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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
Amazon launches research initiative with Stanford University to advance AI and science
Amazon and Stanford University have launched the Stanford and Amazon Research Initiative, a formalized framework to advance research in AI, energy, and healthcare. The initiative builds on a deep relationship where currently more than 10 teams across Amazon fund active research and PhD fellowships at Stanford, spanning humanoid robotics, post-quantum cryptography, causal measurement science, and AI-driven radiology. It aims to accelerate the path from breakthrough research to real-world solutions that benefit society at large and broaden participation from diverse scholars. Focus areas will leverage strengths in artificial intelligence, machine learning, automated reasoning, and health, supported by Amazon’s cloud computing and AI services. The collaboration includes joint research projects, PhD fellowships, and interdisciplinary symposia, and was announced at a Stanford event with AWS CEO Matt Garman and Vice Provost David Studdert discussing the impact of university-industry collaborations.
Advancing AI for biology: Teaching models to design and characterize antibodies
Monoclonal antibodies are a mainstay of biopharmaceutical development, with over 100 FDA-approved drugs and established manufacturing and regulatory pathways, yet conventional discovery is costly and slow, typically taking six to twelve months from target to lead candidate. AI promises to make antibody development cheaper, faster, and more flexible by designing and characterizing therapeutic antibodies computationally, but scientific questions remain. Development hinges on three factors: the best binding site on the target, which candidates bind most tightly, and whether candidates can survive manufacturing and the clinic. Predictive models for each factor do well on familiar targets and assays but considerably worse on unfamiliar ones, a gap that in-distribution benchmarks have made difficult to measure. Three papers from Amazon Bio Discovery’s science team address research questions about each factor, with methods integrated into the AI-powered application that connects biological AI models with lab services.
Open-Sourcing Rebalancer: A Generic, High-Performance Library for Solving Assignment Problems
Meta has open-sourced Rebalancer, a generic library for solving assignment problems that has been used internally for over nine years. Rebalancer separates problem specification from solution, using a three-step approach with modeling constructs like dimensions, partitions, scopes, and utilization, and a high-level spec API for common objectives and constraints. Problems are translated into an expression graph, and Rebalancer offers two solving techniques: an optimal solver using mixed integer programming (MIP) with commercial or open-source solvers, and a local search solver that evaluates millions of moves per second for large-scale problems. The local search solver handles problems with over a million objects and 5k bins, with an average solve time of 171 seconds. Rebalancer is used for roughly 40 million assignment problems daily across Meta’s infrastructure, and the open-source release includes a debugging UI called Rebalancer Explorer. The accompanying paper was published at OSDI’24.
Introducing Our New Dropbox API Documentation
Dropbox has launched a modernized API documentation site at https://docs.dropboxapi.com, rebuilt to make integration faster and more intuitive. The new documentation features interactive API testing that lets developers set parameters, issue API calls, and view real responses from the Dropbox API directly in their browser without switching tools. An embedded AI assistant offers instant help with endpoint questions, troubleshooting, and code examples, while the documentation integrates with MCP (Model Context Protocol) so AI tools like Claude Code and Cursor can access up-to-date API information. Through the MCP server at https://docs.dropboxapi.com/_mcp/server, developers can connect their AI assistants to answer questions, explore the API, and assist with integrations using information directly from the docs. The update delivers a clean, responsive, searchable interface that brings endpoints, types, schemas, and related documentation together, with improved navigation, and provides comprehensive information including detailed error responses, type definitions, and full request/response schemas.
AI threat modeling shouldn't be a bottleneck, so we open sourced ours
The post announces the open-sourcing of agentic-threat-modeling, an AI-assisted threat-modeling module for Claude Code, Cursor, and Gemini CLI, built to address the bottleneck of security teams reviewing AI-generated code. It runs on Lola, an open source package manager for AI contexts, and profiles likely attackers, generates narrative attack scenarios grounded in actual code, and checks them against six structured threat modeling frameworks (STRIDE, PASTA, LINDDUN, attack trees, and more). The module ties every finding to a specific file and line of code, as shown in a real-world example against a file upload endpoint. It’s designed to produce structured output for AI-assisted triage, though that pipeline isn’t built yet. The team open sourced it because the problem isn’t unique to them, and it’s available in RedHatProductSecurity/agentic-threat-modeling and LobsterTrap/lola.
Python Workers are now generally available
Cloudflare announced general availability of Python Workers, making Python a first-class language on its developer platform. The runtime now encapsulates type conversion so developers use Cloudflare bindings like D1 and Workers AI directly, without the previously required JavaScript glue code. Popular frameworks FastAPI, Django, and Flask run inside Workers via built-in ASGI and WSCI connectors that translate JavaScript requests into standard Python structures. A custom socket syscall implementation enables TCP connections for database drivers, unlocking Hyperdrive integration with PostgreSQL and MySQL, previously blocked by WebAssembly sandbox limits. Example projects include streaming from Bluesky Jetstream via Durable Objects, building an MCP server with the official Python MCP package, and creating a RAG system with Vectorize, with documentation now including Python examples alongside TypeScript.
Inside Petal: Building the World’s First Petabit-Class Transoceanic Subsea Cable
Meta announced Petal, the first transoceanic subsea cable to deliver petabit capacity, connecting France and the United States over 7,000 km and expected to enter service in 2029. It will be the first subsea cable system to deploy multi-core fiber technology at scale, using 2-core fiber in a 24 fiber-pair system to achieve 1 Pbps. The cable overcomes the Shannon Limit by adopting spatial division multiplexing with multi-core fiber, requiring ultra-pure synthetic silica for low attenuation and counter-propagation to minimize crosstalk. Petal’s repeaters use a single-body 96 amp design with a Fan-In/Fan-Out interface to transition between 2-core and single-core fiber for amplification, staying within existing 18 kV power feeding limits. Built with NEC, Sumitomo Electric, and Orange, Petal represents the largest generational capacity increase of any repeatered subsea system.
Building Sentry's Laravel AI Integration
Sentry’s Laravel AI integration, built during an internal Agent Hackweek, provides Agent Tracing for Laravel AI agents. The author used Claude for coding, which initially produced poor generic helper code. Laravel AI emits lifecycle events for agents and tool calls, but not for individual LLM invocations. To capture these, the integration matches outgoing HTTP requests to active invocations using provider URL prefixes, associating with the most recently started invocation if multiple match. Enabling the integration requires only updating to sentry-laravel 4.27, with no Sentry-specific code. Traces appear in Sentry’s Explore > Agents section, showing conversations, tool calls, and their inputs and outputs.
Helix: The internal tool powering our Shopify app's native migration (2026) - Shopify
Shopify’s internal tool Helix powers the native migration of its Shopify app, which has over 300 screens. The company previously rebuilt the Shop app in just 12 weeks using similar methods. Helix is a set of tools and skills that help LLMs migrate features and screens from the React Native app while following a highly opinionated architecture. Unlike most tools that gather information, create specs, and implement everything at once, Helix aims for consistent, high-quality, and maintainable results. The approach is now being applied to the larger Shopify App after its success with Shop.
tokenizers v1: encode, decode and scaling, measured
The tokenizers library is being reworked for version 1 with a heavy focus on performance, as tokenization can become a bottleneck that starves models of data when workloads scale. The work draws on ideas from other open-source tokenizers like gigatoken, tiktoken, and kitoken. Key optimizations include using SIMD instructions for UTF-8 text, ensuring merges never cross pre-token boundaries, and reusing scratch buffers to avoid allocations. The release candidate is implemented in the Rust pre-release on crates.io, with the crate split into tk-encode, tk-serialize, tk-convert, and tk-train. Batched model calls and faster decoding with parallel batch processing are also part of the improvements.
Your sandbox can now serve requests
Sandboxes have traditionally been outbound-only machines, unable to receive inbound connections because they sit behind NAT with no public IP or open ports. BoxCompute now enables sandboxes to serve requests by using Tailcat, an open-source library from Tailscale that performs NAT traversal. Tailcat establishes a direct peer-to-peer WireGuard-encrypted path between the client and sandbox, using a DERP relay only for the initial handshake. This approach avoids the latency and bandwidth bottlenecks of relay-based tools like ngrok or Cloudflare Tunnel, and keeps the relay out of the data path. The feature is rolling out as an early preview with scoped, short-lived grants for selected TCP ports.
Partnering with Accenture on embedded evaluation
Anthropic is partnering with Accenture’s specialist AI business, Faculty, on independent evaluation of frontier AI, advancing a commitment to embed evaluators within Anthropic. The work will cover model evaluation and red-teaming, alignment assessments, and testing safeguards, informed by Accenture’s experience deploying AI across industries. Anthropic and Accenture each expect to invest at least $1 billion in building capacity over five years, although operating details, access standards, reporting practices, and funding models remain unsettled. Embedded evaluators would work inside AI companies with employee-comparable access to observe training, follow development and deployment decisions, speak with staff, verify safety commitments, identify blind spots, and report incidents; Anthropic says its partnership is non-exclusive and that model safety remains its responsibility.
Making Local AI Smarter and Faster
Junie Local’s update introduces Qwen3.8-3.6-27B-blend, a merged model that balances speed and capability for local coding agents. The blend, created by merging Qwen3.6 and Qwen3.8 weights in equal proportions, completed 37 tasks in a 100-task internal benchmark, compared to 34 for Qwen3.6 and 39 for Qwen3.8, while generating 71% fewer output tokens than Qwen3.8. The runtime uses multi-token prediction (MTP), with a two-token proposal on M5 MacBook Pro making decoding 60% faster. A four-bit MTP head was kept over an eight-bit one due to similar acceptance rates and memory savings. The update also brings experimental NVIDIA RTX support on Windows for machines with at least 24 GB of VRAM.
Ktor 3.6.0 Is Now Available!
Ktor 3.6.0 introduces experimental typed authentication with full type safety, including role-based access and anonymous users, plus a dedicated OpenID Connect plugin that supports sessions and auto-refreshing tokens, and experimental HTTP/3 support for the Netty engine via QUIC. Request-parameter conversion now supports Kotlin’s Uuid, Byte, and unsigned numeric types, while receive() accepts nullable types, deprecating receiveNullable(), and more flexible routing syntax. The ContentNegotiation plugin gains a ContentTypeMergeStrategy.SkipIfPresent to preserve explicit Accept headers. Kotlin Multiplatform projects benefit from the ktor-client-engine-defaults artifact and file-based HttpCache storage using kotlinx-io Path, simplifying cross-platform configuration. Additional improvements include respondHtmlPartial for partial HTML output, asynchronous DNS resolution for the CIO engine, and WebRTC support for JVM.
Classic Engine Feature EOL: SSR, Desktop SSO, Device Trust
Okta will end support for Self-Service Registration, Desktop Single Sign-On using Integrated Windows Authentication (IWA), and Device Trust on Classic Engine on March 5, 2027. After that deadline, the capabilities will be turned off unless organizations upgrade to Okta Identity Engine, and Okta will no longer address related bugs, security vulnerabilities, or broken flows. The migration paths are Profile Enrollment Policy for self-service registration, Agentless Desktop Single Sign-On or Okta FastPass for desktop authentication, and management attestation with Okta Verify for mobile device trust. Identity Engine adds passwordless authentication, device assurance, phishing-resistant multifactor authentication, identity verification, zero-trust capabilities, and a newer policy framework, with the upgrade included at no additional licensing cost. Organizations are advised to assess feature usage, test in non-production, and schedule production migration before the hard deadline.
Databricks recognized as a Gartner® Peer Insights™ Customers' Choice for Analytics and BI
Databricks says it was recognized as a Customers’ Choice in Gartner Peer Insights’ Voice of the Customer for Analytics and Business Intelligence Platforms, based on verified reviews from customers using platforms in production. The post reports a 4.8 out of 5 overall rating and 94% willingness to recommend, while noting that the designation reflects customer adoption and overall experience meeting or exceeding market averages. It presents Databricks AI/BI, AI/BI Dashboards, Genie Spaces, and Genie as a unified architecture combining governed live data, AI, business intelligence, and Unity Catalog controls without moving or duplicating datasets. The described use cases include conversational exploration, dashboard and visualization development, consolidating fragmented reporting, and expanding governed analytics access, with the company positioning these capabilities as a foundation for agentic analytics.
Protect Performance and Reduce Surprise Costs with Default Warehouse
Databricks SQL’s generally available Default Warehouse lets administrators choose the SQL warehouse automatically used for ad hoc experiences, addressing manual selection and fallback behavior such as “Last Selected” or alphabetical ordering. A workspace-level setting applies across SQL Editor, Catalog Explorer, AI/BI Dashboards, Alerts, and Genie Spaces, while users can override it or retain “Last Selected”; admin APIs and a Terraform resource support per-user assignment, auditing, and control. The feature supports serverless, pro, and classic SQL warehouses and respects existing governance and access controls, with the setting opt-in and new assets automatically using it. In customer results, Catalog Explorer queries sent to smaller warehouses rose from 77% to 96%, and users running ad hoc SQL across multiple warehouses fell by 15%, while future coverage is planned for MLflow, Lakeflow CLI, and DBSQL MCP.
The next era of the open lakehouse: Apache Iceberg™ v3 in Public Preview on Databricks
Databricks has placed support for Apache Iceberg v3 into Public Preview, bringing new table-format capabilities to its open lakehouse. Unity Catalog Managed Iceberg v3 tables support Row Lineage, Deletion Vectors, and the VARIANT type, while interoperability extends these features to foreign Iceberg tables. Row IDs and modification sequence numbers identify changed rows, and lightweight delete files avoid immediate Parquet rewrites, with data manipulation reported as up to 10x faster than copy-on-write. VARIANT stores evolving semi-structured payloads beside relational columns for direct SQL querying, while Unity Catalog provides cross-catalog governance and Delta-Iceberg interoperability without data duplication. Iceberg v3 is available on Databricks Runtime 18.0+ with Unity Catalog enabled; Databricks is also advancing Iceberg v4 metadata proposals.
Auth0 Agent Gateway: The Identity Control Plane for Customer-Facing AI
Auth0 Agent Gateway is announced in beta as a Customer Identity and Access Management (CIAM) control plane for governing product-native, customer-facing AI agents in multi-tenant SaaS applications. It addresses a trust model involving organizations, delegated user access, customer consent, independently owned connected accounts, and cross-tenant data boundaries, rather than workforce-only identity. Positioned between an agent and its tools, the gateway evaluates each call using agent, user, and organization context; it uses Organizations, Agents as Principals, Token Vault, token exchange, tenant logs, and administrative controls. The described capabilities include short-lived agent-only tokens, user-scoped credentials, runtime tool authorization, audit logging with optional SIEM streaming, and an agent kill switch. Auth0 is seeking product and engineering teams to join the beta.
Move and Scale Faster with FGA Permissions Index
Auth0 FGA Permissions Index is announced in Early Access as a way for B2B SaaS applications to enforce permissions while handling expanding AI-agent activity and complex relationship graphs. Customers select relations used for search and filtering, typically high-cardinality relations such as can_view, and the index precomputes the access those relations imply. It streams the resulting data into the customer’s database alongside application data, allowing indexed permission checks through SQL JOINs instead of repeated authorization API calls or runtime graph traversal. Computation moves to relationship-write time, while relations outside the index continue to be evaluated by FGA in real time. The announcement positions this design as a path to consistently low-latency filtering at enterprise scale, with permission decisions described as precomputed, traceable, and compliant.
Top topics
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- AI Agents564
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