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How Amazon Bedrock AgentCore Evaluations connects decision models through code evaluators
Amazon Bedrock AgentCore Evaluations can connect decision models through code evaluators, converting evaluation inputs into decision model requests and then converting probabilities back into evaluation results. The example connects TypeSafe-hosted Jev and the open-source Strands Decider 2B (2 billion parameters), supports three types of questions—noul, choice, and score—and returns calibrated confidence scores that can be thresholded. AgentCore Evaluations supports 15 built-in evaluators; decision models are suitable as a filtering layer and work in conjunction with LLM-as-a-judge.
Amazon Bedrock AgentCore Evaluations can connect decision models through code evaluators, converting evaluation inputs into decision model requests and then converting probabilities back into evaluation results. The example connects TypeSafe-hosted Jev and the open-source Strands Decider 2B (2 billion parameters), supports three types of questions—noul, choice, and score—and returns calibrated confidence scores that can be thresholded. AgentCore Evaluations supports 15 built-in evaluators; decis
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How Figma Manages Sandboxes at Scale: An Analysis of the Nimbus Platform
Figma built a centralized sandbox management platform called Nimbus. Through namespace configuration files and three APIs—GetOrCreate, Get, and Delete—engineers can create, reuse, and delete sandboxes without needing to understand cloud provider details. Nimbus uses a unified base image, sandbox-base, and a daemon, sandboxd, to manage processes, networking, and health checks. Its goal is to return a fully configured sandbox within 1 second and to load-balance across multiple cloud providers to handle failures.
Figma built a centralized sandbox management platform called Nimbus. Through namespace configuration files and three APIs—GetOrCreate, Get, and Delete—engineers can create, reuse, and delete sandboxes without needing to understand cloud provider details. Nimbus uses a unified base image, sandbox-base, and a daemon, sandboxd, to manage processes, networking, and health checks. Its goal is to return a fully configured sandbox within 1 second and to load-balance across multiple cloud providers to h
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How U Mobile Built Agentic Telecom Operations with Amazon Bedrock AgentCore
U Mobile built the AI-Driven Agentic Helpdesk (AiDaH) on Amazon Bedrock AgentCore, covering more than 150 operational scenarios across 6 business units, with internal measurements showing fault ticket volumes in key categories dropping by up to 84.5%. The platform uses a six-layer architecture, is accessed through Microsoft Teams, and has 8 specialized agents collaborating via the A2A protocol; about 70% of requests are handled directly by deterministic keyword matching without invoking a model. AiDaH launched in Q3 2026 for 50 L2 call center and store users.
U Mobile built the AI-Driven Agentic Helpdesk (AiDaH) on Amazon Bedrock AgentCore, covering more than 150 operational scenarios across 6 business units, with internal measurements showing fault ticket volumes in key categories dropping by up to 84.5%. The platform uses a six-layer architecture, is accessed through Microsoft Teams, and has 8 specialized agents collaborating via the A2A protocol; about 70% of requests are handled directly by deterministic keyword matching without invoking a model.
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Cohere Retrieval Models Embed 5 and Rerank 5 Now Available on OpenRouter
Cohere's Embed 5 and Rerank 5 retrieval models are now available on OpenRouter, accessible through a single API call with unified billing and no additional SDK required. Embed 5 Pro and Fast share the same embedding space, so you can index with Pro and query with Fast to reduce latency; Rerank 5's scores are calibrated, allowing the same relevance threshold to filter weak context across queries and domains. Embed 5 supports 256 to 2048-dimensional Matryoshka vectors, defaults to 2048 dimensions, and handles up to 128k tokens per request.
Cohere's Embed 5 and Rerank 5 retrieval models are now available on OpenRouter, accessible through a single API call with unified billing and no additional SDK required. Embed 5 Pro and Fast share the same embedding space, so you can index with Pro and query with Fast to reduce latency; Rerank 5's scores are calibrated, allowing the same relevance threshold to filter weak context across queries and domains. Embed 5 supports 256 to 2048-dimensional Matryoshka vectors, defaults to 2048 dimensions,
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Cohere Model Vault Launches Third-Party Open Models, Supporting DeepSeek V4.1…
Cohere has launched third-party open models in Model Vault and Model Vault Encrypted, allowing enterprises to run both Cohere's own models and open models such as DeepSeek V4.1 Flash, GLM 5.3 Flash, Gemma 4, GPT-OSS, and Phi on a fully managed encrypted inference platform. Model Vault applies zero data retention to prompts and outputs by default, while the Encrypted version uses confidential computing so that neither Cohere nor cloud providers can see the data. Model Vault is now available in the United States, the European Union, and Canada; the Encrypted version is currently limited to the United States, with support for the European Union and Canada coming soon.
Cohere has launched third-party open models in Model Vault and Model Vault Encrypted, allowing enterprises to run both Cohere's own models and open models such as DeepSeek V4.1 Flash, GLM 5.3 Flash, Gemma 4, GPT-OSS, and Phi on a fully managed encrypted inference platform. Model Vault applies zero data retention to prompts and outputs by default, while the Encrypted version uses confidential computing so that neither Cohere nor cloud providers can see the data. Model Vault is now available in th
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ServiceNow implements on-behalf-of (OBO) user… with Bedrock AgentCore Gateway
Amazon Bedrock AgentCore Gateway can be configured for on-behalf-of (OBO) token exchange, allowing AI agents to preserve the identity of the initiating user when calling ServiceNow REST APIs. The gateway exchanges the JWT held by the agent for a new token intended for the ServiceNow API connector; Entra ID verifies that the user has consented to delegation before issuing it, and ServiceNow applies that user's permissions based on the preferred_username claim. This pattern also applies to other OAuth-protected REST APIs.
Amazon Bedrock AgentCore Gateway can be configured for on-behalf-of (OBO) token exchange, allowing AI agents to preserve the identity of the initiating user when calling ServiceNow REST APIs. The gateway exchanges the JWT held by the agent for a new token intended for the ServiceNow API connector; Entra ID verifies that the user has consented to delegation before issuing it, and ServiceNow applies that user's permissions based on the preferred username claim. This pattern also applies to other O
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How Amazon Quick integrates structured and unstructured data
Amazon Quick uses the Space workspace to combine unstructured documents in an S3 knowledge base with structured datasets that have semantic descriptions, enabling a single conversational AI agent to automatically determine which type of data to query in a single question and return a merged answer with citations. This solution requires users to have an Enterprise subscription, the S3 bucket must be in the same region as Amazon Quick, and administrators can grant read-only permissions in the console.
Amazon Quick uses the Space workspace to combine unstructured documents in an S3 knowledge base with structured datasets that have semantic descriptions, enabling a single conversational AI agent to automatically determine which type of data to query in a single question and return a merged answer with citations. This solution requires users to have an Enterprise subscription, the S3 bucket must be in the same region as Amazon Quick, and administrators can grant read-only permissions in the cons
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Amazon Bedrock Launches In-India Inference for Kimi K3
In addition to its existing global and US-based inference options, Amazon Bedrock has added an in-India inference option: when calling in.moonshotai.kimi-k3, requests are processed only in the Mumbai and Hyderabad Regions, meeting strict data residency requirements. Ultrahuman already uses this model in production, leveraging prompt caching to reduce LLM costs by 80% and speed up multi-turn conversations by 85%. The article also provides sample OCR code that uses Kimi K3 to directly read scanned Hindi documents and ask follow-up questions in Hinglish.
In addition to its existing global and US-based inference options, Amazon Bedrock has added an in-India inference option: when calling in.moonshotai.kimi-k3, requests are processed only in the Mumbai and Hyderabad Regions, meeting strict data residency requirements. Ultrahuman already uses this model in production, leveraging prompt caching to reduce LLM costs by 80% and speed up multi-turn conversations by 85%. The article also provides sample OCR code that uses Kimi K3 to directly read scanned
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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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Browser Use vs. Kernel: Pricing, Stealth, and Performance
Browser Use and Kernel are both cloud browser automation services. Browser Use's headless browser costs $0.02/hour with no subscription required, while Kernel costs about $0.06/hour and is billed by the second. In Stealth Bench V2 on September 25, 2026, Browser Use achieved a 90.5% success rate accessing 100 protected sites, compared with 80.5% for Kernel. Browser Use's hosted proxy traffic costs $5/GB, while Kernel's proxy traffic is not billed but is subject to fair use limits.
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Lu Chuanying: The Diffusion of Global AI Governance Issues, the Institutional Framework, and China's Role
Lu Chuanying, a distinguished expert at the Center for Strategic and Security Studies at Tsinghua University, pointed out that global AI governance issues are extending from behavioral norms toward both the technology and industry ends; as of October 2026, the OECD Policy Navigator had catalogued 2,568 policy initiatives, yet governance fragmentation is intensifying. He analyzed that the diffusion of issues is jointly driven by the consensus dilemma over behavioral norms, the endogenous nature of technological risks, and the strategization of the conditions for forming technological capabilities. The current framework consists of existing multilateral mechanisms such as the United Nations, G20, and APEC, specialized mechanisms such as the Bletchley Summit, and comprehensive mechanisms such as "Silicon Peace." China's approach is supported by placing equal emphasis on development and security, multilateral institutional coordination, and capacity building in the Global South.
Lu Chuanying, a distinguished expert at the Center for Strategic and Security Studies at Tsinghua University, pointed out that global AI governance issues are extending from behavioral norms toward both the technology and industry ends; as of October 2026, the OECD Policy Navigator had catalogued 2,568 policy initiatives, yet governance fragmentation is intensifying. He analyzed that the diffusion of issues is jointly driven by the consensus dilemma over behavioral norms, the endogenous nature o
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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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NVIDIA Open-Sources NVIDIA Recovery to Automatically Recover Distributed Training Jobs on Clusters
NVIDIA has released NVIDIA Recovery, an open-source workload-level recovery service that detects training interruptions through application logs, GPU metrics, and scheduler signals, and handles them in a four-stage loop: detection, attribution, isolation, and restart or stop. The service is available under the Apache 2.0 license in the dsx-ai-factory/NVIDIA-Recovery repository. Slurm integration is implemented through an Advisor Controller deployed via a Helm chart, supporting both in-cluster and SSH modes. New deployments default to monitoring mode, which only outputs recovery recommendations without executing scheduling actions; from July 9 to October 9, monitoring on one training cluster covered nearly 200 jobs per day and recorded approximately 8,550 anomaly detections, with average detection and recovery action latencies of 69 seconds and 94 seconds, respectively.
NVIDIA has released NVIDIA Recovery, an open-source workload-level recovery service that detects training interruptions through application logs, GPU metrics, and scheduler signals, and handles them in a four-stage loop: detection, attribution, isolation, and restart or stop. The service is available under the Apache 2.0 license in the dsx-ai-factory/NVIDIA-Recovery repository. Slurm integration is implemented through an Advisor Controller deployed via a Helm chart, supporting both in-cluster an
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How Worldwide Express Automated Freight Exceptions with Amazon Bedrock
Worldwide Express built a multi-agent AI solution based on Amazon Bedrock and integrated it into its transportation management system SpeedShip. Six months after launch, active exceptions dropped by 75%, the collection cycle was shortened by 4 weeks, annual savings reached $1 million, and $10 million in free cash flow was released at once. The system dispatches invoice exceptions to three specialized agents—invoice matching, price discrepancy, and surcharge validation. Each agent first retrieves the corresponding carrier's billing rules and dispute procedures before making recommendations, and the same set of agents can be used universally without carrier-specific code.
Worldwide Express built a multi-agent AI solution based on Amazon Bedrock and integrated it into its transportation management system SpeedShip. Six months after launch, active exceptions dropped by 75%, the collection cycle was shortened by 4 weeks, annual savings reached $1 million, and $10 million in free cash flow was released at once. The system dispatches invoice exceptions to three specialized agents—invoice matching, price discrepancy, and surcharge validation. Each agent first retrieves
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Cohere Advances CAD 4 Billion AI Investment in Canada, Will Create 1,500+ Jobs
Cohere has announced progress on its investment plan in Canada, expecting to invest more than CAD 4 billion in R&D and AI infrastructure by 2031 and create more than 1,500 jobs, of which about 1,000 will be technical roles in AI research, engineering, and product development. The plan began in 2024 and will also provide more than 250 paid student positions by 2029, along with research partnerships with the University of Toronto, the University of Waterloo, and others. Cohere has reached a CAD 309 million GPU cloud partnership with Bell AI Fabric to run its models on Canadian sovereign infrastructure.
Cohere has announced progress on its investment plan in Canada, expecting to invest more than CAD 4 billion in R&D and AI infrastructure by 2031 and create more than 1,500 jobs, of which about 1,000 will be technical roles in AI research, engineering, and product development. The plan began in 2024 and will also provide more than 250 paid student positions by 2029, along with research partnerships with the University of Toronto, the University of Waterloo, and others. Cohere has reached a CAD 30
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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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Cognizant Forms 25,000-Person OpenAI Frontier Capability Team
On October 30, Cognizant announced the establishment of the OpenAI Frontier Capability Group, bringing together more than 25,000 professionals dedicated to delivering OpenAI products such as Codex and ChatGPT to clients. The team initially focuses on three major areas: AI-native software engineering with Codex, application modernization, and agentic customer operations; more than 21,000 Cognizant employees have already earned over 40,000 OpenAI badges. An OpenAI enterprise assistant co-built for a global biopharmaceutical client has handled more than 5 million conversations, cutting content creation time by 60%.
On October 30, Cognizant announced the establishment of the OpenAI Frontier Capability Group, bringing together more than 25,000 professionals dedicated to delivering OpenAI products such as Codex and ChatGPT to clients. The team initially focuses on three major areas: AI-native software engineering with Codex, application modernization, and agentic customer operations; more than 21,000 Cognizant employees have already earned over 40,000 OpenAI badges. An OpenAI enterprise assistant co-built for
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Latent Space announces AIE CODE coding agent conference curation themes
Latent Space host swyx introduces the curation approach for the annual flagship conference AIE CODE, which is single-track, invitation-only, and aimed at about 1,000 agent engineers. This year's three themes are Scaling without Slop, System One models, and Increasing Ambition, among which Jarred Sumner will share Bun's rewrite in Rust and a Bun AOT preview. The article also rounds up that day's AI news, including Mistral's open-weight release and OpenAI's frontier model monitoring.
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Anthropic files S-1 to list on Nasdaq under the ticker ANT, with a valuation of up to $2 trillion
Anthropic filed its S-1 on October 26, planning to list on Nasdaq under the ticker ANT with a valuation of $1.5 trillion to $2 trillion; if completed, it would surpass SpaceX to become the largest IPO in history. The prospectus shows revenue of $16.286 billion in the first half of 2026, up nearly 13-fold year on year, with about 80% of revenue coming from enterprise APIs and nearly 12,000 customers each spending more than $100,000 annually. The company has signed long-term compute contracts worth more than $650 billion, about 80% of which are non-cancelable; Q3 revenue was about $16.9 billion, and the quarter-on-quarter growth rate fell from 144% to about 47%, with some investors discussing cutting the valuation to about $1.5 trillion.
Anthropic filed its S-1 on October 26, planning to list on Nasdaq under the ticker ANT with a valuation of $1.5 trillion to $2 trillion; if completed, it would surpass SpaceX to become the largest IPO in history. The prospectus shows revenue of $16.286 billion in the first half of 2026, up nearly 13-fold year on year, with about 80% of revenue coming from enterprise APIs and nearly 12,000 customers each spending more than $100,000 annually. The company has signed long-term compute contracts wort
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