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Announcement posts on Yomu, summarised newest first.
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Announcement posts
SKT partnership announcement
SK Telecom (SKT), Korea’s largest mobile operator, has become a commercial partner and strategic investor in Anthropic. The companies will develop a multilingual large language model customized for telecommunications applications such as customer service, marketing, sales, and interactive consumer experiences, with support planned for Korean, English, Japanese, Spanish, and more. Anthropic will fine-tune Claude using SKT’s telecommunications expertise: SKT experts will provide feedback on responses, and that feedback will help train industry-specific solutions. SKT has invested an additional $100 million in Anthropic, following an earlier investment by SK Telecom Venture Capital, while the companies describe the partnership as a way to promote AI innovation and develop safer, more reliable technology for telcos worldwide.
Zoom partnership and investment in Anthropic
Anthropic announces a partnership with Zoom under which Zoom will use Claude, Anthropic’s AI assistant built with Constitutional AI, to develop customer-facing products centered on reliability, productivity, and safety. Zoom’s federated approach will combine its own technology with models including Claude to address diverse customer needs, with the first Claude integration planned for the Zoom Contact Center portfolio. There, Claude is intended to improve the end-user experience and support contact-center agent performance. Zoom Ventures has also invested in Anthropic, aligning the companies around customer-centric AI products grounded in trust and security and designed for real-world use. The announcement presents the collaboration as a way to bring robust, steerable AI into workplace applications.
Introducing 100K context windows
Claude’s context window has expanded from 9K to 100K tokens, or about 75,000 words, enabling businesses to submit hundreds of pages and sustain conversations for hours or days. The announcement describes using the larger window to digest documents, synthesize information across multiple files, answer questions, and work with full codebases, with the claim that complex questions may perform substantially better than vector-search approaches. In a test, Claude-Instant processed the 72K-token text of The Great Gatsby after one line was altered, identified the change correctly, and responded in 22 seconds; 100K tokens is also described as roughly six hours of audio. The capability is available through the API, with examples including financial analysis, legal review, developer-documentation search, podcast summarization, and rapid prototyping.
Anthropic partners with Google Cloud
Anthropic, an AI safety and research company, has selected Google Cloud as its cloud provider in a partnership intended to support the next phase of its work. The companies plan to co-develop AI computing systems, while Anthropic will use Google Cloud’s GPU and TPU clusters to train, scale, and deploy its AI systems to a larger set of people. The work centers on Claude, Anthropic’s AI assistant, and on building safe, steerable systems using techniques including Constitutional AI, which is intended to make AI technologies easier to rely on and understand. Anthropic says the infrastructure’s performance and scale will support a more robust platform for reliable, interpretable, and steerable AI systems.
Experiment with Qwen3.8-Flash-Next on NVIDIA GB300 NVL72 for Agentic Coding
Alibaba released the model weights for Qwen3.8-Flash-Next as a preview of the upcoming Qwen4 architecture, enabling developers to experiment with a multimodal mixture-of-experts model with a 125B-parameter main model, 51B N-gram embeddings, and 6B parameters activated per token. It supports a native 262,144-token context window, extensible to 1M tokens with YaRN. To reduce long-context attention compute and KV-cache growth, its hybrid design uses Gated DeltaNet in three of every four layers and Qwen Sparse Attention in the remaining layer, where micro-block importance guides retrieval. On NVIDIA GB300 NVL72, it delivers more than 16K tokens per second per GPU and more than 200 tokens per second per user; QSA achieved up to 7.6x prefill and 4.9x decoding speedups versus full attention. Fine-tuning is available through NVIDIA NeMo AutoModel with SFT or LoRA, and reinforcement learning through NVIDIA NeMo RL.
Compose Multiplatform 1.12.0 Released
JetBrains has announced the release of Compose Multiplatform 1.12.0 with targeted capabilities for artificial intelligence integrations, web rendering, and desktop application layouts. The update incorporates an experimental Model Context Protocol server inside Compose Hot Reload, allowing AI agents to trigger reloads, capture screenshots, inspect the semantic tree, and simulate user interactions directly. For web deployments, the framework now provides automatic font fallback by downloading necessary Noto font subsets on demand when unresolved characters occur during rendering. Desktop developers receive an experimental v2 window and dialog application programming interface in the androidx.compose.ui.window.v2 package, which distinguishes requested states from actual window states and permits precise control over positioning, sizing constraints, and multi-screen placement. These combined enhancements improve developer workflows and runtime flexibility across multiple target platforms.
OpenTelemetry Comes to IntelliJ IDEA, GoLand, PyCharm, and WebStorm
With the 2026.2 release, JetBrains expanded its OpenTelemetry plugin from Rider to IntelliJ IDEA, GoLand, PyCharm, and WebStorm. The tool captures logs, metrics, traces, and service maps locally without requiring an external observability backend. Developers can search structured log records, plot metric values over time, inspect distributed spans, and verify communication paths across databases and message queues during local runs. To ingest telemetry, the plugin automatically configures OpenTelemetry Protocol environment variables for supported run configurations and terminal sessions, or accepts forwarded data from existing collectors. Additionally, experimental Model Context Protocol support allows AI coding agents to query gathered logs, spans, and service topology.
Try the Ansible playbook generation lab with Gemini and OpenAI
The official Visual Studio Code extension for Red Hat Ansible Automation Platform includes an automation coding assistant, formerly called Ansible Lightspeed. Initially integrated with IBM watsonx, the extension now supports Google Gemini and OpenAI-compatible endpoints through Red Hat AI. An updated interactive lab demonstrates these capabilities through an end-to-end scenario involving database installation and HTML landing page deployment across two nodes. Learners write natural language prompts to generate Ansible playbooks and roles, evaluate output, and utilize real-time automated linting and integrated documentation. By serving an open-source model through a preconfigured OpenAI-compatible endpoint, the refreshed environment removes external credential setup steps.
Introducing Governance Hub: Intelligent, account-level governance over your Databricks estate
Databricks introduces Governance Hub, a beta account-level interface for platform and governance teams overseeing estates spanning hundreds of workspaces and multiple regions. It consolidates data health, access, AI usage, and cost information across AWS, Azure, and GCP, replacing scattered system tables, workspace views, and third-party tools with drill-downs for metadata gaps, principal access, token and model activity, and untagged spend. The hub also integrates with Genie, allowing users to ask governance questions in natural language and receive answers and recommendations grounded in governance data; future actions such as configuring policies and alerts are described as planned. Account admins enable it from the Account Console, with access varying by role and existing permissions respected, while upcoming features include performance and security insights and delegated recommendations.
MongoDB Atlas Joins Google Cloud Foundation Fabric (FAST): Secure Private Data Infrastructure in Minutes
Many organizations lack cloud fundamentals, making foundational setup complex and time-consuming; Google Cloud Foundation Fabric (FAST) is a GitOps-driven landing zone toolkit that accelerates establishing a production-ready Google Cloud organization. A new MongoDB Atlas module within FAST enables provisioning MongoDB Atlas clusters with Private Service Connect (PSC) for secure, private connectivity. PSC avoids VPC peering issues like CIDR conflicts and transitive routing, using a unidirectional consumer-producer model that respects project boundaries and routes traffic over Google’s private fiber network. The module automates cluster creation, PSC endpoint registration with Service Directory, and internal DNS resolution, integrating with Google Cloud KMS for customer-managed encryption keys. This integration reduces foundational setup from weeks to minutes, providing a secure data foundation that connects to BigQuery for real-time analytics and Vertex AI for GenAI workloads.
What's new in the ClickHouse .NET Driver: the road from 1.0 to 1.3
The ClickHouse .NET Driver moved from its 1.0 stable release to versions 1.1–1.3, changing how applications insert, read, and parameterize data while expanding type coverage, configurability, observability, and .NET ecosystem integrations. The largest ergonomic change is registered POCO support: RegisterPocoType() validates mappings up front, pre-compiles getters and setters, and enables InsertBinaryAsync with IEnumerable plus lazy QueryAsync() results through IAsyncEnumerable. Attributes customize column and ClickHouse type mappings, JSON serialization supports POCOs with paths and ignored properties when JsonWriteMode is Binary, and three optional extension points govern parameter type inference, wire formatting, and read conversion. The announced 1.4 roadmap targets allocation reductions, pluggable compression, and box-free POCO operations; version 1.5 is planned to add a Native/TCP client with columnar transfer and server progress information.
Funding better evaluations of AI’s impact on wellbeing
A $5 million grant program will fund independent research into how AI affects user wellbeing, offering direct funding, model access, and technical support for open-source evaluations. The initiative addresses the difficulty of judging wellbeing-related behavior, where risks may emerge only across long, shifting conversations and where otherwise reasonable advice can become harmful in a different context, such as disordered eating. Grantees will work independently and publish reusable evaluations or benchmarks, with guidance asking projects to define pass/fail criteria, involve clinical and subject-matter experts, test overcompliance and overrefusal, model realistic multi-turn scenarios, and validate graders against experts. Applications are due September 21, with selected full-proposal applicants notified by October 5.
Introducing the Anthropic Economic Index
Anthropic introduced the Anthropic Economic Index to track how AI is used in work and to study its effects on labor markets over time. Its first report analyzes approximately one million anonymized Free and Pro Claude.ai conversations, using Clio to match them with occupational tasks from the U.S. Department of Labor’s O*NET database. Usage concentrated in computer and mathematical work, which represented 37.2% of queries, while 36% of occupations showed AI use in at least a quarter of associated tasks and about 4% reached three-quarters. The report found 57% augmentation versus 43% automation, lower use in both low- and very-high-wage occupations, and plans for open data and repeated analyses, while noting possible classification errors and coding overrepresentation.
Launching the Anthropic Economic Futures Programme in the UK and Europe
Anthropic is expanding its Economic Futures Programme to the UK and Europe to support research and policy discussions about AI’s effects on labor, productivity, and growth. The expansion begins with an Economic Futures Symposium at the London School of Economics and Political Science, where more than 100 experts and selected researchers will discuss policy proposals. Planned support includes research grants, API credits for UK and EU researchers, forums for evidence-based policy, and more granular data on European AI use. The Anthropic Economic Index reports that coding is Claude’s most common use across Europe, while UK usage most often supports academic research, writing, and educational content; Germany shows high per-capita use for manufacturing equipment, and France records four times the global average for dining, shopping, and nightlife information. Regular public releases will track adoption across European industries and regions as the programme seeks to inform decisions about the workforce transition.
Supporting ambitious external research through the Anthropic Economic Futures Research Fund
The Anthropic Economic Futures Research Fund commits $200 million to external research on interventions that could help society prepare for AI’s economic impacts. It prioritizes five areas: workplace integration, support for AI-driven transitions, income support for displacement, worker stakes in AI-driven growth, and evidence on public investments. The fund will favor large-scale randomized controlled trials, ambitious pilots, and program evaluations, with particular interest in projects that share findings at key milestones and can scale rapidly if promising; typical awards will be $5 million to $30 million, with nothing below $1 million funded directly. Eligible applicants include accredited degree-granting institutions, independent research and policy organizations, and nonprofits with experience running field experiments at scale, while the fund remains global despite US-centric examples.
The Anthropic Economic Index connector
The Anthropic Economic Index connector for Claude makes the Index’s data available for direct question-and-answer exploration. The Index measures how AI is used in the economy, including occupations, geographic usage, and the tasks people perform or automate with Claude. Users enable the connector from the claude.ai connectors directory, without installing anything, then can ask broad or specific questions and request the underlying data behind an answer. Responses are grounded in Index data, but the source cautions that it reflects Claude usage patterns rather than the labor market as a whole; the full datasets remain freely available on the website for further exploration.
Modernizing SQL ETL in Lakehouse with Declarative Patterns
Databricks is extending declarative ETL patterns into the Lakehouse SQL Editor so SQL practitioners can define recurring transformations without a dedicated pipelines-oriented environment. The initial operations cover append-only updates, change data capture through AUTO CDC, and targeted batch overwrites through REPLACE WHERE; flows can be scheduled, triggered by upstream updates, run on demand, or orchestrated through SQL tasks in Jobs. APPEND tracks processed source data, AUTO CDC handles keys, sequencing, deletes, and SCD Type 1 or Type 2 results, while Enzyme can incrementally process changed data for REPLACE WHERE. In Lakehouse benchmark testing, Enzyme-powered REPLACE WHERE ran 3.4x faster and 2.5x cheaper than traditional REPLACE WHERE, while users can retain procedural SQL and later move to the Lakeflow Pipelines Editor for multi-file development, dependencies, validation, and deployment.
CUDA Python 1.0: Stable APIs, One Foundation, Full Platform Access
CUDA Python 1.0, released with CUDA 13.3, presents Python as a supported way to access the full NVIDIA CUDA platform. It addresses a fragmented ecosystem in which libraries used separate binding layers, complicating shared GPU memory, streams, and resource ownership, and forcing developers toward either CUDA C++ extensions or higher-level libraries. The release combines independently versioned components, including cuda.core, cuda.compute, cuda.bindings, cuda-pathfinder, and nvmath-python, with semantic-versioning and deprecation commitments. cuda.core supplies common Python objects for devices, streams, and buffers, allowing NVIDIA and external libraries to compose on shared resources, while cuda.bindings offers 1:1 CUDA C API coverage. The stack spans runtime services, tuned CUDA libraries, communication tools, and kernel-authoring systems, although some newer kernel languages remain experimental and outside the 1.0 guarantees.
Get in and out of the airport faster—and back to earning—with pickup codes
The post introduces pickup codes, a new airport process intended to reduce eligible drivers’ waiting time, simplify rider matching, and help them return to the road sooner. Instead of a standard airport trip request or the usual queue, an eligible driver may receive a Driver app alert, choose a dedicated pickup-code line, and meet the next rider there. The rider provides a unique 6-digit code, distinct from the 4-digit PIN used for some non-airport pickups; the driver enters it in the app before the rider and luggage enter the vehicle, confirming the match. Trip details appear after confirmation, while fare information is available after completion, although the fare and destination can be viewed beforehand through the Waybill link in Trip Guide. Availability and eligibility vary by airport and product type, and drivers can instead remain in the waiting lot for a Trip Radar request without affecting their acceptance rate.
MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet
Meta has designed MetaRoCE, a clean-sheet RDMA transport for AI workloads running on commodity Ethernet, targeting clusters that can reach million-GPU scale. Unlike standard RoCE, it treats packet loss and out-of-order delivery as normal: packets are sprayed across NIC-managed paths, written directly to their destinations, and retransmitted selectively without PFC or reorder buffers. It combines per-path telemetry and congestion windows with ECN-based sender control and receiver-driven fair-share hints, while a single connection supports multiple ordered streams and paths. On a 64-node AMD GPU cluster using RCCL collectives, Meta reports higher throughput and lower flow-completion times than RoCEv2, about 86% throughput at 1% packet loss, linear scaling across four- and eight-plane topologies, and autonomous recovery from plane failures. Meta is contributing the specification, reference implementation, and compliance suite to OCP, with a broader release planned for October at the 2026 OCP Global Summit.
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