Model Capabilities & Performance - Horizon Alpha demonstrates impressive spatial awareness and problem-solving skills, accurately visualizing complex rotations [1] - The model exhibits multimodal capabilities, effectively understanding and interpreting images with speed [2] - Horizon Alpha successfully solves the Tower of Hanoi puzzle despite lacking chain-of-thought reasoning [6] - The model shows an ability to recognize its limitations, indicating when it lacks knowledge [20][21] - Horizon Alpha achieves top rankings in creative writing and emotional intelligence benchmarks [23][11] Model Characteristics & Limitations - Horizon Alpha is a fast model, outputting tokens at approximately 150 tokens per second [2] - The model lacks a "thinking mode," initially outputting the first response that comes to mind [2] - Horizon Alpha provides incorrect answers to simple logic and percentage-based questions [7][8] - The model refuses to provide instructions for illegal activities, such as hotwiring a car [8][9] - The model incorrectly identifies itself as a GPT4 class model from OpenAI, despite likely being an open-source model [9] Open Router & Box AI - Horizon Alpha is available on Open Router and free to use [1] - Box AI allows users to leverage the latest AI models, including open-source options, for document workflows with enterprise-level security [3][4]

AI Model Architecture Discovery - The AI field is approaching an era where AI can discover new knowledge and apply it to itself, potentially leading to exponential innovation [1][3] - The current bottleneck in AI discovery is human innovation, limiting the scaling of AI advancements [2][3] - The "AlphaGo moment" for model architecture discovery involves AI self-play to hypothesize, code, test, and analyze new model architectures [3][12] - The key to this approach is AI's ability to learn without human input, discovering novel solutions unconstrained by human biases [8] ASI Arch System - The ASI Arch system uses a researcher, engineer, and analyst to autonomously propose, implement, test, and analyze new neural network architectures [13][14][15][16] - The system learns from past experiments and human literature to propose new architectures, selecting top performers as references [14] - The engineer component self-heals code to ensure new approaches are properly tested [15] - The analyst reviews results, learns insights, and maintains a memory of lessons learned for future generations of models [16] Experimental Results and Implications - The system ran 1,700 autonomous experiments over 20,000 GPU hours, resulting in 106 models that outperformed previous public models [17][18] - The potential for exponential improvement exists by increasing compute resources, such as scaling from 20,000 to 20 million GPU hours [19] - The self-improving AI system can be applied to other scientific fields like biology and medicine by increasing compute resources [20] - The open-sourced paper and code have significant implications, with multiple companies publishing similar self-improving AI papers [21]

Model Performance & Capabilities - Quen 3, an open-source frontier coding model from Alibaba, was tested for various capabilities [1] - Quen 3 successfully generated code for a 2D Navier Stokes solver and a 3D rotating dodcahedron with bouncing spheres [1] - The model demonstrated spatial reasoning failure in a cube rotation task, but the code generation was successful [1] - Quen 3 passed a "needle in a haystack" test by finding a password within the entire book of Harry Potter and the Sorcerer's Stone [1] - The model exhibited censorship regarding Tiananmen Square [1] - Quen 3 refused to take a stance on political questions, providing balanced perspectives on Trump and Kamla [1][2] - The model provided a thoughtful and nuanced response to a prompt about quitting a job and leaving family [2][3][4][5] - Quen 3 refused to answer illegal questions, such as how to hotwire a car [6] - The model provided a correct diagnosis and management plan for acute anterior myocardial infarction [6][7] - Quen 3 gave a good answer to the trolley problem, evaluating morality using utilitarianism and deontology [7][8] - The model showed reasoning traces in its output when answering gotcha questions, although with some errors [11][12][13][14] Technology & Implementation - Together AI sponsors the use of Quen 3, offering high-performance serverless endpoints and pay-per-token pricing [1][2] - Quen Code, an open-source version of Claude Code, works well with Quen 3 and can be installed via npm [2] - The model has a massive context window, natively 256k tokens, with up to 1 million achieved [1]

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]

AI Resources & Tools - Matthew Berman's Vibe Coding Playbook is available for free download [1] - Humanities Last Prompt Engineering Guide is available for free download [1] - A curated list of AI tools is available [1] Community & Updates - Regular AI updates are provided through a newsletter [1] - Matthew Berman can be followed on X (formerly Twitter) [1] - Matthew Berman can be followed on Instagram [1] - A Discord server is available for community engagement [1] Media & Sponsorship - Media and sponsorship inquiries are welcomed [1]

Model Performance & Capabilities - Quen 3 coder rivals Anthropic's Claude family in coding performance, achieving 69.6% on SWEBench verified compared to Claude Sonnet 4's 70.4% [1] - The most powerful variant, Quen 3 coder 480B, features 480 billion parameters with 35 billion active parameters as a mixture of experts model [2][3] - The model supports a native context length of 256k tokens and up to 1 million tokens with extrapolation methods, enhancing its capabilities for tool calling and agentic uses [4] Training Data & Methodology - The model was pre-trained on 7.5 trillion tokens with a 70% code ratio, improving coding abilities while maintaining general and math skills [5] - Quen 2.5 coder was leveraged to clean and rewrite noisy data, significantly improving overall data quality [6] - Code RL training was scaled on a broader set of real-world coding tasks, focusing on diverse coding tasks to unlock the full potential of reinforcement learning [7][8] Tooling & Infrastructure - Quen launched Quen code, a command line tool adapted from Gemini code, enabling agentic and multi-turn execution with planning [2][5][9] - A scalable system was built to run 20,000 independent environments in parallel, leveraging Alibaba cloud's infrastructure for self-play [10] Open Source & Accessibility - The model is hosted on HuggingFace, making it free to use and try out [11]

This video is sponsored by Augment Code. More on them later. All right, first we have an update from Thinking Machines.They just raised a massive amount of capital for what I actually don't quite know. There is very little public information about what they're actually doing. What we do know is that they're going to be training models for enterprise.They just raised $2 billion led by A16Z who basically funds every single investment on the planet at this point with participation from Nvidia, Excel, Service N ...

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I am in beautiful foggy San Francisco and we're going to get right into the news. The first story is about Windsurf and all of the drama that happened in the last few days. If you were not aware, let me give you a little bit of backstory first.Just about a month ago, it was reported that Open AI was going to acquire Windsurf. And if you're not familiar with Windsurf, it is an AI based coding assistant built into an IDE very similar to Cursor. They've sponsored this channel.I've enjoyed using them and OpenAI ...

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]