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Amazon scales back most Nova AI models and reorganizes team
According to Bloomberg, after its in-house large language model failed to achieve the expected results, Amazon scaled back investment in most Nova models and placed its text, video, and image generation models on a minimal-resource footing. The company shifted compute and personnel to a single frontier model project led by Pieter Abbeel; a week earlier, Amazon had also closed an 80-person AGI research office in San Francisco. No further progress has been disclosed.
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How Gen Digital Built... with Amazon Bedrock AgentCore Web Search
Gen Digital used Amazon Bedrock AgentCore Web Search to build an AI-curated news engine for the Norton Revamp, providing real-time web information with citations for content suggestions, goal recommendations, and news feeds. The platform launched in March 2026, with content generated by Amazon Nova Lite; Web Search is integrated into AgentCore Gateway as an MCP-compatible managed connector, returning source URLs, titles, and publication dates.
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Anthropic Fable 5.5 rumor: 14 trillion parameters, animation generated purely from code, to replace video and image models within 6 months?
A rumored Anthropic model, Fable 5.5, has had real-world tests leak on X: by autonomously writing Python code to generate a single HTML file, it played an animated short in the browser, and some users say it autonomously completed a 66-second short film in 2 hours and 40 minutes. Rumors say it belongs to the Mythos tier, has a parameter scale of 14 trillion, and uses a hybrid architecture combining GDN with MLA, but the weight leak on Hugging Face has not yet been independently verified. The article also mentions that on October 13, LiteLLM briefly showed a claude-fable-5-5 entry in the Azure catalog, which was withdrawn the next day, and that the words Claude Mythos 5.5 appeared in the source code of Anthropic's official website.
A rumored Anthropic model, Fable 5.5, has had real-world tests leak on X: by autonomously writing Python code to generate a single HTML file, it played an animated short in the browser, and some users say it autonomously completed a 66-second short film in 2 hours and 40 minutes. Rumors say it belongs to the Mythos tier, has a parameter scale of 14 trillion, and uses a hybrid architecture combining GDN with MLA, but the weight leak on Hugging Face has not yet been independently verified. The art
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The Jevons Paradox Meets Chip Iteration: Revisiting AI Compute Investment
Gartner estimates that by 2030, AI data centers will need approximately $8.5 trillion in revenue to cover their investments, but actual AI-related revenue inflows will reach at most only $3.6 trillion, leaving more than $3.5 trillion in investment exposed to asset impairment risk. Based on Ornn leasing data, a16z points out that the actual rental rates for A100, H100, B200, and H200 are currently 9%, 54%, 111%, and 143% above expected levels, respectively. The author argues that demand growth and impairment of the first wave of infrastructure may occur simultaneously, and that the silicon roadmap determines the true pace of chip depreciation.
Gartner estimates that by 2030, AI data centers will need approximately $8.5 trillion in revenue to cover their investments, but actual AI-related revenue inflows will reach at most only $3.6 trillion, leaving more than $3.5 trillion in investment exposed to asset impairment risk. Based on Ornn leasing data, a16z points out that the actual rental rates for A100, H100, B200, and H200 are currently 9%, 54%, 111%, and 143% above expected levels, respectively. The author argues that demand growth an
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Tripo Agentic Canvas Tutorial: Build a Cyberpunk Scene in 20 Minutes
Tripo has released a tutorial demonstrating how to use the node-based Agentic Canvas in Tripo Studio to start from a single reference image, describe the goal in natural language, and build a cyberpunk 3D scene in about 20 minutes and export a UE5 project. In the workflow, the main scene uses the H3.1 model to retain richer geometric and surface detail, while props such as signs and trash cans use P2.0 (Smart Mesh) to make polygon counts easier to control. The Agent offers two modes, Control and Auto; generated results can be adjusted for layout and lighting in AI 3D Lab, and can also be exported to Blender or Maya via Send to.
Tripo has released a tutorial demonstrating how to use the node-based Agentic Canvas in Tripo Studio to start from a single reference image, describe the goal in natural language, and build a cyberpunk 3D scene in about 20 minutes and export a UE5 project. In the workflow, the main scene uses the H3.1 model to retain richer geometric and surface detail, while props such as signs and trash cans use P2.0 (Smart Mesh) to make polygon counts easier to control. The Agent offers two modes, Control and
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Figma Weave in Detail: Turning Creative Processes into Reusable Workflows
Figma Weave integrates AI generation and editing tools into reusable workflows based on a node canvas, and adds a new way of working in parallel with Figma frames. Workflows support branching exploration and comparing results from the same prompt across multiple AI models, with built-in editing nodes for color correction, compositing, levels, cropping, trimming, and painting. Weave tools are now open for community publishing, so teammates can run packaged workflows directly without manipulating nodes.
Figma Weave integrates AI generation and editing tools into reusable workflows based on a node canvas, and adds a new way of working in parallel with Figma frames. Workflows support branching exploration and comparing results from the same prompt across multiple AI models, with built-in editing nodes for color correction, compositing, levels, cropping, trimming, and painting. Weave tools are now open for community publishing, so teammates can run packaged workflows directly without manipulating
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GitHub officially makes Copilot Fusion available at Universe 2026, and opens public beta of Copilot…
At GitHub Universe 2026, held October 28–29, 2026, GitHub announced that Copilot Fusion, which integrates automatic model selection with multi-model collaboration, is now generally available. It can be used in VS Code, the GitHub Copilot app, the CLI, and JetBrains IDEs, and it automatically determines which model to use and which of three execution modes—Single, Cascade, or Critique—to apply, based on three processing tiers: Standard, High, and Max. The desktop GitHub Copilot app is also beginning a public beta of Projects, which centralizes materials, repositories, sessions, and artifacts in one place for management. GitHub says Fusion Max scores 60 on the Artificial Analysis Coding Agent Index, within 2 points of Claude Code using Opus 5.5 Max, while its average API cost per task is about half and its time is about 40% less.
At GitHub Universe 2026, held October 28–29, 2026, GitHub announced that Copilot Fusion, which integrates automatic model selection with multi-model collaboration, is now generally available. It can be used in VS Code, the GitHub Copilot app, the CLI, and JetBrains IDEs, and it automatically determines which model to use and which of three execution modes—Single, Cascade, or Critique—to apply, based on three processing tiers: Standard, High, and Max. The desktop GitHub Copilot app is also beginn
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How Figma trained its design editing model: from SFT to RL
Figma Vice President of Engineering Josh Clemm described the training method for its design editing model, moving from supervised fine-tuning (SFT) to reinforcement learning (RL), and post-training an as-yet-unreleased editing model based on Qwen 3.8 27B. In early work, training Llama 3.3 70B on more than 250,000 samples was enough to compete with top models on editing tasks at the time. The Figma agent is now available to all users; in testing, typical tasks are about 20% faster, in expert comparisons it was better than or on par with the previous production agent in 62% of cases, user ratings were 33% higher, and undo operations were 60% fewer.
Figma Vice President of Engineering Josh Clemm described the training method for its design editing model, moving from supervised fine-tuning (SFT) to reinforcement learning (RL), and post-training an as-yet-unreleased editing model based on Qwen 3.8 27B. In early work, training Llama 3.3 70B on more than 250,000 samples was enough to compete with top models on editing tasks at the time. The Figma agent is now available to all users; in testing, typical tasks are about 20% faster, in expert comp
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Chaoyan Intelligence Open-Sources 35B Model Apex-35B-V1.0, with AI Participating in Its Own R&D
Beijing-based large model startup Chaoyan Intelligence has released and open-sourced its first large model, Apex-35B-V1.0, with 35 billion total parameters, approximately 3 billion parameters activated per token, and support for a 262,000-token context window, optimized through mid-training and post-training based on Qwen3.6-35B-A3B. The model scored 60.9 and 40.0 on the paper reproduction test PaperBench and the scientific research challenge test FrontierScience, respectively, higher than GLM-5.3's 42.5 and 30.7; it scored 84.0 on the tool-calling test BFCL V4, higher than DeepSeek-V4.1-Flash-1017's 75.0. The model has been released with open weights under the Apache 2.0 license, and the official release provides approximately 72GB of BF16 weights and GGUF quantized versions at various precisions.
Beijing-based large model startup Chaoyan Intelligence has released and open-sourced its first large model, Apex-35B-V1.0, with 35 billion total parameters, approximately 3 billion parameters activated per token, and support for a 262,000-token context window, optimized through mid-training and post-training based on Qwen3.6-35B-A3B. The model scored 60.9 and 40.0 on the paper reproduction test PaperBench and the scientific research challenge test FrontierScience, respectively, higher than GLM-5
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OpenAI Offers Up to $500 Monthly API Credit for ChatGPT Pro and Business
Starting October 28, 2026, OpenAI will provide eligible ChatGPT Pro and Business subscribers with up to $500 per month in API credits, usable for app and agent development. Pro's three monthly price tiers of $100, $200, and $500 correspond to credits of the same amount; Business accumulates at $20 per month per Standard seat and $100 per month per Premium seat, with a workspace-wide cap of $500. Annual Business plans receive up to $6,000 in credits for the full year at once, initially prorated based on the remaining days in the billing cycle, with unused portions expiring at the end of the period. These credits cannot be used to call Codex via API key, nor do they increase the usage limits of ChatGPT or the logged-in version of Codex; users must link an API organization in billing settings and enable it manually.
Starting October 28, 2026, OpenAI will provide eligible ChatGPT Pro and Business subscribers with up to $500 per month in API credits, usable for app and agent development. Pro's three monthly price tiers of $100, $200, and $500 correspond to credits of the same amount; Business accumulates at $20 per month per Standard seat and $100 per month per Premium seat, with a workspace-wide cap of $500. Annual Business plans receive up to $6,000 in credits for the full year at once, initially prorated b
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Anthropic S-1 shows roughly $850 billion in compute commitments over the next two decades
The Verge analyzed Anthropic's S-1 filing, noting that its non-cancelable purchase commitments, not-yet-commenced lease agreements, and new compute agreements total roughly $850 billion, and would approach $1 trillion if un finalized deals are included. The filing shows that revenue in the first half of 2026 grew 14-fold year over year to $16.3 billion, adjusted revenue was $1.62 billion, and net revenue retention was 900%. The author argues that these fixed costs require Anthropic to maintain rapid growth, and that the recursive self-improvement mentioned among its risk factors is precisely the key supporting this growth assumption.
The Verge analyzed Anthropic's S-1 filing, noting that its non-cancelable purchase commitments, not-yet-commenced lease agreements, and new compute agreements total roughly $850 billion, and would approach $1 trillion if un finalized deals are included. The filing shows that revenue in the first half of 2026 grew 14-fold year over year to $16.3 billion, adjusted revenue was $1.62 billion, and net revenue retention was 900%. The author argues that these fixed costs require Anthropic to maintain r
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