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Announcement posts on Yomu, summarised newest first.
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Announcement posts
Together AI brings Thinking Machines Lab’s new model Inkling on day 0
Thinking Machines Lab released Inkling, and Together AI announced day-zero access through its production inference platform. The multimodal mixture-of-experts model accepts text, image, and audio inputs and generates text through a unified decoder architecture, with controllable inference effort for trading reasoning depth, token usage, and latency. Inkling has 975B total parameters, 40B active parameters per token, a 1M-token context window, query-conditioned relative attention, short causal convolutions, and shared-sink MoE routing; Together serves it with a FlashAttention-4-based kernel. Preliminary evaluations report strong results across scientific reasoning, mathematics, coding, agentic, vision, and audio benchmarks, while the model is also post-trained for forecasting and calibrated prediction. It is available on Together Serverless through OpenAI-compatible APIs without infrastructure provisioning.
New in Together GPU Clusters: Reliability and control for production GPU clusters
Together GPU Clusters introduces changes for production training and inference workloads, addressing hardware failures, scheduler problems, and the limits of single-admin access. Platform health improvements include continuously running passive health checks, human-approved node repair recommendations, and Slurm-on-Kubernetes 2.0, which adds self-healing daemons, durable job accounting, descendant-process cleanup, and refreshed GPU state after reschedules. Operational controls include a cluster details view with health, utilization, events, configuration, and node actions, plus external OIDC, startup scripts, and an acceptance-test opt-out. Newly provisioned Slurm clusters use the latest stack by default, while existing managed clusters can be migrated during a maintenance window at no cost; the stated result is faster recovery, fewer support tickets, and greater visibility and control for growing teams running large clusters.
Welcome Inkling by Thinking Machines
Thinking Machines Lab has released Inkling and the smaller Inkling-Small family as open multimodal models that accept image, text, and audio inputs. Inkling is described as a roughly 1T-parameter decoder-only Mixture-of-Experts model, with 975B total and 41B active parameters, a 1M-token context window, and training on 45 trillion multimodal tokens. Its architecture combines relative attention, alternating global and sliding-window attention, short convolution, shared experts, hierarchical image patchification, and discretized mel-spectrogram audio processing. The release includes BF16 and NVFP4 checkpoints, speculative MTP layers, day-zero support in Transformers, SGLang, vLLM, and llama.cpp, plus deployment configurations and interactive voice and image demos. Reported benchmarks show Inkling and Inkling-Small achieving strong but varied results across reasoning, coding, vision, audio, factuality, chat, and safety evaluations.
Introducing Real World VoiceEQ: Measuring the human quality of voice AI
Existing voice-AI benchmarks increasingly suggest near-human performance, while real-world conversations still expose failures involving speaker consistency, hesitation, accents, noise, emotion, and natural responses. Real World VoiceEQ measures human quality in voice interaction by testing whether systems recognize, produce, and respond to acoustic information omitted by transcripts, including tone, emotion, speaker identity, and background context. It evaluates more than 40 proprietary and open-source models across 15+ dimensions and 60+ metrics covering ASR, TTS, S2S, and Speech Understanding, using more than 1 million human ratings, including 785,000 TTS and 48,000 STS ratings. Findings indicate that capabilities are specialized, speech-to-speech systems often underuse paralinguistic cues, and traditional benchmarks can overestimate performance in noise and other conversational conditions. The research also reports that human agreement remains essential for subjective acoustic and social judgments because SLM evaluators were less reliable outside clear, verifiable tasks.
Take insights anywhere with Genie One on mobile
Genie One mobile apps for iOS and Android let business users ask questions of company data, view dashboards, access Databricks Apps, and use conversational agent capabilities away from a desk. Answers draw on Genie Ontology, Genie Agents, enterprise governance, and connectors to Google Drive, Microsoft 365, and Atlassian, while respecting source permissions. AI/BI Dashboards reflow widgets into a single column in portrait mode and preserve their designed layout in landscape. Authentication uses the existing OAuth flow and identity provider, with MFA, conditional access, device posture, network controls, regional data handling, and workspace permissions carried over from the browser; there is no separate mobile backend or mobile-only endpoint. The app is available in Public Preview, with dark mode, push notifications, voice mode, and account-level access listed as planned capabilities.
Meet the first cohort of recipients shaping the future with the Uber Transit Innovation Fund
The Uber Transit Innovation Fund will provide up to $50,000 each to 20 US public transit agencies, municipalities, and regional authorities to pilot on-demand mobility programs, with $1 million available overall. Its first announced cohort includes agencies and authorities from San Antonio, Volusia County, Polk County, Belton, Monterey, South Bend, the Quad Cities, and San Juan. Proposed projects cover late-night overflow rides, microtransit and paratransit supplementation, airport connections, curb-to-curb service, modernization of taxi vouchers, subsidized rides for opioid-use-disorder treatment, food-access mobility, and first-mile/last-mile trips to 16 rail stations. Applications remain open through June 30, 2026, with proposals reviewed on a rolling basis until funds are fully allocated.
Foundational context: Cross-industry & function-specific accelerators for Lakebase
Databricks presents Lakebase as a fully managed, serverless, standard Postgres database for combining operational and analytical workloads on its Data + AI Platform. The platform separates compute from storage, integrates with the lakehouse through Synced Tables and Lakebase CDF, and uses Unity Catalog for governance; copy-on-write branching and autoscaling to zero are described as core infrastructure primitives. The post showcases partner-built, ready-to-deploy accelerators spanning technology, finance, marketing, sales, supply chain, human resources, customer service, and operations. Examples include PostgreSQL migration assessment, multi-agent Genie orchestration, stateful enterprise agents, autonomous data reliability, governed contact-center intelligence, and project operations management. These offerings package Lakebase patterns into migration controls, domain-specific solutions, and agent frameworks intended to accelerate modernization and reduce transformation complexity.
NVIDIA Ising Decoding Cuts Color Code Logical Error Rates by Over 300x
Fault-tolerant quantum computing requires effective quantum error correction, but color codes have historically been constrained by difficult decoding demands. NVIDIA introduced the Ising Decoder ColorCode 1 Fast, a pre-decoder designed to accelerate and improve color code logical error rates. The framework uses a 3D Convolutional Neural Network trained via cuStabilizer in NVIDIA cuQuantum alongside PyTorch to handle localized error syndromes. In benchmark testing at code distance d=31 with a 0.3% physical error rate, the decoder achieved over 347.7x lower logical error rates and 7.3x faster runtime compared to the Chromobius decoder. These local, space-time predictions scale to arbitrary distances, supporting parallel blockwise decoding for real-time quantum algorithm execution.
Claude for Creative Work
Claude for Creative Work describes a set of connectors and workflows intended to help creative professionals expand ideation, reduce repetitive work, and take on larger-scale projects. The connectors connect Claude with tools including Ableton, Adobe Creative Cloud, Affinity by Canva, Autodesk Fusion, Blender, Resolume, SketchUp, and Splice, supporting documentation access, asset production, 3D modeling, live visual control, and sample search. The described uses also include tutoring, Claude Code scripts and plugins, cross-application pipeline work, Claude Design exploration with export to Canva, and batch processing. Blender has an officially available MCP connector that can analyze and debug scenes, batch-apply object changes, and add tools through Blender’s Python API; because it uses MCP, it is accessible to other LLMs. The post also describes access for students and faculty in three art and design programs, whose feedback will inform understanding of creative practitioners’ needs.
Anthropic Sydney office
Anthropic has officially opened a Sydney office and appointed Theo Hourmouzis as General Manager for Australia and New Zealand. Hourmouzis brings more than 20 years of technology leadership experience across Asia Pacific and most recently served as Snowflake’s Senior Vice President for Australia, New Zealand and ASEAN. He will lead the growing local team, shape a regional strategy for customers, and bring Claude into important work. The team will deepen relationships with enterprise customers and AI for Science research partners while delivering on commitments in a memorandum of understanding with the Australian government. Local partnerships include Canva, Xero, and YMCA South Australia, where custom Claude AI skills turn operational data into actionable insights, reduce branded content production from hours to minutes, and bring previously outsourced technical work in-house.
Introducing Feature Views
Feature Views are presented as a framework for creating managed feature pipelines in Databricks, addressing duplicated logic, training/serving skew, backfills, online-store plumbing, and governance for real-time ML. A Feature View defines the source, entity, time-series column, and computation once; Databricks uses it to generate point-in-time-accurate historical data, then materializes pipelines for batch or streaming production use. Registered in Unity Catalog, materialized features receive lineage and access controls, while MLflow records model dependencies and Model Serving retrieves required features without custom lookup code. For Kafka-sourced streaming features, Spark Realtime Mode, Lakebase, and Model Serving support rolling-window updates with reported end-to-end p99 latency of 200ms from event to online availability. The announcement says Feature Views are in Public Preview and can be developed through the Feature Engineering Client SDK and Genie Code, with streaming materialization requiring an Enterprise-tier workspace in a region supporting Lakebase.
ClickHouse on Docker Hardened Images
ClickHouse is now available as Docker Hardened Images (DHI), stripped-down builds for the ClickHouse server, ClickHouse Keeper, Kubernetes Operator and Helm chart, and metrics exporter. The standard image’s Ubuntu 22.04 base includes unused packages such as Perl, wget, and apt, whose known CVEs can block enterprise deployments even though ClickHouse does not use them. DHI replaces that base with a minimal image that has no package manager or network tools, runs as non-root, provides SLSA Level 3 provenance, and is maintained for CVE patching; Docker Scout reported 0 medium and 14 low findings for DHI, versus 8 medium and 11 low across 111 packages in the standard image. Migration preserves volumes and configuration, while development tooling remains available through a dev variant or temporary docker debug attachment.
One Driver, One Format, Every Language: ADBC
ClickHouse announces an official ADBC driver, a modern Arrow-native alternative to ODBC and JDBC for analytics and AI applications. It provides end-to-end columnar movement: query results leave ClickHouse as Apache Arrow and arrive in applications without a row-oriented conversion or staging step. A single Rust-built implementation connects through ClickHouse's HTTP interface, supports TLS, and serves languages and tools with ADBC bindings, including Ruby, R, and C. Users can install precompiled, signed binaries through the ADBC Driver Foundry and connect with a clickhouse:// URI; dbt Fusion distributes the driver directly. The driver also supports streaming Arrow inserts, while schema discovery, improved bulk ingest, usage examples, and JSON support remain on the roadmap.
The ambulatory intelligence gap
Health systems face an ambulatory intelligence gap: access, provider capacity, referral retention, panel management, and financial performance are interconnected, while information remains scattered across disconnected systems. Health Catalyst’s Ambulatory Intelligence combines AI with nearly two decades of healthcare improvement expertise, deploying directly in a customer’s Databricks workspace so sensitive data stays within the health system’s environment. It uses a medallion-based semantic layer, Unity Catalog for governance, Lakebase for low-latency serving, and Genie alongside dashboards to help leaders investigate why metrics change. The solution ships with prebuilt metrics across Access Optimization, Revenue Intelligence, Panel Management, and Referral Insights, plus cross-domain scorecards and configurable terminology and workflows. Reported outcomes from supported improvement work include increased revenue and encounters at Thibodaux Regional, 55,000 closed care gaps at INTEGRIS Health, and higher outpatient visits with fewer cancellations without reschedules at WakeMed; future plans include models based on prior outcomes and agentic capabilities.
UST is bringing Claude to physical AI
UST is partnering with Anthropic to bring Claude into engineering and production systems used by semiconductor, automotive, manufacturing, telecom, embedded, and IoT companies. Claude Code reads chip pinouts and hardware schematics, then writes and runs regression tests while carrying design context across hours-long tasks. UST is integrating Claude as the reasoning layer in its iDEC validation pipeline, which generates tests, compares live equipment data with a digital twin, and already cuts validation cycles from four days to 48 hours, 50 to 70 percent. The partnership also applies Claude to healthcare, telecom, and banking platforms, with human approval retained for recommended actions and response workflows. UST will train 20,000 associates worldwide and build specialized deployment teams, with governance and audit controls supporting production use in high-stakes industries.
Side Chats and Conversation Search
An update adds side chats for exploring tangents alongside a main agent conversation without interrupting it. Each side chat is a durable, full agent conversation with main-chat context, can be revisited, followed up on, and at-mentioned back into the main thread. In the Agents Window, users can search agent transcripts through Cmd+K using a local index designed to scale to thousands of conversations, or search within a conversation with Cmd+F. Redesigned project and repo pickers support creating projects and connecting GitHub, GitLab, or Azure DevOps without leaving the picker, with search scoped to the current location. New cloud agent hooks expose prompts, responses, thinking, subagents, compaction, and turn completion for observation and control.
The Long-Term Benefit Trust
Anthropic announces the Long-Term Benefit Trust (LTBT), a new governance structure intended to address the unusually large public externalities it associates with transformative AI. The independent Trust has five financially disinterested Trustees and, alongside Anthropic’s Delaware Public Benefit Corporation status, is meant to add accountability to the public interest without directing ordinary commercial decisions. Created through Class T stock after Series C, it can elect and remove board members, with authority phasing in through time- and funding-based milestones and reaching a board majority within four years; investors also receive a new directly elected seat. Protective provisions require notice of certain major actions, while amendment rules combine agreement-based changes with supermajority failsafes whose thresholds rise as the Trust gains power. Anthropic presents the arrangement as an experiment, with initial one-year Trustees selected after a year-long search and future Trustees elected by the Trustees.
Inviting hard questions
Anthropic is inviting the public to submit difficult questions about AI’s effects on jobs, society, families, science, medicine, and the future. The initiative responds to both optimism about AI’s potential and concerns about job loss, creative work, human agency, misuse, and whether benefits outweigh costs. Anthropic says its Public Benefit Corporation mission is to secure the benefits of advanced AI models and mitigate risks through safeguards, alignment research, free access for scientists, and a fellowship pairing early-career Claude users with nonprofits. It has gathered views through surveys of 52,000 Americans and 81,000 Claude users in 159 countries and 70 languages, plus focus groups and anonymized usage data, and will publicly track actions and shortcomings in addressing submitted questions. The Anthropic Institute and Long-Term Benefit Trust are also described as mechanisms for confronting societal challenges and providing oversight.
A new way to reflect on how you use Claude
The beta reflection feature helps Claude users review and refine their use of the assistant by tracking and visualizing activity over one, three, six, or twelve months. A dashboard summarizes key topics, usage patterns, frequent tasks, and when Claude is used, while periodically prompting users to consider what they should keep doing themselves; a future view will show time spent. It also assesses activity through the 4D AI Fluency Framework—Delegation, Description, Discernment, and Diligence—and can suggest practices such as using a Project for ongoing context, alongside quiet hours and break nudges. Available on Claude for web and the desktop app to Free, Pro, and Max users with memory enabled, the beta excludes incognito chats, connected-tool source files, and conversations tied to health integrations, with sensitive conversations represented only at a high level.
amazon.science
Capturing token IDs during agentic interactions for better reinforcement learning
Reinforcement learning for agentic tasks requires updating model parameters against the precise token sequences generated during rollouts. Standard agent harnesses frequently modify conversation transcripts through message compaction and tool formatting, causing retokenization drift that degrades policy-gradient training. Turnstile addresses this issue as a Rust-based proxy situated between agent harnesses and inference backends such as SGLang. By intercepting OpenAI-compatible Chat Completions traffic, the proxy records exact token IDs, log probabilities, loss masks, weight versions, and mixture-of-experts routing traces at the moment of generation. Validation across text-based coding and multimodal computer-use agents demonstrated steady reinforcement learning improvements without requiring modifications to agent harnesses.
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