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Announcement reads
Announcement posts on Yomu, summarised newest first.
- 1,547 posts
- 20 companies
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Announcement posts
NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running Agents
NVIDIA Nemotron 3.5 Lightning is an open 30B mixture-of-experts model with 3B active parameters, designed for the high-volume execution layer of long-running autonomous agents. The post positions it alongside frontier reasoning models: larger systems handle planning and orchestration, while Lightning handles tool calls, result validation, subagent delegation, and other routine work. Its design combines harness-optimized training, multi-token prediction for speculative decoding, DFlash and DSpark draft models, and NVFP4 quantization alongside BF16. The model is customizable through LoRA or full SFT, reinforcement learning, and environment-based evaluations, with weights, training data, and recipes released under OpenMDW-1.1. The post reports up to 4x the output speed of similar-sized models, 86% accuracy on PinchBench, and completion of 10,000 tasks 30% faster than Qwen3.6 35B at similar accuracy, while NeMo Switchyard routes work among models.
What's new in Sentry Logs: The summer 2026 roundup
Sentry introduced a collection of updates to its logging platform aimed at improving search scalability, export flexibility, and general viewer usability. The system now supports URL-synced log pinning alongside an overhauled export interface that handles aggregate data, full attribute payloads, and high-volume JSONL formats across Logs, Errors, and Tracing. Performance enhancements to the log viewer include a more constrained layout with reduced initial data loads, inline error representations, individual chips for multi-value search terms, and trace-level filtering. To assist large-scale queries spanning hundreds of gigabytes or terabytes, frontend and backend modifications added longer query timeouts with continuations, clearer rate-limiting messages, and in-place retries.
Electric joins Databricks to bring WASM Postgres to AI agent sandboxes
Databricks says Electric is joining the company to extend its Postgres capabilities from the lakehouse to AI agent sandboxes. The post frames agentic applications as requiring both durable governed state and rapidly changing local context: agents choose data at runtime, operate in sandboxed environments where cloud databases are reached over the network, and collaborate while working in parallel. Electric’s PGlite is a lightweight Postgres database built for WebAssembly that runs inside an agent, application, browser tab, or device, while its real-time sync engine synchronizes distributed data with centralized Lakebase infrastructure. PGlite’s weekly downloads reportedly grew from 1 million to 13 million in twelve months, and the sync architecture is described as keeping agents’ shared context current. Combined, Lakebase, PGlite, and synchronization are presented as a Postgres-based foundation for collaborative agentic applications at production scale.
bitdrift brings ClickHouse-powered mobile observability to ClickStack
bitdrift has joined ClickHouse’s House Mates program as a mobile observability partner, bringing mobile-native telemetry, tracing, and debugging to ClickStack. ClickStack provides an open-source or managed observability stack built on ClickHouse, with HyperDX and an OpenTelemetry Collector for logs, metrics, traces, and OTLP ingestion. bitdrift’s Capture SDK supports iOS, Android, and React Native, stores unsampled telemetry in a fixed-size on-device ring buffer, and uses control-plane configuration to decide what is collected or uploaded. Shared OpenTelemetry trace context lets engineers follow a mobile request’s trace ID from a session timeline into ClickStack, where backend traces, dependencies, logs, metrics, database operations, and infrastructure behavior are available, creating a continuous debugging path across the device and distributed systems.
amazon.science
AWS Trainium Frontier competition: Co-design models and kernels on purpose-built AI chips
Modern language model architectures evolved around specific hardware constraints, but AWS Trainium chips alter memory and compute dynamics through on-chip SBUF memory, systolic matrix multiplication, and explicit DMA control. The AWS Trainium Frontier competition invites teams to train language models from scratch under strict wall-clock time constraints to uncover optimal architectures and custom kernels for Trainium silicon. Participants start from a fifty-million-parameter dense baseline and can modify everything from the model architecture and optimizer to custom Neuron Kernel Interface routines. Phase 1 evaluates thirty-minute single-chip training runs by validation bits-per-byte, while Phase 2 grants four-hour multi-chip server access to evaluate both training efficiency and reasoning on the CORE benchmark. Top finalists will present their findings at NeurIPS 2026, compete for cash prizes, and co-publish research with Annapurna Labs.
Introducing Claude Sonnet 5
Claude Sonnet 5 is introduced as an agentic Sonnet model that plans, uses browser and terminal tools, and operates autonomously. The post says it approaches Opus 4.8 performance at lower prices while improving on Sonnet 4.6 in reasoning, tool use, coding, and knowledge work. Comparisons on BrowseComp and OSWorld-Verified across effort levels show a wider cost-performance range, with higher-effort results matching Opus 4.8 on some tasks. Safety assessments found lower overall undesirable behavior, hallucination, and sycophancy rates than Sonnet 4.6, while default cyber safeguards address dangerous usage; the model is available across all plans and via the Claude API for $2 per million input tokens and $10 per million output tokens.
Everything we launched during Agents Week
Agents Week introduced a series of products and primitives across Cloudflare to support an Agentic Internet where autonomous software and humans interact. Developing capable agents requires addressing technical challenges spanning identity, communication, orchestration, memory, observability, and security. Cloudflare introduced the Agent Development Lifecycle alongside tools like Cloudflare Agents for live tracing, replay, and human-in-the-loop approvals. The releases also included programmable CI/CD pipelines capable of repairing failures, Cloudflare Wallets for agent transactions, and Kitesurf, an agent-first browser running in V8 isolates. New protocols and tools like WebMCP, MCPv2, and Cloudflare AI Search aim to make web data and applications readable, discoverable, and searchable by autonomous software.
Whats new in ClickStack - June + July
ClickStack’s June–July releases add a broader set of observability features, with work centered on trace navigation, metrics sources, dashboards, and filter performance. The trace waterfall now assigns stable colors by service, keeps span durations outside bars, preserves clickable proportions for very short spans, and adds a minimap plus OpenTelemetry span-link navigation. Metrics support includes quantiles for exponential histograms, a leaner default schema, and an experimental UI connection to external Prometheus-compatible endpoints, while PromQL is proxied to the configured endpoint. Dashboard filters can narrow one another, event patterns can appear as tiles, kiosk mode provides a read-only display, and filter-value discovery prefers ClickHouse text indexes when available. Additional changes cover alert annotations, browser RUM dashboard views, external links from table tiles, grid snapping, and service-map coloring by latency, error rate, or throughput.
Build Low-Latency Multilingual Voice Agents: Open Weights & Full Deployment Control with NVIDIA Magpie TTS
NVIDIA Magpie Multilingual TTS is a 364M-parameter open-weights model for multilingual voice applications, supporting 12 languages, including newly added Modern Standard Arabic, Korean, and Brazilian Portuguese. It is positioned for cascaded voice-agent architectures in which ASR, TTS, and LLM components are independently deployed, tuned, and scaled on controlled infrastructure. Frame stacking predicts two audio frames per decoding step, while a local transformer models dependencies between simultaneously generated codebook tokens; NVIDIA reports 32–79 ms single-stream TTFA across its listed GPUs. Compared with the previous release, French and Spanish CER improved to 1.54% and 0.60%, while SSIM reached 0.747 and 0.793; German SSIM also increased to 0.742. The Hugging Face checkpoint supports research and fine-tuning, while NVIDIA NIM provides optimized production serving and NeMo supports customization.
Introducing FILE type: a native column type for multimodal data
The beta of FILE introduces a native column type for storing unstructured data such as documents, images, audio, and video alongside structured table data. FILE applies fine-grained governance, row- and column-level controls, ABAC, and lifecycle synchronization, so deleting a row also deletes the corresponding binary in object storage. Its lightweight pointers fetch file content only when a query requires it, while SQL, Python UDFs, built-in AI functions, and materialized views support multimodal processing. The example uses sampled dashcam frames and object detection to identify stops with no hazard, then joins extracted results with trip metadata; the announcement also describes connectors, incremental pipelines, and an open effort around Parquet, Delta Lake, Apache Iceberg, and Apache Spark.
Run Local Agentic AI Workflows with Meta’s Muse Glimmer on NVIDIA
Meta has released Muse Glimmer, a 30B open-weight dense model with a 120K+ context window aimed at local agentic AI workflows. The model uses a dense architecture that activates every parameter per token, avoiding routing overhead and targeting reliable instruction following, long-context coherence, and predictable latency for multi-step agent tasks. On NVIDIA Blackwell Ultra hardware, Muse Glimmer reports over 20K tokens/sec/GPU at BF16/NVF4 precision, with the full model fitting in a single GPU's VRAM. Deployment options span NVIDIA GeForce RTX 5090, DGX Spark, DGX Station, and Jetson edge devices, supported by vLLM, SGLang, and NVIDIA NIM containers. Fine-tuning is available through NVIDIA NeMo AutoModel for SFT and LoRA, with NeMo RL for reinforcement learning, and the NemoClaw harness enables long-running assistants in a sandboxed environment.
Serving the most critical missions: Cloudflare for Government achieves FedRAMP Class D (High) Certified status
Cloudflare for Government has achieved FedRAMP Class D (High) certification status, following its FedRAMP Moderate authorization in 2022 and sponsorship by the National Institute of Standards and Technology. FedRAMP High covers systems handling the nation’s most sensitive unclassified data, including law enforcement, emergency services, financial systems, and national security information, where a control breach could be catastrophic. Cloudflare says its offering runs the same software stack and services as its commercial platform on one global network, while its Data Localization Suite ensures traffic inspection and processing for FedRAMP High occur exclusively in U.S. data centers. The systems developed for FedRAMP High are also the foundation of Cloudflare’s stated pursuit of DoD Impact Level 4 authorization, with access to its Zero Trust, application performance, and developer features as they are released.
Meta is back with Muse Glimmer: local, agentic, multimodal, and open source
Meta’s Muse Glimmer is a 30B-parameter multimodal model released under the Apache 2.0 license for local, agentic applications including coding, document analysis, and personal assistants. Its dense architecture combines a 2B ViT-style perception encoder with a 28B text decoder, while an optional DFlash speculative-decoding drafter targets faster generation for structured content. The decoder uses alternating sliding-window and full-attention layers, grouped-query attention, and query-key normalization with extra query scaling. Day-zero support is available in Transformers, llama.cpp, vLLM, Inference Endpoints, and other libraries, with examples for text, images, videos, deployment, and agent workflows. Published benchmarks show varied results: Muse Glimmer leads on MCP Atlas and SWE-Bench Pro, while Qwen3.6 leads on OSWorld-Verified and SWE-Bench Verified.
Introducing Radar Researcher: An AI tool for exploring Internet data in plain language
Cloudflare launched the beta of Radar Researcher, an AI tool designed to let users explore global Internet traffic data using plain-language queries. Built entirely on Cloudflare's developer platform, the system uses the Cloudflare Agents SDK within a Cloudflare Worker, maintaining conversation state and SQLite databases inside Durable Objects. The inference layer relies on Workers AI running models like Kimi K2.7 through an ordered fallback chain across three model families to prevent outages, routed via AI Gateway for logging, caching, and guardrails. Radar's frontend renders retrieved data using its existing native interactive charts, while smaller dedicated models generate conversation titles and follow-up suggestions asynchronously. Additionally, Radar introduced support for the WebMCP standard, exposing imperative and declarative tools to allow external browser agents to navigate and interact with the platform directly.
Announcing Cloudflare Ambassadors, Community Engineers, and another $1M in open-source funding
Cloudflare introduced an updated community initiative featuring two tracks: Cloudflare Ambassadors and Cloudflare Community Engineers. The Ambassador program selects community leaders for terms lasting up to two years to organize events, lead student groups, and create learning spaces with company-provided credits, resources, and marketing assets. The Community Engineer track offers grants from a new one million dollar open-source fund spanning the next two years to support maintainers of projects such as Astro, Agents SDK, EmDash, Hono, and Vinext. To maintain its Discord server of nearly 100,000 members, a new Discord committee composed of staff and Ambassadors will guide community discussions while automated spam protections handle routine moderation.
Unifying Workers AI and AI Gateway into a single AI control plane
Workers AI and AI Gateway are converging into a unified AI control plane to simplify model access, billing, and observability. Developers can now access both services through a single entrypoint using unified Workers bindings and the /ai/ REST endpoint. Specifying a default gateway automatically instruments requests with payload logging, token tracking, and cost attribution without manual dashboard setup. Cloudflare has also rolled out unified prepaid billing, allowing teams to spend AI Gateway credits on Workers AI models while unlocking higher rate limits. Planned future features include model-first routing for automated provider failover and smart routing powered by task classifiers on Workers AI.
Supabase is now a connector on Perplexity Computer
Supabase is now available as a connector on Perplexity Computer, allowing the agent to use a Supabase project as a persistent data layer across runs. Computer can read from and write to Postgres tables, answer questions by running queries, retrieve user and payment data for support debugging, and invoke Edge Functions with test payloads. Recurring queries can be scheduled, with results posted to Slack, while workflows can combine Supabase data with Stripe events and route fixes through GitHub pull requests. The announcement presents the connector as a way to keep database work inside Computer’s multi-step workflows across more than 400 apps, without custom glue code.
Uber Advertising Launches New Marketing Solutions for Restaurants
Uber Advertising is introducing Deal Drops and Reorder Rewards, two marketing solutions for restaurants designed around customers’ real-time ordering decisions. Deal Drops let participating restaurants run special offers for one hour before select cultural events, with in-app and push notifications, home feed placement, a dedicated hub, and optional inventory limits that add urgency. Reorder Rewards give customers a surprise reward after an order for their next purchase, keep it visible for a limited time, and apply it at checkout without extra steps. Uber says early global results showed restaurants using Reorder Rewards saw about a 5.5% average sales increase compared with what they would have earned without them, based on aggregate advertiser data from September through November 2025, not a guarantee. Together, these tools target high-intent event decisions and post-purchase re-engagement, with the stated aim of turning attention into action and one-time orders into stronger customer relationships.
What's new in ClickHouse Managed Postgres: Customer notifications, better observability, faster backups, extensions, and more
ClickHouse reports a set of updates to its Managed Postgres beta, aimed at simplifying onboarding and advancing its unified Postgres-and-ClickHouse data stack. The redesigned flow supports sample-data loading or PostgreSQL migration through ClickPipes, built-in SQL exploration, replication to ClickHouse, and cross-system queries through pg_clickhouse; services also notify users when disk usage remains above 85%, with console, email, and optional Slack channels. Observability now includes searchable PostgreSQL logs, CPU, memory, disk, IOPS, connection and per-table metrics, query insights, a Prometheus-compatible /metrics endpoint, and an OpenAPI slow-query API. Backups use more CPU and disk throughput on larger servers, making backup and restore times predictable even on 10+ TB servers, while extensions add pg_re2 and improvements to pg_clickhouse and pg_stat_ch, including regex pushdown, Arrow batching, sampling, and safer worker exits; Google Cloud Platform availability and further maintenance, configuration, and integration work are planned.
Kimi K3 from Moonshot AI is now available on Databricks through Unity AI Gateway
Kimi K3 from Moonshot AI is now available on Databricks through the Foundation Model API and Unity AI Gateway, giving customers native access to an open-weight model alongside proprietary models from Anthropic, OpenAI, and Google Gemini. The announcement positions Kimi K3 as a frontier-quality option for coding, agentic reasoning, and document understanding, with deployment flexibility, customizability, and zero data retention coverage. Databricks says the model runs against governed enterprise data through Unity Catalog, while Unity AI Gateway provides permissions, safety guardrails, audit trails, spend controls, routing, and a standardized REST API within Databricks’ security perimeter. The post cites a score of 57 and rank of #4 in Artificial Analysis’ Intelligence Index, internal benchmarks showing performance at the level of leading proprietary models, and 50–72% lower cost per task; availability includes US hosting for AWS and GCP workspaces.
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