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Announcement reads
Announcement posts on Yomu, summarised newest first.
- 1,477 posts
- 20 companies
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Announcement posts
NVIDIA NVLink: The Scale-Up Network for AI Factories
Scale-up networking has emerged as a critical architectural component for AI factories supporting trillion-parameter models, mixture-of-experts architectures, and disaggregated serving. NVIDIA presents its sixth-generation NVLink scale-up fabric and NVLink 6 Switch to address latency-sensitive all-to-all GPU communication and offload collective operations. In simulation benchmarks on models like DeepSeek-R1, Qwen 235B, and a simulated 2T parameter model, NVLink achieved up to 2.3X higher decode throughput than off-the-shelf Ethernet in a 72-accelerator domain. The architecture integrates NVLink-C2C to supply 1.8 TB/s of coherent CPU-GPU bandwidth in the Vera Rubin NVL72 platform, as well as NVLink Fusion for integrating semi-custom silicon XPUs. Through co-designed hardware, SHARP in-network compute, and NCCL libraries, the platform optimizes token throughput, factory uptime, and deployment economics.
Coding Agent Horror Stories: The Agent That Deleted Production
Recent industry updates focus on AI agent security, container maintenance, and developer workflows within Docker ecosystems. The OpenAI and Hugging Face incident involved 17,600 attacker actions, demonstrating that AI agent security requires automated system controls to constrain, observe, and govern agents rather than relying on human review. In software maintenance, Docker Extended Lifecycle Support (ELS) addresses escalating supply-chain risks and MinIO end of life by maintaining patches, building software from source, and enforcing local developer policies. For continuous integration, Docker Sandboxes enable isolated AI agents to run Testcontainers tests, implement code fixes, and submit draft pull requests within GitHub Actions. Additionally, Docker Verified Publisher applications have transitioned to a self-serve process accessible directly through Docker Hub.
Together AI and Y Combinator partner to launch the first dedicated GPU cluster for the YC community
Together AI and Y Combinator are partnering to provide YC portfolio companies with a dedicated GPU cluster, addressing compute access as a growing bottleneck for AI-native startups. The service is designed for both inference and training, letting founders reserve, provision, and manage GPUs through Together’s self-service portal rather than making long-term commitments or routing usage through YC. Startups can run short-term sprints at long-term rates, use capacity from single-node workloads to larger deployments, and receive GPUs in minutes, while the cluster is already running at full utilization and companies can plan needs months ahead. Together says its research on attention mechanisms and the Mamba architecture targets faster inference and improved unit economics. The partners plan to expand the cluster and support startups as their requirements grow.
Meta’s Spark Muse 1.1 is now available on Databricks, fully governed by Unity AI Gateway
Databricks announces support for Meta’s Muse Spark 1.1 through Model Provider Services (MPS) in Unity AI Gateway, addressing fragmented API keys, access controls, and usage visibility when organizations adopt new models. An MPS is a Unity Catalog securable that stores provider configuration and an encrypted API key, while callers use their Databricks credentials and the gateway attaches the key at request time. The post demonstrates registering Muse Spark through the OpenAI provider type with Meta’s API base URL and Responses API, then governing use with Unity Catalog privileges, model allowlists, policies, rate limits, usage metering, and inference tables. Requests are routed through the gateway, where access and guardrails are applied before reaching Meta; usage, spend, tokens, latency, status codes, and optionally full payloads are recorded for attribution and audit.
Improvements to Cursor in Slack
Cursor in Slack now gives users more control over tasks by presenting a plan before work begins and reporting status as it progresses. Its Slack presentation has also changed: compact footer links replace in-message buttons, while tables, pull requests, and artifacts render more cleanly. In named multi-repo environments, Cursor can select an environment with access to related frontend, backend, and shared-code repositories; if it needs another repository, it prompts for a switch and resumes the task afterward. Cross-channel workflows let Cursor read context from other channels and threads and send updates to the original thread or a relevant channel. Together, these changes support earlier redirection, broader repository access, and task coordination across Slack workspace conversations.
The skills gap behind agentic AI — and how Databricks is closing it with a new context engineer certification and agent trainings
Databricks introduced the Databricks Certified Context Engineer Associate beta exam to validate skills for building reliable, production-grade AI agent systems as organizations scale agentic AI. Context engineering is presented as the practice of curating, maintaining, and filtering tokens, memory banks, and tool parameters so an LLM can solve a task; the certification is intended to benchmark that technical skill set. Databricks also added AI Agent Fundamentals, Building Retrieval Agents on Databricks, and Agent Evaluation, covering agent reasoning, retrieval-augmented architectures, and systematic performance testing and improvement. Its AI-first certification-prep guide is available on every certification page, works with free tiers of major LLMs, includes guardrails and disclaimers about hallucinations, and shows candidates how to use Free Edition for hands-on practice; registration for the Context Engineer Associate is open, with the first exam scheduled for July 29, 2026.
How Unity Catalog managed tables bring interoperability, performance, and unified governance to the Lakehouse
Unity Catalog external access to Unity Catalog managed Delta tables is now in Public Preview, allowing external engines to create, read, and write while governance remains centralized. The announcement addresses the previous multi-engine trade-off: external tables enabled access but lacked managed-table performance optimizations and governance guarantees. Catalog commits coordinate writes through Unity Catalog, making it the source of truth for table state and enabling safe external writes, multi-statement transactions, and auditing of external operations. Predictive Optimization cleans storage, collects query statistics, and selects Liquid clustering columns as query patterns change; the post says these capabilities can deliver up to 50% storage cost savings and 20x faster queries. Support includes Spark, Flink, Starburst, DuckDB, and StreamNative, with open APIs and Delta Kernel extending integrations across Databricks UC and UC OSS.
Introducing Apache Spark 4.2
Apache Spark 4.2 extends the engine’s role in modern data and AI workloads with governed metrics, vector and top-K primitives, Arrow-first Python execution, native change data capture, and stronger streaming foundations. Metric views provide shared business definitions, while Spark Connect uses gRPC and Arrow to let remote clients submit logical plans without a full Spark runtime. Spark SQL adds vector similarity functions, NEAREST BY, geospatial types, sketches, and time-series features; Python interoperability includes Arrow UDFs and can move Spark DataFrames into supported Arrow-native tools without copying or serializing underlying data. Spark Declarative Pipelines adds Auto CDC for SCD Type 1 targets, while Data Source V2 standardizes change streams through CHANGES and expands row-level operations and schema evolution. The release also includes operational updates such as Web UI modernization, Kubernetes improvements, JDK 25 support, and dependency upgrades.
Unified Logs is now in open beta
Unified Logs enters open beta, bringing events from the API gateway, Postgres, Auth, Storage, PostgREST, Realtime, and connection poolers into one searchable dashboard view. Before this, debugging a single request required separate log pages, service-specific filters, and manual correlation across tabs, which slowed investigation and risked losing context. Users can filter by log type, level, status, method, or pathname, search event messages, share URL-based filter states, stream new events live, and zoom into timeline ranges colored by log level. Clicking a row opens a detail panel that presents network timing, gateway, database query, and Auth blocks alongside raw JSON, while Log Drains forward events to external destinations. The feature is rolling out to all projects during open beta, with the classic Logs Explorer still available and feedback requested.
Evolving Spec-Driven Development: Conductor Now Supports Antigravity
Conductor, introduced as a Gemini CLI extension for spec-driven development (SDD), is evolving into a plugin that supports Antigravity CLI and other tools. The change moves project awareness from ephemeral chat logs into persistent, version-controlled Markdown artifacts while replacing strict command sequences with conversational creation and iteration of context, specs, and plans. Plugins package skills, rules, MCP servers, and hooks, and the Conductor Plugin can update project context and mark plan tasks complete as discussions progress, while preserving existing commands, specs, plans, shared configuration, and development tracks. The post reports a higher success rate on the most complex subset of TerminalBench tasks than a user not using SDD, and gives an Antigravity installation command: agy plugins install https://github.com/gemini-cli-extensions/conductor.
Expanding Choice in Gemini Enterprise Agent Platform: Introducing Grounding with Parallel Web Search
Gemini Enterprise Agent Platform now natively integrates Parallel Web Systems as a web-grounding provider, expanding developers’ choice of sources for real-time, verifiable information. Grounding with Parallel Web Search connects Gemini models to Parallel’s structured, LLM-optimized search results, combines the provider’s web index with Gemini’s prompt decomposition and synthesis, and adds precise citation annotations. The capability is available through the Gemini API and Agent Studio, and can be subscribed to through Google Cloud Marketplace with usage charged on an existing Google Cloud invoice; zero data retention is available for sensitive workloads. Developers can make programmatic calls at scale, extract and cache web data, post-process results with other LLMs, and build catalog enrichment, autonomous-agent, and multi-agent workflows. Setup requires subscribing, accepting the terms, reviewing pricing, and selecting Parallel Web Search under Agent Studio’s partner-grounding settings.
July 2026 newsletter
The July 2026 newsletter rounds up ClickHouse releases, community projects, benchmarks, engineering work, and upcoming events in real-time data warehousing. Version 26.6 adds hypothetical skip indexes, cascading refreshable materialized-view dependencies, experimental continuous queries, AI embedding functions, PNG output, CLI documentation, and geospatial improvements. A CostBench comparison running the full analytics path over 28 hours found better cost-performance for ClickHouse Cloud than Snowflake Interactive Tables, which required a continuously running refresh warehouse for pre-aggregated data. Other features cover conversational analytics with ClickHouse Cloud, LibreChat, and MCP; Postgres change-data capture through Debezium and Kafka; local agent queries with chDB; high-concurrency tuning; and Silk, whose fiber runtime targets lower I/O tail latency. Quick reads report ingestion, caching, security, and multi-tenant SQL developments.
Build a Multi-Camera 3D Tracking Application with NVIDIA DeepStream 9.1 Skills
NVIDIA DeepStream 9.1 introduces AutoMagicCalib and Multi-View 3D Tracking skills to resolve the limitations of single-camera 2D tracking and manual camera calibration in large-scale video analytics. Multi-View 3D Tracking projects detections into a shared 3D coordinate system, matching tracklets across cameras via MQTT and maintaining globally consistent object IDs. AutoMagicCalib automates camera intrinsic and extrinsic parameter estimation by analyzing object trajectories across video feeds, optionally using a Visual Geometry Grounded Transformer. These capabilities are integrated into 13 agentic skills that allow coding agents to set up, configure, and execute end-to-end vision AI pipelines from natural language prompts. Outputs include an on-screen display grid, bird's-eye view trajectories, and per-frame protobuf metadata streamed through Kafka.
Introducing Claude Tag
Claude Tag is a Slack-based way for teams to delegate work to Claude by tagging @Claude in shared channels, with access to selected tools, data, and codebases. Claude breaks requests into stages, uses permitted tools, replies in threads, retains relevant channel context, and can pursue scheduled tasks asynchronously; ambient behavior can also surface information and follow up on unresolved work. Administrators scope Claude identities, memories, tools, and information by channel, set organization- and channel-level token-spend limits, and review activity logs, while users can also message Claude privately. Available in beta to Claude Enterprise and Team customers, Claude Tag replaces the existing Claude in Slack app, works with Opus 4.8, and is credited with creating 65% of the product team’s code through Anthropic’s internal version.
Agents for financial services
Anthropic is releasing ten ready-to-run agent templates for financial services, targeting work such as pitchbook creation, KYC screening, and month-end close operations. Each template packages task-specific skills, governed data connectors, and subagents, and is available as a plugin for Claude Cowork or Claude Code, or as a cookbook for Claude Managed Agents. Claude add-ins for Excel, PowerPoint, Word, and, soon, Outlook carry context between applications, while new connectors and a Moody’s MCP app provide access to market, research, credit, and company data. The announcement says the updates pair best with Claude Opus 4.7, which leads Vals AI’s Finance Agent benchmark at 64.37%, and states that templates are available today through the financial services marketplace.
Inkling model from Thinking Machines Lab now on Databricks
Databricks announces that Inkling, Thinking Machines Lab’s first open-weights model, is available to enterprise customers through the Unity AI Gateway. The model is positioned for coding and agentic reasoning workflows, supports multi-modal inputs, and can be applied to enterprise data, including proprietary codebases, internal documentation, and domain-specific data. Unity AI Gateway provides centralized security, permissions, audit logging, policy enforcement, cost controls, budgets, and observability, while data remains within the governed environment; Inkling is invoked through a REST API, with SQL query support planned. Teams can try it in AI Playground, deploy a governed endpoint, connect coding agents such as Cursor, OpenCode, or Pi, and build agents with Agent Bricks, with open weights enabling customization and inference-cost optimization without per-token API pricing.
Building Faster Cryptography with Carryless Multiplication in NVIDIA CUDA 13.3
NVIDIA CUDA 13.3 introduces clmad, a native PTX carryless multiply-accumulate instruction for Ampere and newer GPU architectures. Prior to this release, GPUs lacked dedicated hardware for carryless multiplication, requiring developers to emulate binary extension field arithmetic with ALU operations or bitsliced circuits. The new primitive performs 64-bit by 64-bit carryless multiplication into 128-bit results with accumulation, allowing full 128-bit field multiplications via Karatsuba in six instructions. Benchmarks demonstrate that clmad achieves up to 6.3 TB/s throughput for GHASH on the NVIDIA B200, representing an 18.8x improvement over bitslicing. Additionally, sum-check protocol latency in zero-knowledge proving workloads decreases by up to 13x on the B200 and 4x on the RTX 5090.
Anthropic commits $10 million to Canadian AI research
Anthropic is committing $10 million CAD to Canadian research institutions to support beneficial and responsible AI applications, building on Canada’s contributions to neural networks, deep learning, and reinforcement learning. The announcement names partnerships with Amii, Mila, Vector, CHEO, CAMH, Université Laval, University of Toronto, and the University of Saskatchewan, with Claude credits supporting work spanning trust and safety, health, scientific discovery, mental health, robotics, sustainability, and public services. Amii, Mila, and Vector will also join Anthropic’s for Startups program, providing affiliated Canadian startups with at least $5,000 USD each in API credits. A Canadian country brief based on the March 2026 Anthropic Economic Index reports that Canada ranks eighth in Claude.ai use, accounts for 2.6% of global consumer use, and ranks second by per-capita adoption; usage is highest in British Columbia and closely follows professional, scientific, and technical employment.
Expanding the Uber Transit Innovation Fund: meet our next cohort of recipients
The Uber Transit Innovation Fund responds to reduced transit funding by supporting public agencies and partners pursuing projects that keep communities moving. Launched with a $1 million commitment, the fund initially offered up to $50,000 to each of as many as 20 forward-thinking US transit agencies, municipalities, and regional authorities. Strong interest from US and Canadian agencies led to an expanded investment and eligibility, following an initial cohort of nine winners. The additional 26 recipients bring total awarded funding to $1.7 million, or $1,705,000, with projects spanning microtransit in Oregon, food access in Richmond, Virginia, and transportation gaps across urban, suburban, and semi-rural communities. Additional funding and recipients are scheduled to be announced by the end of the month.
Inkling by Thinking Machines now available on Modal
Thinking Machines released Inkling, a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. The 975B-parameter mixture-of-experts transformer activates 41B parameters per token, uses a 1M-token context window, and employs local attention: five sliding window layers for every global layer. Modal provided day-zero support with a custom DFlash block-diffusion speculator, achieving 250 tokens per second per user on 8x B200s with 67% faster interactivity than the model’s built-in multi-token prediction path. The DFlash drafter uses all local attention and causal layers, keeping cost flat as block size grows. Inkling is available today as an OpenAI-compatible shared endpoint through Modal Auto Endpoints with token-based pricing, allowing users to try the model interactively.
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