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Friday

  1. The New Stack

    Sam Altman once considered abandoning token-based pricing, but dropped the idea due to customer opposition

    OpenAI CEO Sam Altman revealed that the company had considered abandoning token-based billing for its next-generation models in favor of a model where developers submit a prompt and receive a single price directly, similar to AWS's billing model, but dropped the idea because customer feedback was extremely negative. Customers said they need to know what they are paying for, and would even rather accept inflated token counts and pay higher fees than give up itemized billing they can verify. Altman acknowledged that tokens are "a kind of fake unit" and not a good way to measure usage, but OpenAI still uses token-based pricing for now.

    y had considered abandoning token-based billing for its next-generation models in favor of a model where developers submit a prompt and receive a single price directly, similar to AWS's billing model, but dropped the idea because customer feedback was extremely negative. Customers said they need to know what they are paying for, and would even rather accept inflated token counts and pay higher fees than give up itemized billing they can verify. Altman acknowledged that tokens are "a kind of fake

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  2. Sina Tech

    Amazon's AI spending turns free cash flow negative at $42.4 billion, shares fall after hours

    Amazon disclosed that free cash flow for the trailing twelve months ended September 30, 2026 was negative $42.4 billion, compared with positive $14.8 billion in the same period of 2025; the company said this was mainly due to a $94.8 billion increase in purchases of property and equipment related to AI investments. Amazon expects cash capital expenditure of $220 billion for 2026, and AWS Chief Financial Officer John Felton said the order backlog as of the second quarter was close to $500 billion. In the earnings report, the advertising business's year-over-year growth rate fell to 12%, compared with 26% in the previous quarter.

    positive $14.8 billion in the same period of 2025; the company said this was mainly due to a $94.8 billion increase in purchases of property and equipment related to AI investments. Amazon expects cash capital expenditure of $220 billion for 2026, and AWS Chief Financial Officer John Felton said the order backlog as of the second quarter was close to $500 billion. In the earnings report, the advertising business's year-over-year growth rate fell to 12%, compared with 26% in the previous quarter.

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  3. Wallstreetcn

    Amazon's stock fell about 7% after its Q3 earnings report, showing AI investment pressure alongside Alphabet

    Amazon's total Q3 revenue grew 12% year-over-year to $201.9 billion, below analyst expectations, and its stock fell about 7% after hours following the earnings release. AWS revenue grew 39% year-over-year to $46 billion, accelerating for the sixth consecutive quarter, but online retail growth slowed to 2% and advertising growth slowed to 11%; meanwhile, equipment and property expenditures exceeded $77 billion, and free cash flow over the past 12 months was negative $42.4 billion. This trend echoes Alphabet's single-day plunge of 13% after its search advertising revenue fell short of expectations, as market concerns grow that AI investment is suppressing cash flow and the pace of monetization.

    Amazon's total Q3 revenue grew 12% year-over-year to $201.9 billion, below analyst expectations, and its stock fell about 7% after hours following the earnings release. AWS revenue grew 39% year-over-year to $46 billion, accelerating for the sixth consecutive quarter, but online retail growth slowed to 2% and advertising growth slowed to 11%; meanwhile, equipment and property expenditures exceeded $77 billion, and free cash flow over the past 12 months was negative $42.4 billion. This trend echo

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  4. AWS Machine Learning

    How MoEngage Used AgentCore Gateway to Turn Over 400 APIs into Agent Tools

    Using Amazon Bedrock AgentCore Gateway, MoEngage converted approximately 400 internal REST APIs distributed across private VPCs in 7 AWS Regions into permission-controlled agent tools, saving about 6 months of platform engineering effort and reducing new API onboarding from several days to a few hours. The solution requires no MCP server code from product teams, no per-integration network configuration, and involves no public endpoints anywhere in the path. MoEngage also established a principle: engineering teams only maintain OpenAPI specs, while the AI platform team is solely responsible for the MCP layer.

    AgentCore Gateway architecture: agent services reach the gateway through a private VPC interface endpoint, which evaluates Cedar policies and routes calls over Amazon VPC Lattice to internal APIs

    Using Amazon Bedrock AgentCore Gateway, MoEngage converted approximately 400 internal REST APIs distributed across private VPCs in 7 AWS Regions into permission-controlled agent tools, saving about 6 months of platform engineering effort and reducing new API onboarding from several days to a few hours. The solution requires no MCP server code from product teams, no per-integration network configuration, and involves no public endpoints anywhere in the path. MoEngage also established a principle:

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  5. AWS Machine Learning

    Building a Repeatable PHP-to-Java Migration Engine with Kiro

    AWS describes building a repeatable PHP-to-Java migration engine with Kiro: the migration methodology and discovery scanner are encapsulated as skill files, using an eight-phase process and a parity harness to verify that the Java port behaves consistently with the original PHP application. The same set of skill files can be used for interactive, application-by-application migration in Kiro, or handed to AWS Transform to run as parallel tasks at portfolio scale. Running requires PHP 8.0 or later, and the generated Spring Boot project requires Java 21 and Maven.

    Workspace architecture: one shared brain, many projects. Diagram of the workspace layout. At the top is the Kiro workspace root, PHP-Java-Migration. Below it, a shared, global layer under .kiro/ holds the skills (the methodology) and the steering file (the governing workflow), and applies to every project. Beneath that, three per-project folders each hold their own spec sub-folder, and the diagram notes that improving the shared layer once benefits every project.

    AWS describes building a repeatable PHP-to-Java migration engine with Kiro: the migration methodology and discovery scanner are encapsulated as skill files, using an eight-phase process and a parity harness to verify that the Java port behaves consistently with the original PHP application. The same set of skill files can be used for interactive, application-by-application migration in Kiro, or handed to AWS Transform to run as parallel tasks at portfolio scale. Running requires PHP 8.0 or later

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  6. AWS Machine Learning

    Epsilon Builds Intent-Driven UI Testing with Amazon Nova Act

    Epsilon's Agentic Platform team used Amazon Nova Act together with Amazon Bedrock AgentCore Browser to turn intent-driven UI testing into a multi-tenant platform service layered on top of its existing Playwright regression suite. Test steps are written in natural language, executed by Nova Act over a CDP WebSocket, with per-step screenshots and pass-rate statistics, and results stored in Amazon S3. The solution provides managed temporary browser sessions, AWS IAM-based authentication, and cross-account AssumeRole isolation, and the article also presents an 18-step test workflow.

    Epsilon UI Testing Service architecture with user, authentication, API, execution runner, and Amazon S3 storage layers

    ntCore Browser to turn intent-driven UI testing into a multi-tenant platform service layered on top of its existing Playwright regression suite. Test steps are written in natural language, executed by Nova Act over a CDP WebSocket, with per-step screenshots and pass-rate statistics, and results stored in Amazon S3. The solution provides managed temporary browser sessions, AWS IAM-based authentication, and cross-account AssumeRole isolation, and the article also presents an 18-step test workflow.

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Thursday

  1. AWS Machine Learning

    Implementing agentic QA automation with Amazon Nova Act to accelerate software delivery (Part 3)

    The AWS blog introduces two new capabilities added to QA Studio based on Amazon Nova Act: applications organize test cases and test suites into application containers with owners, record CloudWatch metrics by application and environment, and provide a 0–100 health score (starting from the pass rate and deducting points for flaky tests), supporting two trend windows of 7 days/hourly and 30 days/daily. Team-based user management defines five cumulative roles: Admin, Team Lead, Editor, Executor, and Viewer; each application belongs to only one team, and permissions are managed by application rather than by individual test.

    QA Studio application detail page showing pass rate, executions, and failures above the use cases and test suites tabs

    The AWS blog introduces two new capabilities added to QA Studio based on Amazon Nova Act: applications organize test cases and test suites into application containers with owners, record CloudWatch metrics by application and environment, and provide a 0–100 health score (starting from the pass rate and deducting points for flaky tests), supporting two trend windows of 7 days/hourly and 30 days/daily. Team-based user management defines five cumulative roles: Admin, Team Lead, Editor, Executor, an

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  2. AWS Machine Learning

    Distributed Training on EKS with Ray Train DLC and EFA

    AWS has released the Ray Train DLC image ray:train-ml-cuda-v1.1, which comes pre-integrated with Ray 2.58.0, PyTorch 2.13.0, CUDA 13.0.2, NCCL 2.29.7, and EFA along with the AWS OFI NCCL plugin, enabling distributed training on EKS without the need to build images yourself. The example uses PyTorch FSDP to fine-tune Qwen2.5-1.5B on 8 NVIDIA L4 GPUs across two g6.12xlarge instances, with cross-node communication going directly over EFA. The image is publicly hosted on Amazon ECR Public, so nodes can pull it without credentials.

    EKS cluster with a CPU system node running KubeRay and the Ray head, and two EFA-enabled g6.12xlarge GPU worker nodes in one Availability Zone

    AWS has released the Ray Train DLC image ray:train-ml-cuda-v1.1, which comes pre-integrated with Ray 2.58.0, PyTorch 2.13.0, CUDA 13.0.2, NCCL 2.29.7, and EFA along with the AWS OFI NCCL plugin, enabling distributed training on EKS without the need to build images yourself. The example uses PyTorch FSDP to fine-tune Qwen2.5-1.5B on 8 NVIDIA L4 GPUs across two g6.12xlarge instances, with cross-node communication going directly over EFA. The image is publicly hosted on Amazon ECR Public, so nodes

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  3. AWS Machine Learning

    Building an Enterprise-Grade Agentic AI Platform on Amazon Bedrock AgentCore

    AWS has released an enterprise-grade Agentic AI platform blueprint based on Amazon Bedrock and AgentCore, adopting a federated operating model in which a central Platform team provides governed shared services while each Workstream team retains ownership of its own agents, tools, and data. The platform enforces model admission, regional restrictions, rate and token quotas, and budget controls through an independent inference gateway and an MCP tool gateway, and uses an evaluation pipeline to perform gate checks before agents go live.

    Operating model linking the governance foundation, Platform control plane, and team-owned Workstream cells

    AWS has released an enterprise-grade Agentic AI platform blueprint based on Amazon Bedrock and AgentCore, adopting a federated operating model in which a central Platform team provides governed shared services while each Workstream team retains ownership of its own agents, tools, and data. The platform enforces model admission, regional restrictions, rate and token quotas, and budget controls through an independent inference gateway and an MCP tool gateway, and uses an evaluation pipeline to per

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Wednesday

  1. heise onlineSelected

    Anthropic Prepares for IPO: Prospectus Discloses Compute Commitments and Security Risks

    Anthropic is preparing for an IPO, with a valuation estimated at up to $2 trillion, and the prospectus published this Monday disclosed details on security, customer dependence, and corporate governance. In the first nine months of 2026, about 5% to 7% of revenue came from customers found to have violated usage policies; the top five customers contributed about 25% of revenue, and in the first half of 2026, 48% of revenue was obtained through platforms such as AWS, Google Cloud, and Microsoft Azure. Existing and planned AI infrastructure agreements total about $657 billion, of which about $417 billion is non-cancelable, and spending could rise to $806 billion after including hardware capacity still under negotiation. After listing, the seven founders will collectively control 50.1% of ordinary shareholder voting rights. The specific listing date has not yet been determined; according to The Wall Street Journal, the target is the week of November 16.

    ound to have violated usage policies; the top five customers contributed about 25% of revenue, and in the first half of 2026, 48% of revenue was obtained through platforms such as AWS, Google Cloud, and Microsoft Azure. Existing and planned AI infrastructure agreements total about $657 billion, of which about $417 billion is non-cancelable, and spending could rise to $806 billion after including hardware capacity still under negotiation. After listing, the seven founders will collectively contro

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Monday

  1. Sakana AI:Blog

    How Sakana AI Is Taking Japan's Homegrown AI Products Global: An Interview with the VP of GTM

    In 2026, Sakana AI launched products including Sakana Chat, Sakana Marlin, Sakana Fugu, and Sakana Translate. Its GTM team was formally established in September 2026, led by Vice President Hidekazu Kakishima. The team adopts a "product-led GTM" model and hires Forward Deployed Engineers (FDEs) to feed customer on-site experience back into product improvements. Kakishima previously served as head of the strategic accounts team at AWS Japan, a founding member of SORACOM, and in a related role at Skydio.

    6, Sakana AI launched products including Sakana Chat, Sakana Marlin, Sakana Fugu, and Sakana Translate. Its GTM team was formally established in September 2026, led by Vice President Hidekazu Kakishima. The team adopts a "product-led GTM" model and hires Forward Deployed Engineers (FDEs) to feed customer on-site experience back into product improvements. Kakishima previously served as head of the strategic accounts team at AWS Japan, a founding member of SORACOM, and in a related role at Skydio.

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Tuesday

  1. AWS Machine Learning

    Building Agentic Retrieval on a Managed Amazon Bedrock Knowledge Base with CDK

    AWS released a solution that uses AWS CDK to deploy enterprise-grade agentic retrieval on a managed Amazon Bedrock Knowledge Base: the reasoning agent invokes two knowledge bases as MCP tools through AgentCore Gateway, and AgenticRetrieveStream performs multi-turn planning, internal reranking, and returns answers with citations. The managed knowledge base (Type: MANAGED) takes over ingestion, storage, indexing, and retrieval, eliminating the need to build your own OpenSearch Serverless collection and index. The solution also provisions two CloudWatch dashboards covering seven layers of telemetry, and provides both on-demand and continuous evaluation, deployable with a single command.

    Agent routing a question across two managed knowledge bases through the Gateway, with every step landing in Amazon CloudWatch

    AWS released a solution that uses AWS CDK to deploy enterprise-grade agentic retrieval on a managed Amazon Bedrock Knowledge Base: the reasoning agent invokes two knowledge bases as MCP tools through AgentCore Gateway, and AgenticRetrieveStream performs multi-turn planning, internal reranking, and returns answers with citations. The managed knowledge base (Type: MANAGED) takes over ingestion, storage, indexing, and retrieval, eliminating the need to build your own OpenSearch Serverless collectio

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  2. AWS Machine Learning

    Implementing fleet-level computer vision lifecycle management at the edge with SageMaker SDK v3

    AWS has released a fleet-level edge computer vision model lifecycle solution based on the SageMaker Python SDK v3, using SageMaker Neo to replace the deprecated SageMaker Edge Manager for edge optimization. The solution uses a dual-branch SageMaker Pipeline to train an anomaly detector and a ResNet18 defect classifier for each camera; after passing quality gates, they are compiled and registered to the Model Registry, with Step Functions controlling the fleet training cadence. Running the full pipeline once on synthetic data costs about $0.07 in compute.

    Solution architecture: SQS FIFO queue triggers AWS Lambda, which starts a Step Functions state machine with an Amazon DynamoDB semaphore for concurrency control. The state machine launches a 13-step SageMaker Pipeline with parallel entry steps (Preprocess, Fetch Latest Anomaly, Fetch Latest Classifier), dual training branches with ONNX evaluation, quality gates, Neo compilation, and Model Registry registration. Compiled artifacts land in S3. Completions and failures converge on an SNS topic.

    AWS has released a fleet-level edge computer vision model lifecycle solution based on the SageMaker Python SDK v3, using SageMaker Neo to replace the deprecated SageMaker Edge Manager for edge optimization. The solution uses a dual-branch SageMaker Pipeline to train an anomaly detector and a ResNet18 defect classifier for each camera; after passing quality gates, they are compiled and registered to the Model Registry, with Step Functions controlling the fleet training cadence. Running the full p

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Saturday

  1. cnBeta

    Large numbers of Amazon NVIDIA GB300 servers temporarily idle due to insufficient power supply

    Amazon AWS at one point expected to receive NVIDIA GB300 server racks worth about $5 billion, but after some equipment was delivered, it was temporarily stored in warehouses or transit facilities because data centers lacked sufficient power capacity; the industry calls such equipment dark GPUs. AWS also plans to continue purchasing at a rate of about $3 billion per month, and has shifted to accepting racks early and paying early to lock in scarce hardware. Amazon plans to invest about $220 billion in capital expenditure this year for AI and cloud computing infrastructure, and free cash flow has turned negative for the first time in recent years.

    Amazon AWS at one point expected to receive NVIDIA GB300 server racks worth about $5 billion, but after some equipment was delivered, it was temporarily stored in warehouses or transit facilities because data centers lacked sufficient power capacity; the industry calls such equipment dark GPUs. AWS also plans to continue purchasing at a rate of about $3 billion per month, and has shifted to accepting racks early and paying early to lock in scarce hardware. Amazon plans to invest about $220 billi

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  2. TechSpot

    Amazon stockpiles NVIDIA GB300 servers but has no power to run them, exposing the power bottleneck of AI infrastructure

    According to internal emails seen by Business Insider, Amazon receives NVIDIA server racks worth billions of dollars each month; in August alone AWS expects to receive about $5 billion worth of GB300 racks, and thereafter about $3 billion more each month, but some US AWS sites lack sufficient power, so this hardware can only be temporarily stored in transit warehouses. This phenomenon, known as dark GPUs, is putting financial pressure on data centers; Amazon's free cash flow turned negative in the second quarter for the first time since 2023, and capital expenditure this year is expected to be about $220 billion.

    According to internal emails seen by Business Insider, Amazon receives NVIDIA server racks worth billions of dollars each month; in August alone AWS expects to receive about $5 billion worth of GB300 racks, and thereafter about $3 billion more each month, but some US AWS sites lack sufficient power, so this hardware can only be temporarily stored in transit warehouses. This phenomenon, known as dark GPUs, is putting financial pressure on data centers; Amazon's free cash flow turned negative in t

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  3. Huxiu

    Amazon CEO Jassy on $220 Billion in Capital Expenditure: Compute Is Still in Short Supply, Inference Costs Need to Fall by Another Order of Magnitude

    Amazon CEO Andy Jassy said in an interview on the a16z podcast that the company's capital expenditure is expected to reach $220 billion in 2026, but compute capacity is already sold out for 2026, essentially fully booked for 2027, and a large share of 2028 quotas has been locked in. He described enterprise AI demand as dumbbell-shaped: a large number of production systems in the middle have yet to be deeply integrated because inference costs are too high, and costs need to fall by another order of magnitude before an explosion in adoption occurs. Jassy also noted that 99% of AWS's top 1,000 customers use Graviton, which offers about 40% better price-performance than x86, while Trainium has a 30% to 40% advantage over comparable hardware.

    e of 2028 quotas has been locked in. He described enterprise AI demand as dumbbell-shaped: a large number of production systems in the middle have yet to be deeply integrated because inference costs are too high, and costs need to fall by another order of magnitude before an explosion in adoption occurs. Jassy also noted that 99% of AWS's top 1,000 customers use Graviton, which offers about 40% better price-performance than x86, while Trainium has a 30% to 40% advantage over comparable hardware.

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  4. 36Kr · AI

    Orange Cat Universe completes tens of millions Pre-A round financing, Gao Bingqiang invests in three consecutive rounds

    Pet smart hardware company Orange Cat Universe recently completed a Pre-A round financing of tens of millions of yuan, with Professor Gao Bingqiang and his Gaofeng Patience Fund, Qianhai Innovation Investment, and Changlei Venture Capital continuing to add investment; the funds will be used for technological innovation, product iteration, patent system development, and global offline channel expansion. The first product, PawSwing Neo, is a powerless pure-mechanical cat bed grooming collector, priced at about 200 US dollars, with a return rate of less than 3% and grooming efficiency of 3 to 4 milligrams per joule. In 2026, the company has entered Europe's largest pet specialty retail chain channel, with more than 3,000 offline stores stocking its products.

    ing to add investment; the funds will be used for technological innovation, product iteration, patent system development, and global offline channel expansion. The first product, PawSwing Neo, is a powerless pure-mechanical cat bed grooming collector, priced at about 200 US dollars, with a return rate of less than 3% and grooming efficiency of 3 to 4 milligrams per joule. In 2026, the company has entered Europe's largest pet specialty retail chain channel, with more than 3,000 offline stores sto

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  5. gihyo.jp

    AI accelerates cyberattacks; Anthropic, OpenAI, Microsoft and others disclose defense and agent control developments

    gihyo.jp reviews multiple releases and reports from late October, organizing AI-era defense challenges along two lines: vulnerability remediation implementation and permission management. Anthropic Deputy CISO Jason D. Clinton says that within about 18 months, high cyber capability will spread to small, cheap models, but fixes still need to be applied to running systems. The article cites OpenAI's discovery of Brotli's CVE-2026-111497 during testing in an isolated environment, and introduces remediation and credential control solutions such as AWS Continuum, Google Agent Compute Engine, and 1Password for Agents, as well as Microsoft's report on Storm-0940 using AI to automate attacks.

    opic Deputy CISO Jason D. Clinton says that within about 18 months, high cyber capability will spread to small, cheap models, but fixes still need to be applied to running systems. The article cites OpenAI's discovery of Brotli's CVE-2026-111497 during testing in an isolated environment, and introduces remediation and credential control solutions such as AWS Continuum, Google Agent Compute Engine, and 1Password for Agents, as well as Microsoft's report on Storm-0940 using AI to automate attacks.

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  6. AWS Machine Learning

    Amazon SageMaker AI Multi-Turn Reinforcement Learning Practical Guide: Training gpt-oss-20b with a Wordle Example

    AWS has published a practical guide for multi-turn reinforcement learning (MTRL) on Amazon SageMaker AI. Using Wordle as an example, it deploys the agent and reward function to Amazon Bedrock AgentCore, and the training job updates LoRA adapters via GRPO. In the example, gpt-oss-20b at default reasoning effort improved its solve rate on held-out words from 61% to 84%, at a training token cost of about $339 and an AgentCore runtime cost of about $79. The article also details failures such as two inverted reward design errors, token invalidation under concurrency, and tool calls degrading into plain text, and recommends evaluating under the decoding settings used in final serving.

    SageMaker AI training job connected to the AgentCore-hosted agent, Wordle environment, and reward function

    AWS has published a practical guide for multi-turn reinforcement learning (MTRL) on Amazon SageMaker AI. Using Wordle as an example, it deploys the agent and reward function to Amazon Bedrock AgentCore, and the training job updates LoRA adapters via GRPO. In the example, gpt-oss-20b at default reasoning effort improved its solve rate on held-out words from 61% to 84%, at a training token cost of about $339 and an AgentCore runtime cost of about $79. The article also details failures such as two

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Friday

  1. Databricks Blog

    Databricks vs. Oracle 2026 Comparison: Why Open Lakehouse Architecture Replaces Traditional Data Warehouses

    In its 2026 comparison article, Databricks argues that its open lakehouse architecture stores 100% of customer data with Delta Lake and Apache Iceberg, unifying BI, ETL, stream processing, ML and AI, whereas Oracle uses proprietary formats and relies on PL/SQL and Exadata. Databricks separates storage and compute; SQL Serverless warehouses cost nothing when idle and are billed per second; Oracle bills by ECPU and autoscales up to 3x base capacity, making costs harder to predict. Databricks also offers Unity Catalog unified governance, Genie natural language analytics and OpenSharing cross-platform sharing, supporting AWS, Azure and Google Cloud.

    nifying BI, ETL, stream processing, ML and AI, whereas Oracle uses proprietary formats and relies on PL/SQL and Exadata. Databricks separates storage and compute; SQL Serverless warehouses cost nothing when idle and are billed per second; Oracle bills by ECPU and autoscales up to 3x base capacity, making costs harder to predict. Databricks also offers Unity Catalog unified governance, Genie natural language analytics and OpenSharing cross-platform sharing, supporting AWS, Azure and Google Cloud.

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