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.
According to The Information, OpenAI won over the U.S. AI coding platform Replit with model price discounts of more than 30%, and the contract is worth over $100 million annually. OpenAI also sought to win over longtime Anthropic customers such as Rippling and Atomicwork through methods including free credits, doubled accounts, and engineers joining group chats for support, while Anthropic lowered API prices when it released new models.
In the risk disclosures of its S-1/A prospectus supplement updated on Monday, Anthropic stated that the company may soon reach the state of recursive self-improvement as defined in its Responsible Scaling Policy. The filing said the company has already used AI to develop new models and achieve some capability jumps, but the team did not notice at the time, and may in the future only notice after an acceleration in R&D has already occurred. Anthropic also stated that none of its existing models have crossed this threshold, and committed to retaining human involvement in frontier model R&D for the foreseeable future, and is considering revising its Responsible Scaling Policy.
In the risk disclosures of its S-1/A prospectus supplement updated on Monday, Anthropic stated that the company may soon reach the state of recursive self-improvement as defined in its Responsible Scaling Policy. The filing said the company has already used AI to develop new models and achieve some capability jumps, but the team did not notice at the time, and may in the future only notice after an acceleration in R&D has already occurred. Anthropic also stated that none of its existing models h
In internal guidance updated in October, Goldman Sachs tightened access to Claude Code and Microsoft GitHub-related tools, mainly restricting them to use by engineers, which has drawn complaints from some traders. Previously, some traders relied on these tools to monitor client trading flows and simulate market and position changes; "strats" employees whose work spans trading, quantitative research and software engineering are not subject to this restriction. Goldman Sachs' own AI assistant, GS AI Assistant, integrates models from OpenAI, Anthropic and Alphabet.
In internal guidance updated in October, Goldman Sachs tightened access to Claude Code and Microsoft GitHub-related tools, mainly restricting them to use by engineers, which has drawn complaints from some traders. Previously, some traders relied on these tools to monitor client trading flows and simulate market and position changes; "strats" employees whose work spans trading, quantitative research and software engineering are not subject to this restriction. Goldman Sachs' own AI assistant, GS
According to a report by 36Kr's Silicon Star Pro, UniPat, a data company founded less than a year ago, is reportedly in talks to raise about $300 million, led by Alibaba with participation from Tencent and Sequoia China, at a post-money valuation of $2.5 billion. The article says a single piece of data with user behavior trajectories can be quoted at 1,000 to 3,000 yuan, while work-scenario data with a complete operation process can sell for around 10,000 yuan, and Mercor estimates that only 2% to 5% of the data frontier labs need has been completed so far. As model vendors tighten procurement, calculate ROI and run ablations on vendor data, the industry has already seen problems such as blacklisting data vendors and data quality being hard to verify.
According to a report by 36Kr's Silicon Star Pro, UniPat, a data company founded less than a year ago, is reportedly in talks to raise about $300 million, led by Alibaba with participation from Tencent and Sequoia China, at a post-money valuation of $2.5 billion. The article says a single piece of data with user behavior trajectories can be quoted at 1,000 to 3,000 yuan, while work-scenario data with a complete operation process can sell for around 10,000 yuan, and Mercor estimates that only 2%
HCLTech has released an AI operations solution for enterprise mainframe environments, combining dedicated agents with a conversational AI assistant to cover system management, observability, optimization and operations workflows, with the agents operating under human supervision. The solution is built on HCLTech's Mainframe Center of Excellence; the company says it can improve operational efficiency, speed up problem resolution and drive a shift from reactive response to proactive management, but it did not disclose customer deployments, the mainframe platforms supported, or quantified improvement data.
HCLTech has released an AI operations solution for enterprise mainframe environments, combining dedicated agents with a conversational AI assistant to cover system management, observability, optimization and operations workflows, with the agents operating under human supervision. The solution is built on HCLTech's Mainframe Center of Excellence; the company says it can improve operational efficiency, speed up problem resolution and drive a shift from reactive response to proactive management, bu
NVIDIA AI Cluster Runtime (AICR) uses reusable recipes to codify tuning configurations, generate deployment packages, and validate running clusters, helping Kubernetes environments support Vera Rubin NVL72 from Day 1. The Vera Rubin recipe series locks in configurations including the R615.23 open-source kernel module driver, DRA driver v0.5.0 (with ComputeDomains), and the 64k-page kernel of Ubuntu 26.04; end-to-end validation was completed on a reference cluster with GPU Operator v26.7.0, where cross-node NCCL all-reduce reached 744.71 GB/s bus bandwidth at 16 GiB. Early chip validation also found that go-nvlib did not map the Rubin architecture value, causing the DRA driver kubelet plugin to crash and restart; the fix has been included in the official release of DRA driver v0.5.0.
NVIDIA AI Cluster Runtime (AICR) uses reusable recipes to codify tuning configurations, generate deployment packages, and validate running clusters, helping Kubernetes environments support Vera Rubin NVL72 from Day 1. The Vera Rubin recipe series locks in configurations including the R615.23 open-source kernel module driver, DRA driver v0.5.0 (with ComputeDomains), and the 64k-page kernel of Ubuntu 26.04; end-to-end validation was completed on a reference cluster with GPU Operator v26.7.0, where
Circle K built an agentic automated triage system with Amazon Bedrock, reducing average DevOps incident triage time to under 15 minutes—a 67–75% reduction according to internal team metrics. In the system, Grafana alerts trigger AWS Lambda via Amazon SNS and Amazon SQS to invoke Amazon Bedrock Flows, which orchestrates multiple specialized agents in parallel to query logs and infrastructure, and pushes structured summaries to Slack; it has since migrated to Amazon Bedrock AgentCore.
AWS demonstrated a multi-agent engineering assistant based on Amazon Bedrock AgentCore, Amazon Nova Multimodal Embeddings, and the open-source Strands Agents SDK, designed to reduce manual cross-checking in parts substitution evaluations. In the system, a planner routes tasks to three expert agents—Catalog, Evaluation, and Knowledge—which are respectively responsible for multimodal retrieval, scoring according to Python tolerance rules, and verifying lifecycle information using real-time web data, with a supervisor ultimately aggregating the results. The system only assembles evidence and drafts review documents; it does not itself recommend or approve substitutions, and every evaluation writes an audit record aligned with AS9100 8.4 and 8.5.6.
AWS demonstrated a multi-agent engineering assistant based on Amazon Bedrock AgentCore, Amazon Nova Multimodal Embeddings, and the open-source Strands Agents SDK, designed to reduce manual cross-checking in parts substitution evaluations. In the system, a planner routes tasks to three expert agents—Catalog, Evaluation, and Knowledge—which are respectively responsible for multimodal retrieval, scoring according to Python tolerance rules, and verifying lifecycle information using real-time web dat
An AWS official blog post explains how to fine-tune a shopping agent on Amazon SageMaker AI using sequence-level knowledge distillation (SeqKD): using GLM-5.3 on Amazon Bedrock as the teacher model to generate trajectories, using the ShoppingBench scorer to retain only trajectories that solve the task, and then performing serverless SFT on the Qwen3.5-4B student model. On 324 held-out test questions, the distilled 4B student's average absolute success rate increased from 0.297 to 0.541, essentially on par with the teacher model and mid-sized frontier models; when the teacher's reasoning process was removed and only tool calls were learned, the average success rate dropped to 0.415.
r model to generate trajectories, using the ShoppingBench scorer to retain only trajectories that solve the task, and then performing serverless SFT on the Qwen3.5-4B student model. On 324 held-out test questions, the distilled 4B student's average absolute success rate increased from 0.297 to 0.541, essentially on par with the teacher model and mid-sized frontier models; when the teacher's reasoning process was removed and only tool calls were learned, the average success rate dropped to 0.415.
The LMSYS blog introduces how HiCache is implemented in context parallelism, pipeline parallelism, speculative decoding, unified KV layout, and P/D disaggregation scenarios, with the core being to keep cache decisions consistent across ranks. The iFlytek Spark MaaS team used HiCache plus external storage to serve GLM-5.1/5.2 CodingPlan workloads, raising the cache hit rate from about 30% to 95% and reducing median TTFT to under 3 seconds. In CP sharding tests, the GPU token hit rate increased by 40.48 percentage points, the host memory serving token volume dropped by 81.2%, and the average prefill queueing time decreased by 56.2%.
unified KV layout, and P/D disaggregation scenarios, with the core being to keep cache decisions consistent across ranks. The iFlytek Spark MaaS team used HiCache plus external storage to serve GLM-5.1/5.2 CodingPlan workloads, raising the cache hit rate from about 30% to 95% and reducing median TTFT to under 3 seconds. In CP sharding tests, the GPU token hit rate increased by 40.48 percentage points, the host memory serving token volume dropped by 81.2%, and the average prefill queueing time de
AWS presents a five-step closed loop for managing Amazon Bedrock costs: attribute by IAM principal, record request observability, control spending with budget alerts and hard blocks, optimize platform defaults such as model routing and caching, and review on a regular cadence. Amazon Bedrock now supports automatically attributing inference costs to the IAM principal that initiated the call, with the caller ARN written to the line_item_iam_principal column in CUR 2.0, covering IAM users, roles, federated identities, and API keys. In shared gateway scenarios, role session names and session tags can be set per user, credentials are cached for up to 1 hour, and attribution by user and tenant is restored.
AWS presents a five-step closed loop for managing Amazon Bedrock costs: attribute by IAM principal, record request observability, control spending with budget alerts and hard blocks, optimize platform defaults such as model routing and caching, and review on a regular cadence. Amazon Bedrock now supports automatically attributing inference costs to the IAM principal that initiated the call, with the caller ARN written to the line item iam principal column in CUR 2.0, covering IAM users, roles, f
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.
ee 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.
Canadian large-model unicorn Cohere announced the first closing of its Series E funding, with this round targeting up to CAD 7 billion (approximately RMB 33.4 billion); if achieved, it will set the record for the largest single private funding round in Canadian startup history. This round is led by German retail giant Schwarz Group, with existing investors including Nvidia and Saudi Aramco's Prosperity7 Ventures continuing to participate, and the Canadian government and others investing for the first time; if the fundraising target is met, Cohere's valuation will reach CAD 35.7 billion (approximately RMB 170.2 billion). Cohere plans to carry out two AI company acquisitions within a year, launch an enterprise cybersecurity product Cohere Cyber, and train a new generation of frontier large models.
Canadian large-model unicorn Cohere announced the first closing of its Series E funding, with this round targeting up to CAD 7 billion (approximately RMB 33.4 billion); if achieved, it will set the record for the largest single private funding round in Canadian startup history. This round is led by German retail giant Schwarz Group, with existing investors including Nvidia and Saudi Aramco's Prosperity7 Ventures continuing to participate, and the Canadian government and others investing for the
The Information has revealed details of DeepSeek's second funding round, which has raised 105.5 billion yuan (about $15.8 billion), attracting nearly 30 investors. Tencent and CATL each contributed 10 billion yuan, with ChangXin Memory Technologies, SMIC, Meituan, vivo, Xiaohongshu and others also on the list. State-owned investors contributed about 27% of the amount in this round, far higher than 2% in the first round, with China Mobile, China Telecom, CICC, CITIC Securities and China Life among the participants. The report said that due to rising prices of Nvidia's high-end chips and precautions against an AI bubble, DeepSeek doubled its fundraising target midway through the round.
The Information has revealed details of DeepSeek's second funding round, which has raised 105.5 billion yuan (about $15.8 billion), attracting nearly 30 investors. Tencent and CATL each contributed 10 billion yuan, with ChangXin Memory Technologies, SMIC, Meituan, vivo, Xiaohongshu and others also on the list. State-owned investors contributed about 27% of the amount in this round, far higher than 2% in the first round, with China Mobile, China Telecom, CICC, CITIC Securities and China Life amon
At the 88th China Educational Equipment Exhibition, Zuoyebang fully showcased for the first time its AI smart education solution for K12 schools and launched the AI-native smart teaching terminal, the Yunsi AI Smart Teaching PAD, enabling interconnected home-school learning scenarios. The solution integrates the capabilities of its learning device and Yunsi Zhixue, and, backed by a question bank of over 3.3 billion items and self-developed large models, covers scenarios such as lesson preparation, homework grading, and exam assessment. It currently covers more than 1,500 schools across over 20 provinces, municipalities, and autonomous regions nationwide.
At the 88th China Educational Equipment Exhibition, Zuoyebang fully showcased for the first time its AI smart education solution for K12 schools and launched the AI-native smart teaching terminal, the Yunsi AI Smart Teaching PAD, enabling interconnected home-school learning scenarios. The solution integrates the capabilities of its learning device and Yunsi Zhixue, and, backed by a question bank of over 3.3 billion items and self-developed large models, covers scenarios such as lesson preparatio
According to Bloomberg, citing anonymous sources, Anthropic has become SpaceX's largest single customer, contributing about one-third of revenue and replacing the U.S. government in its former position. This relationship stems from a multibillion-dollar partnership Anthropic reached with SpaceX this spring, under which it obtained most of the AI compute capacity originally belonging to xAI. The report also mentions that SpaceX plans to move AI data centers to space on a large scale, but the technical feasibility remains disputed.
According to Bloomberg, citing anonymous sources, Anthropic has become SpaceX's largest single customer, contributing about one-third of revenue and replacing the U.S. government in its former position. This relationship stems from a multibillion-dollar partnership Anthropic reached with SpaceX this spring, under which it obtained most of the AI compute capacity originally belonging to xAI. The report also mentions that SpaceX plans to move AI data centers to space on a large scale, but the tech
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.
Combining the latest financial reports from giants such as Amazon, Google, and Meta with surveys from multiple institutions, the article sorts out how the AI dividend is distributed among leading model companies, upstream chip and cloud vendors, enterprises adopting AI, and ordinary workers. Anthropic's prospectus shows revenue of $16.286 billion in the first half of 2026, up 1,300% year over year, but compute and infrastructure costs reached $8.945 billion, accounting for 55% of revenue. In a McKinsey survey, 80% of respondents said AI improved efficiency, while only 37% believed AI made a positive contribution to their company's EBIT.
Combining the latest financial reports from giants such as Amazon, Google, and Meta with surveys from multiple institutions, the article sorts out how the AI dividend is distributed among leading model companies, upstream chip and cloud vendors, enterprises adopting AI, and ordinary workers. Anthropic's prospectus shows revenue of $16.286 billion in the first half of 2026, up 1,300% year over year, but compute and infrastructure costs reached $8.945 billion, accounting for 55% of revenue. In a M
Research published last month by the AI Language Equity Research Alliance found that AI models consume vastly different numbers of tokens when processing different languages, with a gap of up to 17 times in one of the models. The study says token consumption already varies by 1.5 to 5 times among the 20 most commonly used languages on the internet, and the gap widens further when expanded to 200 languages; the researchers call this extra cost a hidden "tokenizer tax." Higher token consumption not only drives up costs but also slows response times and compresses the available context. The researchers recommend that AI labs publish evaluation results for each language and improve tokenizer design in the next generation.
Research published last month by the AI Language Equity Research Alliance found that AI models consume vastly different numbers of tokens when processing different languages, with a gap of up to 17 times in one of the models. The study says token consumption already varies by 1.5 to 5 times among the 20 most commonly used languages on the internet, and the gap widens further when expanded to 200 languages; the researchers call this extra cost a hidden "tokenizer tax." Higher token consumption no
