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Announcement reads
Announcement posts on Yomu, summarised newest first.
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Announcement posts
We just shipped support for the ugliest part of HTTP: Vary
The Vary response header, often called the ugliest part of HTTP, is now supported in Cache Rules on every Cloudflare plan. Vary tells caches which request fields may affect a response, but not which differences actually matter, leading to cache fragmentation or incorrect responses. For example, a server might deliver different image formats to different browsers, and a cache ignoring Vary risks serving the wrong bytes. The origin still names the request headers that may affect a response, but Cloudflare lets users decide how to handle each one. This support addresses the challenge of balancing correct caching with cache efficiency.
ClickHouse appoints Mike Scarpelli, former Snowflake and ServiceNow CFO, to Board of Directors
ClickHouse appointed Michael Scarpelli, former CFO of Snowflake and ServiceNow, to its Board of Directors as an independent director and chair of the Audit Committee. Scarpelli previously guided three companies through initial public offerings and said ClickHouse is redefining the category for real-time and agentic workloads. The company has surpassed $350 million in annualized revenue, up from roughly $200 million at the end of 2025, and achieved a $15 billion valuation in its Series D round, with over 4,000 customers including DoorDash, Meta, Tesla, and Visa. ClickHouse now employs nearly 800 people across 27 countries, plans to reach 1,000 by year-end, and derives more than half its revenue from outside the United States as it scales its enterprise motion. The appointment is part of a deliberate build-out of finance and governance capabilities, including the 2025 hire of CFO Jimmy Sexton, who also came from Snowflake.
Data liberation: Apache Kafka's native cluster mirroring
KIP-1279 introduces native cluster mirroring, embedding cross-cluster replication directly into the Kafka broker. This eliminates external tooling, offset translation tables, and recompression, as destination brokers fetch committed records using the standard fetch protocol and append them byte for byte. The approach preserves offsets, compression, and consumer group state, simplifying failover to stopping the mirror and redirecting clients. The architecture uses mirror fetcher threads inside the broker, extending AbstractFetcherThread, and leverages existing client quota mechanisms. Migration involves creating a new KRaft cluster, setting up a mirror, and starting mirroring, with future work including synchronous mirroring and tiered storage integration.
Introducing Worker Previews: isolated preview environments for every change your agent makes
Worker Previews gives every Git branch its own production-like environment with an isolated URL, configuration, state, and observability. Developers deploy a Preview using npx wrangler preview, which creates a separate Worker version with its own variables, secrets, and bindings, distinct from production, on a stable URL that updates with each push. Durable Objects and Containers automatically receive new namespaces per Preview, preventing state contamination across branches during migrations or schema changes. Each Preview supports full observability—logs, errors, metrics, and traces—scoped to that branch, enabling agents to autonomously test, catch failures, push fixes, and verify before production. The feature enables an Agent Development Lifecycle where every change is atomic, independently deployable, and revisable before merging to production.
JetBrains Air: Building a System of Products for Agentic Software Development
JetBrains Air is a system of products for agentic software development, built on the premise that no single model, agent, or service fits every developer, team, or task. It connects products for individual work, team coordination, organizational control, context, and process automation, while remaining open to tools and agents developers choose, including those JetBrains does not build. This openness lets developers select the agent, model, or service suited to a task without forcing the organization to give up context, visibility, or control. The Post notes that every new model, agent, or service an organization adds reduces visibility into its own development work. Because intelligence does not belong to one model, the benefit can extend across supported agents and services.
Canary rollouts: upgrade models in production without downtime
Deploying a new model checkpoint in production typically requires a tradeoff: a hard swap exposes all users to regressions at once, while a DIY staged cutover relies on manual traffic shifting. Together AI’s rollout system automates this by moving traffic between source and target deployments through configurable canary steps, such as 5%, 25%, 50%, and 100%. Each step runs health checks before shifting traffic, enforces a propagation wait for routing caches, and drains the source only after traffic has moved. Optional metric gates, like p95 latency, run after each step; if a gate trips, the rollout pauses automatically and waits for human action. In a real canary from Qwen2.5-7B to Qwen3.5-9B, a 137% p95 regression was caught at 10% traffic, allowing reversal with no failed requests.
How UK AISI and EvalEval Are Making Benchmark Results Reproducible
The UK AI Security Institute (AISI) has begun using EvalEval’s Every Eval Ever (EEE) schema and Evaluation Cards platform to openly share LLM evaluation results. This collaboration, stemming from a joint workshop at NeurIPS 2025, aims to make benchmark results more reproducible by providing standardized configuration and context alongside verified scores. AISI released results for five benchmarks—HealthBench, FrontierMath, Humanity’s Last Exam, SWE-Bench Pro, and Terminal-Bench 2.0—across six frontier models, plus two cyber evaluations using a partially overlapping model set. The release accompanies a paper studying how inference-time compute and evaluation protocol affect performance, revealing that models continued solving tasks when given correctness feedback. By adopting a shared reporting schema, evaluators can compare findings across studies and understand how setup choices influence reported performance, while EvalEval invites model developers, evaluation creators, and researchers to contribute to the shared infrastructure.
The Headless Dashboard Initiative
Resend announced a headless dashboard initiative to make all dashboard capabilities available programmatically via the MCP, CLI, and API. Recent releases include the Email Metrics API for deliverability and engagement data, a Headless Webhook API for listing events and replaying deliveries, and a Cancel Broadcast API. The Duplicate Broadcast API copies any broadcast into a draft, and the Share Email API provides expiring read-only email links. The team aims to make the dashboard best-in-class while committing to full headless operation for every future entity. Developers can start building today by connecting the MCP server or using the CLI.
Transformers now runs llama.cpp quants
Transformers now supports running GGUF quantized models locally, reusing llama.cpp’s ggml kernels for efficient inference on Apple Silicon. The GGUF format packages weights, tokenizer, and chat template in one file, with quantization levels like Q4_K_M that trade precision for memory. Users can load a checkpoint from the Hub with from_pretrained and generate text using the standard Transformers API, or serve it via an OpenAI-compatible endpoint. The initial focus is on the Qwen3.5 architecture, with benchmarks on a MacBook Pro M2 Max showing token-generation rates close to llama.cpp, including prefill time. The integration uses packed quantization kernels and generation-loop optimizations to reduce overhead, though it is currently limited to single conversations on MPS without batching support.
Jun Kim, oMLX creator and maintainer, joins Hugging Face to support the MLX community
Jun Kim, creator and maintainer of oMLX, has joined Hugging Face to support the MLX community. MLX is Apple’s framework for local AI, optimized for Apple Silicon, and Hugging Face is the Hub for MLX models. oMLX, staying Apache 2.0, will gain stability and faster development as a funded, maintained project, allowing Jun to guide contributors and build for the long term. The goal is to unblock the community to run local AI in any form, with oMLX serving as a testbed leveraging dependencies like mlx-lm and mlx-vlm, and collaborating with projects such as LMStudio. A key focus is streamlining the transition from transformers model definitions to reference MLX implementations for different engines, so each can focus on unique features.
Simplifying Model Serving Across Multiple GPUs with NVIDIA TensorRT Multi-Device Integration in NVIDIA Dynamo-Triton
NVIDIA TensorRT multi-device inference enables a single TensorRT network to execute across multiple GPUs using NCCL-backed distributed collectives, supported from TensorRT 11.0. Integrated into Dynamo-Triton release 26.07, one model instance can own multiple GPUs and expose a single gRPC endpoint so the client does not coordinate GPU ranks. The post demonstrates this with Cosmos 3 Nano video generation, where the transformer uses Ulysses context parallelism to distribute 44,160 video tokens across up to eight GPUs. End-to-end latency drops from 156.6 seconds on one GPU to 34.2 seconds on eight GPUs, achieving a 6.09x transformer RPC speedup. Teams can trade additional GPU resources for shorter request latency while keeping the application interface and surrounding workflow stable.
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.
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