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Announcement posts on Yomu, summarised newest first.
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Announcement posts
Introducing the unified Supabase .NET SDK
The Supabase .NET SDK v8 is now available, consolidating all packages under a single repository with a unified version number. Previously, packages like Gotrue, Realtime, and the main package were scattered across multiple repositories with separate versions and lifecycles, making upgrades complex. From v8.0.0, all packages—Gotrue, PostgREST, Realtime, Storage, Functions, and the umbrella package—share the same version, and previous repositories are archived as read-only. The jump from v1.x.x to v8.0.0 was necessary because Realtime was already on v7.x.x, and this ensures lockstep versioning going forward. A new dependency injection extension package registers the Supabase client with the .NET service container and lets IHttpClientFactory manage its HttpClient, starting at v8.0.0 as well. The update switches serialization from Newtonsoft.Json to System.Text.Json, adds configurable retry with backoff, accepts publishable and secret API keys, and targets netstandard2.1, with a migration guide available for breaking changes.
Geospatial queries are up to 38X faster with Lakehouse//RT
Databricks announced that Spatial SQL and spatial joins now run on Lakehouse//RT, delivering up to 38X faster performance under high concurrency compared to traditional cloud data warehouses. The platform supports GEOMETRY and GEOGRAPHY data types, over 100 spatial functions, and high-performance spatial joins, all within the open lakehouse using Apache Spark and Parquet files. Organizations in insurance, agriculture, energy, telecom, retail, and logistics can consolidate geospatial workloads onto a single governed platform, eliminating separate stacks like PostGIS. Benchmarks using SpatialBench show Lakehouse//RT is 3X faster on average and up to 20X faster for single-query concurrency, with no code changes required. The service is available in Beta, allowing users to run existing spatial queries directly on Delta and Iceberg tables without copying data.
Offering expanded Claude access across all three branches of the U.S. government
Anthropic is offering Claude for Enterprise and Claude for Government to all three branches of the U.S. government for $1, removing cost barriers to AI adoption. The offer builds on Claude’s addition to the GSA schedule and supports FedRAMP High workloads for sensitive unclassified data. Government partners already using Claude include the Department of Defense, Lawrence Livermore National Laboratory with 10,000 daily users, and the District of Columbia Department of Health. The package includes frontier models with continuous updates and technical support for one year, with coordination underway with GSA for implementation. This expanded access aims to accelerate AI adoption across the federal government for scientific research, national security, and constituent services.
Granite Switch 4.1: Chat, Decisions, and Speech from One Endpoint
Granite Switch 4.1 models combine chat, general and specialized decisions, and speech-to-text from a single hosted endpoint. The 8B and 30B models include a new Decision aLoRA for general-purpose decision-making and a Guardian aLoRA that outperforms Jev 1.13 and other closed models on safety tasks. They also embed Granite-Speech-5.0-Turbo-CTC, the fastest ASR model on the OpenASR Leaderboard, enabling efficient transcription. All components are composed into one model using cache-efficient Activated LoRAs, taking up about the same GPU VRAM as the base model alone, and the KV cache from base generation can be reused for any subsequent adapter decisions. Granite Switch 4.1 can switch between chat, decision, and spoken requests quickly, and a 3B variant achieved 60-100 decisions per second playing Doom, compared to Jev’s 10, while also narrating and answering questions.
Scale Agentic Coding Workflows with Meta’s Muse Spark Open on NVIDIA GB300 NVL72
Meta launches Muse Spark Open, an 816B-parameter mixture-of-experts model with 42B active parameters and a 1M token context window, trained on NVIDIA GB300 NVL72 for agentic coding workflows. It plans, acts, observes, and self-corrects across multistep tasks like multifile refactoring, using tool calls and subagent coordination while holding an entire project in context without breaking it into pieces. Its 1.8B perception encoder natively processes interleaved text, images, video frames, and audio in a single pass. On GB300 NVL72 with speculative decoding, it achieves over 50K tokens per second per GPU and over 600 tokens per second per user. Sparse MoE routing activates only 42B parameters per token, and NVFP4 quantization reduces memory footprint for larger KV cache. The model is available via Hugging Face and NVIDIA NIM, with support from SGLang, vLLM, and TensorRT LLM for serving, and fine-tuning through NeMo AutoModel and Megatron Bridge.
An update on independent evaluators
Anthropic has begun providing employee-like access to METR, a nonprofit evaluator of frontier AI models, as part of its commitment to independent third-party assessment. The company signed the White House Accord on Super Intelligence and aims to close the gap between what evaluators and the company itself understand about its systems. To evaluate frontier models, evaluators require expertise in capabilities assessment, independence, deep access to training and safeguards, and freedom to publish results. Anthropic defined three tiers of access: the first tier allows publication only with company approval, the second tier permits publication without approval but with possible redactions, and the third tier grants employee-like access with similar publication rights as the second. METR is currently at the third tier, while SecureBio operates at the first tier, and collaborations with Accenture and Faculty are in early stages.
A fully programmable secure web gateway
Traditional secure web gateways enforce security through a binary allow, block, or log model, which limits customization for complex environments. Cloudflare is integrating its Cloudflare Gateway and Data Loss Prevention with Cloudflare Workers, allowing customers to run arbitrary code inline at the moment a security decision is made. The new Forward action, available in public beta, routes matched HTTP traffic to a Worker that acts as inline middleware, enabling request and response modification or asynchronous tasks like sending Slack messages. Custom Functions, arriving before the end of the year, call a Worker during policy evaluation and return values used as selectors, enabling data-conditional rules and DLP validation against external APIs. Because Gateway and Workers run on the same machines in Cloudflare’s global network, dispatching traffic to a Worker is a local function call with no hairpinning or added latency.
Sapienz Receives ISSTA 2026 Impact Paper Award
Sapienz, a multi-objective automated testing system for Android, received the ISSTA 2026 Most Influential Paper Award for its 2016 research by Ke Mao, Mark Harman, and Yue Jia. The system combines three goals: exploring more of an app, finding more crashes, and reducing the steps needed to trigger them, making failures easier to investigate. In an evaluation of 1,000 popular Google Play apps, Sapienz found 558 previously unknown crashes. Within 17 months of publication, it was deployed at Meta, where it now runs millions of tests daily, surfaces thousands of unique crashes each week, and helps safeguard hundreds of app releases each month. Sapienz also became the app-exploration engine for dynamic taint analysis at WhatsApp using synthetic conversation test accounts.
Google Summer of Code 2026 With the Kotlin Foundation: Recap
The Kotlin Foundation wrapped up its fourth Google Summer of Code program in 2026, supporting two projects by Ayako Hayasaka and Mark Seif Khalil. Ayako implemented tail-call support in the Kotlin/Wasm backend, using return_call and return_call_ref instructions to avoid stack growth in recursive programs, and then found and fixed a missing optimization for return_call in the V8 engine that caused a performance regression. Mark built a database and automated pipeline to collect and structure scattered information about where Kotlin is taught worldwide, covering courses, curricula, repositories, and learning platforms. In Ayako’s benchmarks, several recursive patterns achieved 1.7 to 4.3 times faster execution at a recursion depth of 10,000, and at a depth of one million, the same programs crashed without tail calls but completed successfully with them. The education landscape project now provides a reusable foundation for monitoring Kotlin’s presence in education and identifying trends and gaps.
Optimized support for Open NVIDIA Nemotron 3.5 Super VL
Red Hat AI released optimized FP8 and DSpark speculative decoding checkpoints for the Nemotron 3.5 Super VL 120B multimodal mixture-of-experts (MoE) model. These checkpoints are designed to double inference throughput on NVIDIA Hopper GPUs without compromising output accuracy. FP8 quantization uses LLM Compressor to reduce model weights and activations from BF16 to 8-bit floating point, cutting memory and storage requirements in half while preserving quality. DSpark speculative decoding proposes 8-16 tokens in a single forward pass and uses a lightweight Markov head to introduce dependencies between speculated tokens. Benchmarks on HumanEval, math_reasoning, and RAG datasets show that each technique delivers meaningful gains individually and combining them outperforms any intermediate configuration at every concurrency level.
NVIDIA Nemotron 3.5 Super VL Powers Specialized Agents with Multimodal Reasoning
NVIDIA released Nemotron 3.5 Super VL, an open 120B-parameter multimodal model for specialized enterprise agents. Its mixture-of-experts architecture activates 12B parameters per token, balancing capacity and inference efficiency. The model combines reasoning, instruction following, tool use, and understanding of text, images, documents, and video for tasks including coding, web research, document intelligence, and cybersecurity. On a single GPU, it achieves up to 5x higher throughput for agentic tasks than competing open models, and it scores higher than the larger Nemotron 3 Ultra on Hermes Bench and LangChain evaluations. Early adopters report cost reductions of 40x for credit processing and 10x lower latency for receipt parsing, and organizations can post-train the model with NVIDIA NeMo for domain-specific workflows and deploy it with NVIDIA NIM and Dynamo, which serves up to 8x more users with 85% lower first-token latency.
Deploy with Void
VoidZero has open-sourced Void, a toolkit for building and deploying full-stack Vite applications to Cloudflare. The release includes three products: Void Platform, Void SDK, and Void CLI, which integrate into a single Vite workflow. The SDK provides file-based routing, database integration via imports, and end-to-end type safety from the database to the user interface. The CLI handles building, migrating, provisioning resources, and deploying with a single command, automatically inferring bindings like databases or AI services. The Platform offers a shared deployment environment with access controls and resource limits, enabling teams to prototype and ship internal apps in minutes.
IntelliJ IDEA’s Java and Kotlin for Cursor and VS Code Hits 1.0
IntelliJ IDEA’s Java and Kotlin language support extension has reached its 1.0 stable release, now available for VS Code and Cursor editors. The extension is powered by IntelliJ IDEA’s language server and debug adapter, providing smart code completion, navigation, code analysis, refactoring, and build tool support for Maven, Gradle, and Bazel. It also offers powerful debugging capabilities including emulated breakpoints and async stack traces, along with JUnit test runner support. The 1.0 release resulted from close collaboration with the Cursor team, making Java and Kotlin a first-class experience in Cursor’s Agents Window with dedicated support for nullability and Spring Framework specifics. Starting with 1.0, the extension requires an IntelliJ IDEA Ultimate subscription but provides a 30-day free trial, and future updates will enable agents to call the LSP directly for faster AI-assisted development, with announcements pending for purely agentic licensing.
Cloudflare and OpenAI: making AI discovery better for everyone
Cloudflare and OpenAI launched a research pilot in July 2026 to help ChatGPT users discover content more efficiently using network signals about page existence changes and potential harm. The pilot ran on a cohort of about one percent of Cloudflare’s network and has enabled OpenAI to index hundreds of millions of new web pages that are useful but sparsely linked. Cloudflare provides these discovery signals to OpenAI’s crawler while OpenAI independently evaluates and ranks pages by relevance and helpfulness for ChatGPT Search. The collaboration also tests signals that flag harmful pages like fake login forms so crawlers avoid them without penalizing the legitimate domain. The next phase will refine page selection test faster change signals and measure business outcomes for site owners whose content is discovered.
October 2026 newsletter
The October 2026 ClickHouse newsletter announces CostBench results where ClickHouse Cloud delivered 752× better performance per dollar than Databricks and 438× to 512× better than BigQuery on real-time analytics workloads. The 26.9 release extends LIMIT with AFTER and UNTIL boundary conditions, adds incremental refresh for materialized views, and introduces CREATE TOKEN for time-limited credentials. Executable user-defined functions are now generally available on ClickHouse Cloud, supporting precompiled Rust, Go, C++, and JavaScript binaries with outbound network access and a deterministic flag. Featured community member Hoang Minh V has had over 100 pull requests merged since May, including a fast path for PromQL aggregations and fixes to TimeSeries reads. The newsletter also covers the official Apache Airflow provider for ClickHouse, the chDB durable layer for agent memory, and a Postgres round-up on memory safety and local NVMe storage.
MPS 2026.2 Release Candidate Available
JetBrains has released the MPS 2026.2 Release Candidate, a language workbench for creating domain-specific languages, with several key improvements over the previous EAP1 build. The RC adds Linux ARM64 support with a bundled JetBrains Java Runtime and the ability to build standalone MPS-based applications for that architecture. Build Language now supports importing files from TAR archives, preserving symbolic links and file permissions, which is useful for packaging platform-specific plugins and native libraries. The Projectional Agent Toolkit receives a major performance boost with token usage reductions exceeding 50% in tests using Claude Code, plus new MCP tools for typesystem debugging, intentions, and quick-fixes. Archive packaging preserves symbolic links and uses .tar.gz for macOS to avoid duplicate native libraries, while the Import dialog now respects user selections and the Diff dialog has visual improvements.
Apertus 1.5 Release Notes
Apertus 1.5 extends the 8B and 70B open-weight models with image and audio understanding, a thinking mode, and a 262,144-token context window, released under Apache 2.0. The models are a continued pretraining of Apertus 1.0, adding 4 trillion tokens to the 8B model and 2 trillion to the 70B model, for totals of 19T and 17T respectively. Multimodality uses an early-fusion tokenizer with Emu3.5 for images and WavTokenizer for audio, keeping the architecture unchanged and training at text-only speed. Post-training includes SFT, long-context SFT, reinforcement learning with verifiable rewards, DPO, and charter alignment via SDPO, with all data and recipes publicly documented. Evaluation results show competitive performance on reasoning, coding, and multilingual tasks, with the 8B model matching GPT-OSS-20B on translation and outperforming Claude Haiku 4.5, reported transparently via the EvalEval Coalition’s EEE schema.
Sync real-time Stripe data to Supabase with Stripe Data Pipeline
Stripe’s Data Pipeline now adds Postgres as a destination, enabling developers to sync Stripe data directly into their Supabase project without maintaining webhooks or working around API rate limits. Setup occurs entirely in the Stripe Dashboard: under Reporting, Data management, users select Data Pipeline, choose Postgres and Supabase, and authorize the connection. Stripe backfills historical records and continuously syncs charges, refunds, disputes, customers, subscriptions, and invoices into a dedicated stripe_sdp schema in the user’s Postgres database. A dedicated stripe_sdp role with scoped permissions, JWT direct connections using short-lived tokens, and enforced SSL ensure the database password is never exposed to Stripe. Developers can then query the synced tables with standard SQL, join them with application data, and implement features like MRR calculations, churn analysis, or subscription-based feature gating without extra API calls.
Unlock powerful insights and forecasts with Predictive AI Functions
Data teams frequently field questions about future sales, metric movements, and anomalous data, but answering them typically requires data science teams to build and manage models, creating a bottleneck. Predictive AI Functions, now in Beta, remove that bottleneck by providing SQL-native functions for forecasting, driver analysis, and anomaly detection, backed by state-of-the-art ML models maintained centrally by Databricks. The launch includes ai_forecast for time series projection, ai_top_drivers for segment driver analysis, ai_detect_anomalies for outlier detection, and ai_predict_value and ai_predict_class for regression and classification. These functions can be called directly in SQL, invoked through Genie with plain-language questions, or embedded in dashboards, delivering consistent, high-quality answers without infrastructure management. Benchmarking on fev-bench shows ai_forecast outperforms forecasting functions from leading cloud data warehouses, achieving industry-leading accuracy out of the box with no tuning required.
Twilio Video Media SDK Powers Smarter Customer Engagement
Twilio launched the Twilio Video Media SDK in Public Beta, a server-side software package that connects applications to a Twilio Video Room as a secure virtual participant, streaming real-time audio and video to backend systems. This launch supports AI-enabled customer engagement by allowing teams to use consistent AI logic across chat, voice, and video channels while keeping sensitive data secure. The SDK enables use cases such as multimodal AI assistants that see and hear users, ambient healthcare documentation, remote verification with computer vision, and server-side compliance checks. By routing individual media tracks to a secure backend, the SDK supports HIPAA and GDPR requirements without client-side rendering. Early adopter GLOBO Language Solutions uses the SDK to enhance real-time quality and AI integration for patient care in healthcare environments.
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