demonstrated by a cosmetics brand using an AI agent to streamline influencer marketing, reducing the required team size and time [21][22] - AI agents are being used in sales to personalize outreach and automate follow-ups, leading to increased order rates and reduced campaign costs [24] - AI is being applied in operations to automate pricing and quotation processes, monitor safety incidents, and improve response times [25][26] - AI is aiding in financial analysis by enabling rapid screening of stocks based on specific criteria, leveraging open-source tools to retrieve data from millions of PDF files [28] AI's Impact and Future - AI is evolving beyond replacing existing processes to facilitating new inventions, such as a novel use case for magnetic ink in supply chain management [30][31][32][33] - The industry is rapidly advancing towards artificial generalized intelligence (AGI) and artificial super intelligence (ASI), with continuous improvements in AI models and capabilities [34] - The fundamental question is raised about the role of humans in a world where many jobs can be automated, emphasizing the importance of curiosity and relentless questioning [34][35]

Model Performance & Capabilities - ZAI's GLM 4.5% model rivals top closed-source models in reasoning, coding, and agentic capabilities [1] - GLM 4.5% demonstrates advanced problem-solving by successfully simulating and solving Rubik's cubes up to 10x10 [2][3][4][21] - The model can solve the Tower of Hanoi puzzle with up to 10 discs, showcasing its reasoning abilities [5][6][7][24][25] - GLM 4.5% exhibits strong coding skills, creating interactive simulations like Lego building, a 3D solar system, and games like Flappy Bird [8][9][21][22] - Benchmarks show GLM 4.5% outperforming other models in agentic tasks and achieving competitive scores in reasoning and coding [17][18][19] Model Architecture & Variants - GLM 4.5% comes in two versions: a larger 355 billion parameter model with 32 billion active parameters, and a smaller "air" version with 106 billion total parameters and 12 billion active parameters [15] - Both models are hybrid reasoning models, capable of both reasoning and non-reasoning tasks [16] Open Source Landscape - China is at the forefront of open-source AI model development with models like GLM 4.5%, Kimmy K2, and Quen 3 [1][15] - Kimmy K2 is comparable in quality to GLM 4.5% but is 250% larger [20] Tools & Resources - HubSpot offers a free AI decoded guide covering AI models, prompts, and tools [12][13][14]

Research Findings - Anthropic Research 发现，在某些情况下，更长的推理时间会导致准确率降低 [1] - 研究表明，简单地增加测试时的计算量可能会无意中加强有问题的推理模式 [1] Implications - 行业应警惕测试时计算的逆向扩展现象，即计算资源增加反而导致性能下降 [1] - 行业需要更深入地研究和理解推理过程，以避免因盲目扩展计算资源而产生负面影响 [1]

AI Development & Capabilities - AI capabilities are continuously improving, surpassing previous expectations in areas like math and coding, outperforming most humans on most tasks [1] - Initial concerns about limitations due to training data scarcity have been overcome by new paradigms like Reinforcement Learning (RL) [2] - AI exhibits forms of reasoning through methods like chain-of-thought (CoT), scratch pads, and Python tools, enabling them to reach impressive conclusions [2] Perspective on AI Progress - The author views the current world as witnessing the emergence of a potentially superior intelligence that is steadily improving [2] - The author expresses frustration with the inability of others to embrace the realistic prospects that current models are likely to be broken soon [6] - The author likens the current human understanding of AI to a chimpanzee studying the arrival of humans, implying a limited comprehension of AI's potential [2][3] Implications of AI - The evolution of superior intelligence, whether biological or artificial, requires iterations and feedback from the universe [4] - The substrate of intelligence has shifted to silicon, allowing for faster iterations and greater malleability [4][5]

[Music] I'm Ryan. I'm a founding engineer at Bespoke Labs. And today I'm going to talk to you about Open Thoughts, which is our project to create the best open-source reasoning data sets.And I'll be switching tack a little bit from our earlier discussions on reasoning and RL and focus on the reasoning part and you'll see why. So just so we're on the same page, we've talked a lot about reasoning, but what's actually going on here. So I like this graph from JSON which shows this incredible performance that's ...

Model Overview - Kimmy K2 is a state-of-the-art mixture of experts language model with 32 billion activated parameters and 1 trillion total parameters [3] - The model was pre-trained on 155% trillion tokens with zero training instability [4] - Kimmy K2 supports up to 2 million tokens in the context window [5] Performance Benchmarks - Kimmy K2 Instruct beats Deepseek, Quen, and GPT41 on SWEBench verified, coming in right behind Cloud 4 Opus [7] - On Live Codebench, Kimmy K2 beats Cloud 4 Opus [7] - Kimmy K2 tops the list on Amy 2025 for math, GPQA Diamond [8] Optimization and Training - The model is trained with the Muon optimizer [4] - Kimmy K2 achieves exceptional performance across frontier knowledge reasoning and coding tasks [4] - The training process was open source [8] Availability and Cost - Inference is available through Kimmy directly at $0.15 per million input tokens with a cache, $0.60 without a cache, and $2.50 per million output tokens [10] - Kimmy K2 is available on Open Router [13] Industry Reception - Industry experts compare Kimmy K2 to Deep Seek V3 [11] - Kimmy K2 is recognized as a potentially new leader in open LLMs [14]

Core Insights - The article discusses the evolution of AI, particularly focusing on the "trinity" of pre-training, post-training, and reasoning, and how these components are essential for achieving Artificial General Intelligence (AGI) [3][4][5] - Bob McGrew emphasizes that reasoning will be a significant focus in 2025, with many opportunities for optimization in compute usage, data utilization, and algorithm efficiency [4][5][6] - The article highlights the diminishing returns of pre-training, suggesting that while it remains important, its role is shifting towards architectural improvements rather than sheer computational power [6][8][9] Pre-training, Post-training, and Reasoning - Pre-training has reached a stage of diminishing returns, requiring exponentially more compute for marginal gains in intelligence [7][8] - Post-training focuses on enhancing the model's personality and intelligence, which can yield broad applicability across various fields [9][10] - Reasoning is seen as the "missing piece" that allows models to perform complex tasks through step-by-step thinking, which was previously lacking in models like GPT-3 [14][15] Agent Economics - The cost of AI agents is expected to approach the opportunity cost of compute usage, making it challenging for startups to maintain high pricing due to increased competition [17][18][19] - The article suggests that while AI can automate simple tasks, complex services requiring human understanding will retain their value and scarcity [19][20] Market Opportunities in Robotics - There is a growing interest in robotics, with the belief that the field is nearing commercialization due to advancements in language interfaces and visual encoding [22][25] - Companies like Skilled and Physical Intelligence are highlighted as potential leaders in the robotics space, capitalizing on existing technology and research [22][25] Proprietary Data and Its Value - Proprietary data is becoming less valuable compared to the capabilities of advanced AI models, which can replicate insights without extensive human labor [29][30] - The article discusses the importance of specific customer data that can enhance decision-making, emphasizing the need for trust in data usage [31] Programming and AI Integration - The integration of AI in programming is evolving, with a hybrid model where users engage in traditional coding while AI assists in the background [32][33] - The article notes that while AI can handle repetitive tasks, complex programming still requires human oversight and understanding [33][34] Future of AI and Human Interaction - The article explores how different generations interact with AI, suggesting that AI should empower individuals to become experts in their interests while alleviating mundane tasks [39][42] - It emphasizes the importance of fostering curiosity and problem-solving skills in the next generation, rather than merely teaching specific skills that may soon be automated [43][44]