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Announcement reads
Announcement posts on Yomu, summarised newest first.
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- 20 companies
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Announcement posts
Custom OIDC Providers for Supabase Auth
Supabase has launched Custom OIDC Providers, enabling projects to connect standards-compliant external OpenID Connect identity providers beyond its built-in social options. Configuration requires an issuer URL, client credentials, and scopes; Supabase automatically resolves the discovery document, endpoints, and JWKS, and verifies ID tokens against the provider’s JWKS. After enablement, users use the standard OAuth sign-in flow through existing JavaScript, Flutter, or Swift clients, while the auth server handles redirects, code exchange, profile retrieval, and user creation or linking. Custom providers include default PKCE, authorization-parameter support, multi-platform audience validation through acceptable_client_ids, and an email_optional setting for providers that do not return email addresses. They can be created, listed, updated, or deleted via the Dashboard or Admin API, with a limit of three per project.
Safetensors is Joining the PyTorch Foundation
Safetensors has joined the PyTorch Foundation as a foundation-hosted project under the Linux Foundation, alongside DeepSpeed, Helion, Ray, vLLM, and PyTorch. It began as a Hugging Face project addressing the risk of arbitrary code execution in pickle-based model-weight formats. Its format stores tensor metadata in a JSON header capped at 100MB, followed by raw tensor data, and supports zero-copy mapping and lazy loading. The move places the trademark, repository, and governance with the Linux Foundation, while Luc and Daniel remain on the Technical Steering Committee and lead day-to-day work. Existing users get the same format, APIs, and Hub integration without breaking changes, while contributors gain a documented maintainer path; planned work includes PyTorch core serialization, accelerator-aware loading, parallel loading, and quantization support.
ClickHouse Release 26.3
ClickHouse 26.3 delivers twenty-seven new features, forty performance optimizations, and over two hundred bug fixes. Common table expressions can now be materialized into temporary tables via the MATERIALIZED clause when enabled, evaluating subqueries once to reduce query execution times and peak memory usage. The query optimizer expands join reordering beyond inner and left or right joins to include anti, semi, and full joins when table statistics are available. In addition, user-defined functions can run compiled WebAssembly code inside sandboxed Wasmtime runtimes. Storage for the Map data type also introduces hash-based bucketing during background merges, accelerating single-key lookups by two to forty-nine times without slowing initial inserts.
Never stop disrupting yourself; introducing the Fin API platform
Fin API platform launch makes the models powering its customer-agent system available through APIs, with contracts starting at $250k annually. It says Fin currently resolves over 2M customer issues weekly across brands and runs on models trained by its AI group. Last week’s Apex announcement is described as a specialized customer-service LLM that beat frontier models from Anthropic and OpenAI in six-month production tests on resolution rate, latency, hallucination rate, and cost. Customers can choose the Fin Agent Platform, Fin Agent API, or direct access to Apex and related models for bespoke, specialized agents. The rationale is customer demand and a broader expectation that software differentiation will shift from features and interfaces toward agents built on differentiated AI.
100,000 GitHub stars
Supabase reports reaching 100,000 GitHub stars and says eight million developers are building with the platform. Founded in 2020 by two people around a Postgres database, the project says it optimized for developers having a good time rather than for star counts, treating the milestone as evidence of a community that contributed, stayed involved, and built unexpected projects. Its open-source approach centers on supporting existing tools and communities: projects run unmodified Postgres, PostgREST handles billions of API requests daily, pgvector stores embeddings, Deno underpins Edge Functions, imgproxy handles image transformations, and Phoenix supports Realtime at almost 1.5 billion messages per day. The team says it aims to help make Postgres the default database for new internet projects, including by pursuing petabyte scale and pluggable storage.
Meet the new Cursor
Cursor 3 is a unified workspace for building software with agents, introduced as Cursor moves toward a third era in which fleets of agents work autonomously to ship improvements. Built from scratch around agents rather than as an extension, its multi-workspace interface brings local and cloud agents into one sidebar across multiple repositories and supports parallel work. Sessions can move from cloud to local for desktop editing and testing, or from local to cloud for offline and longer-running work; cloud agents also produce demos and screenshots for verification. The new diffs view streamlines editing and review through staging, committing, and PR management, while files with full LSPs, an integrated browser, and Marketplace plugins retain IDE capabilities; users can switch back to the Cursor IDE. Cursor says these foundational pieces—model, product, and runtime—are in place for more autonomous agents and team collaboration, while IDE investment continues until codebases are self-driving.
Welcome Gemma 4: Frontier multimodal intelligence on device
Google DeepMind’s Gemma 4 family is available on Hugging Face as Apache 2-licensed, multimodal models intended for use across agents, inference engines, fine-tuning libraries, and on-device deployments. The family accepts image and text inputs across five sizes, while E2B, E4B, and 12B Unified also support audio; context windows range from 128k to 256k tokens. Its design combines alternating local and global attention, dual RoPE configurations, Per-Layer Embeddings, shared KV caches, and model-specific vision and audio handling, including an encoder-free unified 12B variant. The post documents integrations for Transformers, llama.cpp, MLX, WebGPU, Rust, and other tools, alongside fine-tuning examples and demos. Reported instruction-tuned results include a 1452 estimated text-only LMArena score for 31B and benchmark results spanning reasoning, coding, vision, audio, and long-context tasks.
Announcing Role Based Access Control in ClickStack
Managed ClickStack has introduced Role-Based Access Control (RBAC) to replace a legacy model where all users shared identical instance-wide permissions or required duplicate HyperDX instances for isolation. Integrated with ClickHouse Cloud for identity management, the new system allows administrators to assign users to roles governed by three access tiers: no access, read, and manage. Permissions apply across platform resources including dashboards, saved searches, sources, alerts, webhooks, and notebooks, with support for fine-grained rules matching names, IDs, or tags. While table-level source restrictions work through ClickStack, row-level access currently depends on separate ClickHouse SQL console roles, which the team intends to unify directly inside ClickStack in future releases.
Falcon Perception
Falcon Perception is a 0.6B-parameter early-fusion Transformer for open-vocabulary grounding and segmentation from natural-language prompts. It processes image patches, text, and task tokens in one sequence, using hybrid attention so image tokens receive bidirectional context while text and task tokens decode causally. Its Chain-of-Perception interface predicts each instance as coordinate, size, and segmentation tokens, with specialized Fourier-feature and dot-product heads for dense outputs. The post also introduces PBench, which profiles attributes, OCR-guided identification, spatial understanding, relations, and crowded scenes. Falcon Perception reaches 68.0 Macro-F1 on SA-Co versus 62.3 for SAM 3, while presence calibration remains weaker at MCC 0.64 versus 0.82; the 0.3B Falcon OCR model scores 80.3 on olmOCR and 88.6 on OmniDocBench.
Supabase docs over SSH
Supabase is launching supabase.sh, a free public SSH server that gives AI coding agents bash access to Supabase documentation as a public experiment. Its virtual file system exposes every documentation page as a Markdown file under /supabase/docs, allowing agents to use familiar commands such as grep, find, ls, cat, head, and tail to browse, search, and skim current content. The service runs on just-bash, Vercel’s open-source Bash emulator written in TypeScript, inside a sandboxed JavaScript runtime over a virtual file system, so it does not execute an actual shell on the server. Project instructions can be added with ssh supabase.sh agents >> AGENTS.md or generated through ssh supabase.sh setup | claude; the post contrasts this exploratory workflow with the existing search_docs MCP tool and presents supabase.sh as an early experiment.
We taught ClickStack to read your logs like a detective novel
HyperDX introduced AI Summarize, a feature designed to translate raw logs, traces, and log patterns into narrative summaries directly within its observability interface. Instead of relying on backend large language models, the tool runs entirely client-side using OpenTelemetry-aware data extraction combined with hand-written phrase pools. The generation logic inspects Kubernetes and OpenTelemetry attributes—such as service names, endpoints, exception details, and query durations—to tailor tone across detective noir, Shakespearean drama, and nature documentary themes. Because processing occurs inside the browser tab without external API calls, the system avoids third-party latency, token expenses, model hallucinations, and data privacy risks. Users can access summaries across logs, spans, and pattern drawers or re-enable the interface toggle via URL parameter through April 2026.
Granite 4.0 3B Vision: Compact Multimodal Intelligence for Enterprise Documents
Granite 4.0 3B Vision is a compact vision-language model designed to extract information from enterprise documents, including tables, charts, forms, and semantically meaningful key-value pairs. It combines a million-scale ChartNet dataset with a code-guided synthesis pipeline, DeepStack Injection for routing semantic and high-resolution visual features to different layers, and a LoRA adapter architecture built on Granite 4.0 Micro. The modular design supports standalone image processing, text-only fallback, and integration with Docling for multi-page document pipelines. Reported evaluations show 86.4% on Chart2Summary, leading the evaluated models, second place on Chart2CSV at 62.1%, leading scores on the listed table benchmarks, and 85.5% zero-shot exact-match accuracy on VAREX.
Introducing MongoDB Agent Skills and Plugins for Coding Agents
Coding agents often default to relational patterns, resulting in over-normalized schemas, underused compound indexes, and misconfigured search indexes in production MongoDB environments. To address these pitfalls, MongoDB launched official MongoDB Agent Skills alongside integrated plugins for tools including Claude Code, Cursor, Gemini CLI, and VS Code. These skills provide agents with discoverable instructions, schema design heuristics, indexing strategies, and operational safeguards across the development lifecycle. The skills operate alongside the MongoDB MCP Server, which handles database connectivity, authentication, and permission-based governance. Teams can install these bundled extensions directly or manage them via the Vercel Skills CLI to embed MongoDB architectural standards into automated workflows.
Australia and Anthropic sign AI safety MOU
Anthropic and the Australian government signed a Memorandum of Understanding to cooperate on AI safety research and support Australia’s National AI Plan. The agreement centers on collaboration with Australia’s AI Safety Institute, including sharing findings about emerging model capabilities and risks, participating in joint safety and security evaluations, and conducting research with Australian academic institutions. Anthropic will also share Anthropic Economic Index data to track AI adoption, economic effects, and workforce implications, initially covering natural resources, agriculture, healthcare, and financial services. Separately, AUD$3 million in Claude API credits will support four Australian institutions working on clinical genomics, precision medicine, pediatric research, and computing education. The company is also exploring data-center and energy investments, launching a startup credit program, and preparing to open a Sydney office.
TRL v1.0: Post-Training Library Built to Move with the Field
TRL v1.0 marks a transition from research codebase to a library intended for production systems, with clearer stability expectations. It implements more than 75 post-training methods, but its design prioritizes trying, comparing, and using them over coverage alone. The library responds to a field in which PPO, preference optimization methods such as DPO, ORPO, and KTO, and RLVR-style approaches such as GRPO repeatedly changed which components of the training stack appear essential. TRL addresses that volatility with a stable core under semantic versioning, an experimental surface for faster-moving methods, and deliberately limited abstractions that favor explicit implementations over generic hierarchies. v1.0 also follows distributed breaking changes across the 0.x releases, so migration from the last 0.x version is described as minimal, while future work targets asynchronous GRPO, scaling, and actionable training diagnostics.
ClickHouse BYOC on Google Cloud now Generally Available
ClickHouse Bring Your Own Cloud is now generally available on Google Cloud across all public ClickHouse Cloud Google regions without requiring a waitlist. The architecture separates the ClickHouse-managed control plane from the customer data plane, which runs entirely within the user Google Cloud project on Google Kubernetes Engine and Google Cloud Storage. An outbound-only, encrypted Tailscale zero-trust tunnel restricts control plane communication strictly to orchestration traffic, ensuring query data never leaves the customer environment. Deployment involves a three-step Terraform setup that establishes least-privilege IAM roles, configures VPC networking, and automates cluster provisioning. ClickHouse manages database and infrastructure maintenance through ArgoCD, employing a make-before-break strategy to add updated replicas before decommissioning older nodes across customizable release channels.
Announcing Fin Apex: The age of vertical models is here
Fin announces Apex 1.0, a custom model trained by its AI Group for customer-service conversations, replacing frontier-lab models as the core answering model for nearly all English-language chat and email traffic. The post says Apex improves resolution, speed, hallucination rates, and cost, citing one gaming customer’s resolution rate rising from 68% to 75% overnight and describing a 22% reduction in unresolved conversations. Its development relies on proprietary evaluations and billions of human and agent interaction data points generated by Fin’s resolution engine, creating a training flywheel for further domain-specific improvement. The launch is presented as evidence that customer-service applications may need to become full-stack AI companies rather than relying on shared general-purpose models. It also argues that frontier labs face pressure to offer cheaper specialized models as post-training and task-specific data become more important.
Provision a production-ready dev stack from your terminal
Stripe Projects, a new tool in the Stripe CLI, addresses the manual, fragmented work of turning a local prototype into a production-ready environment. It manages a project manifest and Stripe-backed record for attached services, provider accounts, and required credentials, while an integration protocol standardizes provisioning, plan selection, upgrades, and credential handoff. The developer preview supports services from providers including Vercel, Railway, Supabase, Neon, PlanetScale, Turso, Chroma, PostHog, Clerk, and Runloop; resources remain in accounts owned by the developer, and credentials can be synced to local development, CI, or a team secret manager. Through CLI commands such as stripe projects init, add, upgrade, and env --pull, developers or coding agents can repeat setup, audit changes, and upgrade supported plans using tokenized payment credentials, although payment handoff is limited to supported providers and plans in the US, EU, UK, and Canada.
More Visibility, Less Guesswork: ClickHouse Cloud's New Monitoring Capabilities
ClickHouse Cloud released new monitoring capabilities and dashboards to give platform administrators deeper visibility into deployment health and server behavior. A newly introduced Overview page consolidates critical signals into a unified view for rapid health checks. Additionally, an updated Infrastructure page tracks service scaling over time, presenting CPU and memory utilization with new aggregation options alongside modals explaining automatic scaling decisions. The platform now sends automated email and Slack notifications when deployments encounter risks such as excessive parts, failed mutations, or query concurrency saturation. These additions allow administrators to proactively identify operational bottlenecks and configure scaling limits without spending excessive time searching for telemetry data.
Announcing Monitors: Opening the AI black box
Intercom announced Monitors, a new Fin product area intended to complete its observability suite alongside Insights and Recommendations. It addresses the limits of random sampling, CSAT, and spreadsheet-driven quality assurance by letting teams select conversations with filters or natural-language criteria, including risk signals, and maintain repeatable samples. Custom Scorecards then define business-specific evaluation criteria, weighting, and critical failures, with scoring performed by AI, humans, or both to produce an overall quality score. Flagged conversations enter a Review Queue for assignment, notes, status tracking, and follow-up fixes, while reporting compares scores with CX Score, resolution rate, Monitors, and Scorecards over time. Monitors for Fin conversations is live, with human-agent QA, real-time alerts, and knowledge-base evaluation identified as planned additions.
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