Every post, summarised

Sep 9, 2026

• Anthropic

  Anthropic acquires Stainless

  Anthropic is acquiring Stainless, an SDK and MCP server tooling company founded in 2022, to expand how Claude and other agents connect to data and tools. Stainless has generated every official Anthropic SDK since the API’s early days and is used by hundreds of companies to produce SDKs, CLIs, and MCP servers from API specifications. Its tooling supports TypeScript, Python, Go, Java, and other languages, with libraries designed to feel native while remaining fast and reliable. The companies say combining their teams will advance developer experience and agent connectivity on the Claude Platform, while Stainless’s founder, Alex Rattray, said the team will continue its work within Anthropic.

• Anthropic

  KPMG integrates Claude across its workforce

  KPMG announced a global alliance with Anthropic to integrate Claude across its workforce and client services in 138 countries and territories. Every 276,000+ KPMG employees will receive access, while Claude is embedded in Digital Gateway, the Microsoft Azure-based platform that combines tax expertise, proprietary tools, and client data; initial client tools target tax and legal work. Claude Cowork and Managed Agents are intended to let professionals build capabilities inside the platform, while KPMG and Anthropic will use Claude to identify and fix vulnerabilities under KPMG’s Trusted AI framework and develop offerings for private-equity portfolio companies. The alliance also names KPMG a preferred private-equity partner, introduces offerings including KPMG Blaze with Claude Code, and draws on joint UT Austin research emphasizing employee judgment, workflow design, output evaluation, and decision-making alongside AI.

• Anthropic

  KiYoung Choi named Representative Director of Korea

  KiYoung Choi is joining Anthropic as Representative Director of Korea ahead of the planned opening of its Seoul office. Korea is described as one of Claude.ai’s most active markets, with usage more than 3.5 times the rate expected for its population size and concentrated in technical and creative work. Choi joins from Snowflake, where he was General Manager for Korea, bringing over three decades of experience across technology businesses in Korea and Asia-Pacific, including country leadership roles at Google Cloud, Adobe, Autodesk, and Microsoft. He will lead a Korea go-to-market strategy, build the local team and partnerships, and support enterprise, startup, government, research, and developer engagement; examples of existing use include Law&Company’s legal assistant and SK Telecom’s custom customer service model.

• Hugging Face

  IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license

  IBM has released Granite Time Series PatchTST-FM-r2, a roughly 385-million-parameter foundation model for zero-shot forecasting across demand, prices, energy loads, traffic, telemetry, and other time series. The model uses conformer blocks combining multi-head self-attention with temporal convolution, 50% overlapping patches with Hamming-window weighting, overlap-and-add forecasting, contexts up to 8,192 steps, and a 99-quantile prediction head for probabilistic outputs and imputation support. On GIFT-Eval, it ranks second for both CRPS and MASE among replicable zero-shot models as of September 8, 2026, and first in that category among permissively licensed models; including pretrained models, it ranks third for CRPS and fourth for MASE. IBM provides open weights, architecture, inference code, and reproducibility code under Apache 2.0 and OpenMDW 1.0. The release also describes Python use and streaming integration through Apache Flink on Confluent Cloud.

• Red Hat

  Deploy NeMo Guardrails on Red Hat OpenShift AI

  This article shows how to deploy an evaluated NeMo Guardrails configuration on a Red Hat OpenShift AI cluster, continuing a series on local development and dataset-based evaluation. It requires OpenShift AI 3.5 or later and namespace write access, while OpenShift AI 3.2 or newer can be configured to use the 3.5 NeMo Guardrails image through TrustyAI service-operator changes. Deployment uses a ConfigMap for the configuration, a NemoGuardrails custom resource, and an authentication annotation that protects the server route; an empty rails.co file is sufficient when no custom algorithms are defined. The deployed server accepts v1/guardrail/checks requests with a bearer token, and the demonstrated classifier permits a benign prompt, blocks a prompt injection, and rejects an invalid token. The article notes that LLM proxying and LLM-based guardrailing are outside its scope.

• Jetbrains

  Get Gemini 3.8 Flash With 75% Off

  Google’s Gemini 3.8 Flash is now available in Junie’s IDE plugin and Junie CLI, accompanied by a limited-time 75% introductory discount. The model targets long, multi-step engineering tasks: it spends more tokens exploring directories, retrying failed code, writing bug-reproduction scripts, running tests through tool calls, and checking changes before completion. Google’s DeepSWE v1.1 testing is described as showing 3.8 Flash outperforming most larger frontier models at significantly lower cost, although the source does not provide specific scores. Gemini 3.7 Flash remains available for minor fixes and small refactorings, where its lower per-token cost may be preferable, while 3.8 Flash can be selected from the model picker without setup or a waitlist.

• Databricks

  Beyond embedding: How to secure AI/BI Dashboards for every viewer

  Embedding a Databricks AI/BI Dashboard in a customer-facing application is straightforward; authorization determines which rows each viewer may see. The pattern supports external partners and internal Databricks teams with one dashboard and shared dataset, while applying different regional access and masking rules. An entitlements table stores partner IDs and IdP-synchronized group names alongside regions and PII-mask flags; a secured view filters on the signed __aibi_external_value from the embed token. Direct SQL access is separately protected by Unity Catalog row filters and column masks, which evaluate the querying user's identity and groups. The design provides a default-deny, pooled logical multi-tenancy model that centralizes rules while covering both application and workspace query paths.

• Red Hat

  Use a local and open source code assistant

  Closed or cloud-based coding assistants can expose work to external services, so the article presents a local, open-source alternative built around OpenCode. OpenCode is an AI coding agent—not an LLM—that runs as a terminal interface, desktop application, or IDE extension and connects to models through the Model Context Protocol (MCP). The setup installs OpenCode, selects a local model runtime such as Ollama for AMD/NVIDIA GPUs or OpenVINO Model Server with INT4 Qwen3-8B for Intel integrated graphics, then configures opencode.jsonc and an editor plug-in. Examples cover Qwen3:14b with Ollama, Podman-based OpenVINO serving, and NetBeans integration; the resulting assistant can answer queries and edit files after permission is granted, although local inference may be slow and generated advice can be unreliable.

• Databricks

  Introducing Consort: Test-driven development on a branching database

  Consort is introduced as an open-source agentic development framework for test-driven development on branching Lakebase Postgres databases. It addresses the limitations of mocks, shared staging databases, and risky schema-change ceremonies by bringing integration testing into the inner loop. Its build lane runs red/green/refactor cycles against isolated copy-on-write branches of real data, while versioned migrations keep code and schema changes together and pull-request checks test migrations against fresh branches. Consort organizes work through role-based agents and a deterministic conductor, preserving feature artifacts in dedicated directories and supplying scoped context packages. A VS Code plugin pairs Git and Lakebase branches, while an observability dashboard exposes agent turns and human approval gates; the project is community-supported rather than a bundled platform feature with an SLA.

• Databricks

  Adaptive Instructed-Retriever: Frontier-Quality Search at 2x Lower Latency

  Adaptive Instructed-Retriever addresses the tension between retrieval quality and latency for enterprise data agents, especially on multi-hop questions that benefit from sequential search. It combines parallel single-step retrieval with bounded multi-step search, using a fixed upper limit and an adaptive stopping policy that spends extra steps only when they are likely to improve results. The model is trained with online reinforcement learning using CISPO, a reward that balances trajectory quality against search cost, and synthetic multi-hop data alongside Instructed-Retriever-1 data. Across seven held-out internal and external benchmarks, it matches leading third-party and open-source models while answering in 5.8 seconds, reported as more than twice as fast as the compared alternatives, and substantially improves over single-step search. Varying the step penalty produces checkpoints spanning different quality-latency tradeoffs for production workloads.

• Cloudflare

  How we rebuilt Cloudflare Workers’ module registry for Node.js compatibility

  Cloudflare has rebuilt the module registry in workerd, the open-source core of the Workers runtime, to improve speed, standards compliance, and alignment with Node.js. The change addresses compatibility beyond runtime APIs by making module resolution, loading, and caching behave more like Node.js across ESM, CommonJS, and WebAssembly. The new registry uses URLs as specifiers, supports import.meta.url, import.meta.main, and import.meta.resolve(), distinguishes modules by query strings and fragments, validates import attributes, follows require(esm) rules, and compiles modules lazily while sharing caches across isolates. It also supports WebAssembly source phase imports and consistent loading errors; the existing registry remains in place, and the new implementation must be enabled with the new_module_registry compatibility flag.

• Jetbrains

  Join our live webinars: Migrating from Atlassian to YouTrack

  YouTrack announces live webinars for teams considering migration from Atlassian after Atlassian’s stated discontinuation of Data Center sales and support. The worldwide English-language session is scheduled for September 30, 2026, at 2:00 pm CET on YouTube, alongside a Japanese Zoom session and a Russian-language regional session hosted by Noventiq on October 28. The agenda covers migrating projects, users, and data from Jira, Confluence, and Jira Service Management, with a live demo, customer stories, pricing, licensing, deployment options, user packs, and partner support. It is aimed at administrators, project managers, and team leads, and presents YouTrack as a place to combine project management, documentation, and customer support. The Japan session additionally covers workflow automation and connecting AI agents through MCP, while registration for the Asia and Caucasus session is not yet open.

• Jetbrains

  Rust AI in Practice: Building LLM Applications With Rig

  Rig is an open-source Rust library that provides a common interface for LLM providers including OpenAI, Anthropic, and Gemini, avoiding application rewrites when switching services. It structures applications around a provider client, completion model, agent, preamble, and registered tools, with async APIs handling provider network requests. In the Rat Code demo, built with Ratatui, separate tools read and write files and run shell commands, while Rig’s streaming API updates the terminal as responses arrive. The library also supports RAG through vector store abstractions, local inference via Ollama, llama.cpp, or Candle, and cassette-based integration tests that replay recorded HTTP interactions in CI; live model quality requires separate scheduled testing.

• Ramp

  How we built OCA, our AI on-call assistant

  Ramp built OCA, an AI on-call assistant that investigates incidents, shares findings in Slack, answers follow-up questions, and can ask Inspect to prepare code-fix pull requests for human review. In the past four weeks, it assisted with 575 of 1,220 incident-related merged PRs; among incidents with merged fixes, OCA-assisted fixes averaged 37% fewer engineers and about 50% less estimated engineer time, based on Slack activity. Investigations run in a monorepo through Claude Code or Codex, with Temporal managing workflow, while prompts, custom tools, skills, and six one-shot hooks provide successive evidence and reasoning checks. Session-driven testing exposed errors such as misleading one-hour log windows, leading to just-in-time instructions and interim updates that balance accuracy with response speed. OCA remains human-in-the-loop: it has broad read-only access, narrowly scoped writes, and cannot merge changes, although the team is building toward end-to-end routine incident handling.

• Jetbrains

  The Evolution of WSL Support in JetBrains IDEs

  JetBrains IDEs have supported WSL through several architectures, but the 2026.2 release makes Native mode the recommended entry point for projects stored there. In Native mode, the Windows IDE delegates filesystem and process operations to the Rust-based IJent agent inside WSL, with EelApi abstracting local and remote environments for IDE and plugin code. Earlier approaches included 9P filesystem access, WSLg, and Remote Development, with problems involving symlink handling, latency, GUI limitations, installation overhead, and client–backend communication costs. Performance tests on a cold first open of spring-framework measured Native mode at 11.5 seconds to become ready versus 18.5 seconds for 9P, while project-tree scanning took 3.7 versus 8.1 seconds. Remote Development remains available, but direct project opening is now preferred in IntelliJ IDEA, WebStorm, and PhpStorm.

• Supabase

  Supabase is now available in Gemini Enterprise

  Supabase is now available as a prebuilt connector in Google Cloud Gemini Enterprise, allowing teams to query and take action on Supabase projects through natural-language prompts. After an organization-level connection, Gemini Enterprise calls the connector for live answers, does not store or index Supabase data, and can combine results with connected tools such as GitHub, Linear, Notion, Slack, and Salesforce. The integration supports questions about tables, database metrics, and table structure, while exposed tools are marked read-only or destructive. Business users connect through the connector menu, whereas Standard, Plus, and Frontline administrators use a client ID and secret in the admin console; the connector is available across all editions.

• Red Hat

  Personalize your product's text-to-speech voice for any language: Fine-tuning with Kubeflow Trainer on Red Hat OpenShift AI

  Training an English-centered text-to-speech model for Turkish can produce fluent but hard-to-understand pronunciation for native speakers. This tutorial fine-tunes unsloth/orpheus-3b-0.1-pretrained with about 81,000 Turkish text-audio pairs on Red Hat OpenShift AI using a Kubeflow Trainer TrainJob distributed across two nodes with two A100 GPUs each. LoRA freezes the 3-billion-parameter base and trains 97.3M adapter parameters, while preprocessing converts text to Llama-3 tokenizer tokens and audio to three-layer SNAC codes, masking text labels during scoring. The merged model is evaluated against the base on ten Turkish sentences using Whisper transcription and WER/CER metrics. Mean WER falls from 2.44 to 0.35 and CER from 1.59 to 0.09, though the authors describe the small test as directional.

• Databricks

  Evaluation-First AI Agents: How Zepto Scales Customer Support on Databricks and MLflow

  Zepto describes its customer-support system, processing more than 100,000 tickets daily, and the assurance problems that emerged as volume, categories, and customer behavior changed. Its response is an evaluation-first architecture on Databricks and MLflow, using development and production loops connected by a quality gate. Agent traces capture prompts, completions, retrieved documents, tool calls, latencies, and decision paths through MLflow, OpenTelemetry spans, and Unity Catalog-backed Delta tables; stakeholders define evaluation pillars and gates around a living golden dataset. The dataset grew from 500 examples with an eight-point development–production accuracy gap to 5,247 examples with a 0.4-point gap over six months, while prompt optimization and multiple scorers support iteration. Reported outcomes include 80% plus of tickets fully managed with human oversight, a 65% reduction in support cost or tickets, 20% higher CSAT, 8% better accuracy, and faster development and resolution.

• MongoDB

  Benchmark Shows MongoDB Atlas Manages Thousands of Payments Per Second with Zero Data Loss

  A benchmark of the Icon Payments Framework (IPF), trusted by Citi, NatWest, and BNP Paribas, on MongoDB Atlas demonstrated extreme performance and resilience for instant payment processing. The system sustained 6,000 end-to-end payments per second, requiring approximately 430,000 simultaneous database operations, with a mean end-to-end latency of 0.51 seconds and persistence latency below 30 milliseconds. Upgrading from MongoDB Atlas 8.0 to 8.3 reduced persistence latency by roughly 50%, from 40 to 20 milliseconds at 4,000 payments per second, and throughput scaled near-linearly across the 500 to 6,000 range while latency remained flat until peak and hot-shard CPU utilization sat at about half. During a primary failover, a new node was elected in five seconds with zero data loss and no application restart. An application node failure recovered to 3,500 transactions per second in 90 seconds with zero data loss.

• MongoDB

  Build a Fraud Review Agent with MongoDB Atlas and Claude Managed Agents

  Claude Managed Agents runs Anthropic’s Claude as a hosted agent without managing inference infrastructure. Pairing it with MongoDB Atlas provides fast access to operational and vector data through a single pymongo connection, eliminating the need for separate vector, full-text, and graph stores. This tutorial builds a human-in-the-loop fraud review agent that retrieves flagged transactions, runs four retrieval patterns (vector, full-text, hybrid, and graph) against one collection, and escalates risky cases to a human reviewer using Managed Agents’ native pause. The credential-safe integration uses host-side custom tools so the MongoDB connection string never enters the agent sandbox, keeping secrets on your side. The agent verifies a signed authorization mandate, uses $graphLookup to detect mule chains and $rankFusion for hybrid search, writes immutable verdicts and audit events back to the same cluster, and triggers durable human pauses on structuring deposits just under $5,000, wires over $50,000, and ring signals from graph traversal.