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Nov 13Fri
  1. Hacker News · 300+82

    Google launches Gemini 4 Hydrogen and Orchestrator in Antigravity.

    Google has launched Gemini 4 Hydrogen in Antigravity and added an Orchestrator model option, combining Hydrogen with stronger models like Gemini 4 Carbon for long-period tasks.

    Same story · 4 outlets
  2. Ars Technica · AI82

    Google launched Gemini 4 Hydrogen, replacing 3.8 Flash

    Google releases Gemini 4 Hydrogen, replacing Gemini 3.8 Flash in its own products, with access restrictions more relaxed than previous Argon and Carbon, available for use upon release

    Same story · 4 outlets
Nov 12Thu
Nov 11Wed
  1. Google DeepMind87

    Google DeepMind releases the Gemini 4 Carbon cutting-edge model

    Google DeepMind has opened Gemini 4 Carbon to developers, enterprises, scientists, and consumers, claiming it has reached cutting-edge levels in network security capabilities and long-range intelligent agent workflows.

    Same story · 5 outlets

    Why it matters: The official provides the positioning division of roles between Carbon and Argon, multiple benchmark rankings, and cost comparisons per task to help determine when to use a heavier model.

Nov 10Tue
  1. MarkTechPost82

    Anthropic released Claude Fable 5.2 and Mythos 5.2: The same model, different safety guards.

    Anthropic releases Claude Fable 5.2 and Claude Mythos 5.2, both based on the same underlying model. Fable 5.2 adds a classifier to intercept high-risk cybersecurity tasks and most biology research tasks, while Mythos 5.2 removes these restrictions and requires access through the Cyber Verification Program and Life Sciences Verification Program.

    Same story · 4 outlets

    How the accounts differ:TechCrunch states the input price of Fable is reduced to $8 per million tokens; others do not mention the price.

  3. ITHome · AI

    Anthropic released Claude Fable 5.2, surpassing GPT-6 Astra in programming capability and topping multiple benchmarks.

    Anthropic released the Claude Fable 5.2 model, achieving 49.1% programming capability in the FrontierCode 2.0 test, surpassing GPT-6 Astra's 42.8%; scoring 74.2% in the Humanity's Last Exam test, leading GPT-6 Astra's 57.2%. The model's output speed has increased by up to 30%, with a 20% cost reduction and API call fees lowered to $3/ million tokens.

    Same story · 4 outlets

    How the accounts differ:TechCrunch states the input price of Fable is reduced to $8 per million tokens; others do not mention the price.

  5. Claude · YouTube75

    Anthropic released Claude Fable 5.2

    Anthropic released Claude Fable 5.2, stating that it has improved in coding, reasoning, and vision, targeting the most complex and open-ended technical work. The official recommendation is to start most coding and knowledge tasks from Opus 5.5, and Fable 5.2 has been launched in the Pro, Max, Team, and Enterprise packages.

    Same story · 4 outlets

    How the accounts differ:TechCrunch states the input price of Fable is reduced to $8 per million tokens; others do not mention the price.

  6. Claude · YouTube75

    Anthropic released Claude Fable 5.2

    Anthropic releases Claude Fable 5.2, designed for the most challenging technical challenges, with improvements in coding, reasoning, and vision, suitable for the most ambitious open-ended technical work. The official recommendation is to use Opus 5.5 for most coding and knowledge tasks, and Fable 5.2 is now available in the Pro, Max, Team, and Enterprise packages.

    Same story · 4 outlets

    How the accounts differ:TechCrunch states the input price of Fable is reduced to $8 per million tokens; others do not mention the price.

    Why it matters: The official statement explains the positioning and applicable scenarios of Fable 5.2, and provides a choice suggestion between Opus 5.5, making it easier to decide which model to use.

  7. TechCrunch · AI82

    Anthropic releases Mythos 5.2 and Fable 5.2 models, with Fable input price reduced to $8 per million tokens

    Anthropic releases Mythos 5.2 and Fable 5.2, claiming new models have improvements in coding, reasoning, and vision, and communicate more clearly with users. Fable's input price drops from $10 per million tokens to $8, lower than major competitor GPT-6 Astra, with a 30% faster response speed, and can support zero data retention (ZDR) policies through Enterprise Frontier Safeguards.

    Same story · 4 outlets

    How the accounts differ:TechCrunch states the input price of Fable is reduced to $8 per million tokens; others do not mention the price.

Nov 7Sat
Nov 6Fri
  2. ITHome · AI55

    webAI's Open Source TwIL-8B Inference Model: Focused on Mathematics and Programming, Outperforms Gemma 4 31B in Long Board Performance

    webAI Launches TwIL-8B Inference Model, Optimized for Math and Code Tasks Based on IBM Granite-4.2-8B, Scores Better than Gemma 4 31B in MATH-500 and AIME 2025 Tests, Best in LiveCodeBench Group, IFBench on Par with Gemma 4 12B. Model Inference Capability Reaches 88~93% of Frontier Models, Reduces AI Usage Costs but Has Limited Knowledge Base.

Nov 5Thu
  1. Hacker News · 300+65

    Ripple-65B Preview: US Open Source Ultra-Large Scale Model Achieves SOTA on Agentic Coding

    Ripple-65B Improves Computational Efficiency by Optimizing SFT and RL Schemes, Using Synthetic Data Pipelines and Quality Filtering, Employing a Hybrid RL Scheme with Truncated Importance Sampling, Score Centering, and Linear Length Penalty in a High-Quality Long-Term Synthetic Environment, Achieving a TerminalBench 2.1 Score of 80.5 with Only 1.4M Rollouts, 70 Times Fewer than Reflection Beam

    Same story · 3 outlets
  2. ByteDance Seed · Hugging Face new models

    ByteDance-Seed/Qwen3-30B-A3B-MoE-SAE released

    ByteDance-Seed/Qwen3-30B-A3B-MoE-SAE is released, containing TopK sparse autoencoders (k=100, 2^20 latent variables) trained on layers 4, 30, and 46 of the Qwen3-30B-A3B model. Each SAE is approximately 17.2 GB, with reconstruction variance unexplained scores of 12.6%, 25.1%, and 7.6% respectively.

  3. Cognition Blog

    SWE-2 Flash Released: A Low-Cost Intelligent Coding Model in the US

    SWE-2 Flash introduces a mid-training stage before reinforcement learning for the first time, significantly outperforming the baseline model Inkling-Small. Terminal-Bench 4.0 scores increased from 0.9% to 20.0%, and FrontierCode 2.0 improved from 3.2% to 24.0%, surpassing all US open-source models. This model is available in Devin Desktop and CLI, priced at one-fifth of SWE-2 and three times faster.

  4. MarkTechPost

    Ricursive AI has released Ripple-65B Preview: a 65B dense open-source model that scored 80.5 on Terminal-Bench 2.1.

    Ricursive AI has released Ripple-65B Preview, a 65B dense open-source model, scoring 80.5 on Terminal-Bench 2.1, becoming the highest-scoring open-source model in the United States. Its parameter size is 65B (active parameters), pre-training used 12T tokens, with a maximum context length of 1M, and is released under the Apache 2.0 license.

    Same story · 3 outlets
Nov 2Mon
  1. ITHome · AI72

    Meituan LongCat releases LongCat-2.5 native multimodal model, 1.6T parameters targeted at long-range intelligent agent tasks

    Meituan Longma has released the native multimodal model LongCat-2.5 for long-range intelligent agent tasks, with a total parameter count of 1.6 trillion, activating about 48 billion parameters per token, enhancing native visual understanding and intelligent agent programming capabilities based on LongCat-2.0.

    Same story — showing the featured version《》

Nov 1Sun
Oct 30Fri
Oct 29Thu
  1. Mistral AI82

    Mistral releases open-source model Mistral Large 4 (Le Chonk) with trillion parameters

    Mistral releases open-weight version of its flagship trillion-parameter native multimodal model Mistral Large 4 (Le Chonk), which has been in public preview for three weeks.

    Why it matters: The official provides data on the encoding, security, and inference efficiency of the trillion-parameter open-source model, as well as weight downloads and self-hosting paths.

  2. ITHome · AI85

    Google has released Gemini 4 Carbon, focusing on cybersecurity and topping the Artificial Analysis Intelligence Index.

    Google has officially released the next-generation cutting-edge large model Gemini 4 Carbon, featuring top-tier cybersecurity capabilities and long-range task processing, ranking first with a score of 59 in the Artificial Analysis Intelligence Index v5.0.

    Same story · 5 outlets
  3. MarkTechPost80

    Google DeepMind released Gemini 4 Carbon, emphasizing network security defense and coding.

    Google DeepMind has launched Gemini 4 Carbon, describing it as a cutting-edge model for long-period intelligent agent coding and cybersecurity defense, ranking first with 59 points on the Artificial Analysis Intelligence Index v5.0.

    Same story · 5 outlets
  4. Ars Technica · AI65

    Google has released Gemini 4 Carbon: a cutting-edge AI model that cannot be used immediately.

    Google has released Gemini 4 Carbon, reaching new thresholds in coding, agent tasks, and scientific analysis, but it has not yet been made available for use. The model significantly enhances vulnerability detection, validation, and repair capabilities in cybersecurity applications, with a 200% higher probability of generating bug-free code compared to Argon and a 10 times greater ability to generate high-quality patches compared to non-Google models.

    Same story · 5 outlets
  5. Artificial Analysis Articles85

    Gemini 4 Carbon topped the Artificial Analysis Intelligence Index v5

    Google's Gemini 4 Carbon ranked first with 59 points on the Artificial Analysis Intelligence Index v5, leading in four out of nine evaluation items: HLE-Diamond, Terminal-Bench-Science, AA-Omniscience, and AutomationBench-AA

    Same story · 5 outlets
  6. Google DeepMind82

    Google DeepMind releases the Gemini 4 Carbon cutting-edge model

    Google DeepMind releases the Gemini 4 Carbon, claiming it has the strongest capabilities in cybersecurity and long-term tasks, ranking first with a score of 59 in the Artificial Analysis Intelligence Index v5.0

    Same story · 5 outlets
Oct 27Tue
Oct 26Mon
Oct 24Sat
  1. MarkTechPost83

    Moonshot AI has released Kimi K3.1 Pro: a 2.8T total parameter, 104B activated MoE model that supports a context length of 1M.

    Moonshot AI has released the first model of the K3.1 series, Kimi K3.1 Pro, which adopts a MoE architecture with a total parameter count of 2.8T and an activation parameter count of 104B, supporting a context length of 1M tokens. It has been launched on Kimi.ai, Kimi Work, Kimi Code, and Kimi API. The API pricing is $2 per million tokens for input and $10 for output, lower than K3's $3 and $15.

    Same story — showing the featured version《》

Oct 22Thu
  1. The Decoder74

    Recursive releases its first model developed in collaboration with Eureka Machine

    AI startup Recursive has released its first language model, Flint. During the development process, the AI system named Eureka Machine participated in proposing ideas, building, testing, and planning subsequent experiments.

    Why it matters: Recursive publicly announces its first model developed with the assistance of an automated research system, allowing readers to observe how far AI-assisted R&D can go.

Oct 21Wed
  1. Ant Group inclusionAI · Hugging Face new models35

    inclusionAI/Ling-3.0-tiny-UBTree release: A fine-tuned model based on UBTree technology

    inclusionAI released the UBTree fine-tuned version of Ling-3.0-tiny, Ling-3.0-tiny-UBTree-B16, with 183.1M parameters, combining a five-layer DFlash backbone and a bigram selector, achieving 2.6-3.5 times speed improvement on benchmarks like GSM8K and MATH-500. This model needs to be used in conjunction with the target model. The UBTree adaptation for the open-source version of SGLang has not yet been released.

Oct 19Mon
  1. Inception Labs Blog

    The rise of Mercury 2 and real-time sub-agents

    Mercury 2 completes context compression tasks in just 150 seconds while maintaining the same quality as Sonnet 4.6, with a 5-fold speed increase and a 90% cost reduction. Augment Code uses it for enterprise-level coding AI agents, achieving an 82% reduction in compression latency and a 90% cost cut, and dynamically allocates tasks through the Prism model router, reducing total LLM expenditure by 30%.