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Introducing Claude Sonnet 4.5
The release introduces Claude Sonnet 4.5 as a model aimed at complex coding, agentic work, computer use, reasoning, and math, with claimed gains in alignment. It reports state-of-the-art SWE-bench Verified performance and a 61.4% OSWorld score, up from Sonnet 4’s 42.2%, while noting more than 30 hours of observed focus on complex multi-step tasks. Alongside the model, the company released Claude Agent SDK, added Claude Code checkpoints, a refreshed terminal interface, native VS Code extension, context editing, and a memory tool, and brought code execution and file creation to paid Claude apps. Sonnet 4.5 is available through the Claude API as claude-sonnet-4-5 at $3/$15 per million tokens, the same pricing as Sonnet 4. It is released under AI Safety Level 3 protections, with reported reductions in concerning behaviors and improved defenses against prompt injection, while the temporary Imagine with Claude preview is available to Max subscribers for five days.
Introducing Claude Haiku 4.5
Claude Haiku 4.5, the latest small model, is available to all users, offering a new option for near-frontier performance with lower cost and greater speed. Compared with Claude Sonnet 4, it delivers similar coding performance at one-third the cost and more than twice the speed, while surpassing Sonnet 4 on some computer-use tasks. Priced at $1/$5 per million input and output tokens, the model targets real-time assistants, customer service agents, pair programming, Claude Code workflows, and sub-agent orchestration, where Sonnet 4.5 can plan and delegate parallel subtasks to multiple Haiku 4.5 instances. Safety evaluations found low rates of concerning behavior and greater alignment than Haiku 3.5; the model is released under ASL-2 and is available through Claude apps and Code, the API, Amazon Bedrock, and Google Cloud’s Vertex AI.
Introducing Claude Design by Anthropic Labs
Claude Design is introduced as an Anthropic Labs product for collaborating with Claude on visual work including designs, prototypes, slides, and one-pagers. Powered by Claude Opus 4.7, it is in research preview for Pro, Max, Team, and Enterprise subscribers, with gradual rollout and Enterprise access disabled by default. Users can start from prompts, uploaded DOCX, PPTX, or XLSX files, images, codebases, or captured website elements; Claude builds a first version and supports conversational refinement, inline comments, direct edits, custom sliders, and organization-scoped collaboration. During onboarding, it reads a team’s codebase and design files to build reusable design systems, while projects can be exported to Canva, PDF, PPTX, or standalone HTML and handed to Claude Code. Anthropic says teams use it for interactive prototypes, wireframes, explorations, presentations, marketing collateral, and code-powered experiments.
Claude Opus 4.6
Claude Opus 4.6 improves on Opus 4.5 with stronger coding, planning, debugging, code review, long-running agentic work, and performance in large codebases. It is the first Opus-class model with a beta 1M-token context window, while API features include adaptive thinking, four effort levels, context compaction, and up to 128k output tokens. In reported evaluations, it achieves the highest score on Terminal-Bench 2.0, leads frontier models on Humanity’s Last Exam and BrowseComp, and exceeds GPT-5.2 by around 144 Elo points and Opus 4.5 by 190 points on GDPval-AA. The release also adds Claude Code agent teams, upgraded Excel capabilities, a PowerPoint research preview, and availability on claude.ai, the API, and major cloud platforms, with base pricing of $5/$25 per million tokens.
Session revocations at scale
Canva gateways check in-memory session revocations on every request, but pulling over a million revocations from MySQL during deployments created severe database stampedes. To eliminate this bottleneck, the team transitioned to storing a twelve-hour sliding window of revocations in Amazon S3 partitioned into 30-minute chunks. Each revocation is packed into a 16-byte binary structure within a sorted flat array, allowing gateways to query records via direct binary search without object deserialization. Asynchronous workers batch updates into S3 using ZooKeeper leader election and conditional PUT requests for optimistic concurrency control. This architecture reduced the in-memory cache footprint by 87.5% and allowed the team to reduce their MySQL database to just two read replicas.
Cursor Router
Cursor Router now powers Auto mode, analyzing each request by task type and complexity before sending it to a suitable model. Frontier models handle demanding work, while price-efficient models handle other requests, with Cost, Balance, and Intelligence modes defining different positions on the cost-intelligence pareto frontier. Cost optimizes token spend, Balance targets strong quality, and Intelligence targets frontier quality; Balance and Intelligence bill at the routed model’s rate. Admins can enable routing by team or group, restrict modes, set defaults, allow or block underlying models, use soft or hard enforcement, and choose whether to display the routed model, while the feature is available across desktop, web, iOS, CLI, and the SDK and is on by default for Teams plans.
Introducing Cursor Router
Cursor is launching Cursor Router, a model-routing system for Teams and Enterprise plans that automatically selects a model for each coding request to target frontier-quality results at lower cost. It classifies requests using query, context, task complexity, domain, and model behavior, trained on more than 600,000 live requests and optimized for user satisfaction (AFC); the classifier is cache-aware and designed for frequent model updates. In online A/B testing across millions of requests, the company reports frontier-quality performance at 60% savings, while early-access accounts saved 30%–50% versus routing all traffic at Opus 4.8 API rates without a quality decrease. Three modes—Intelligence, Balance, and Cost—let teams choose a cost-intelligence tradeoff, with administrators controlling defaults, availability, and allowed models across supported Cursor clients.
Introducing Sonnet 4.6
Claude Sonnet 4.6 is introduced as an upgrade across coding, computer use, long-context reasoning, agent planning, knowledge work, and design. It adds a 1M token context window in beta, becomes the default for Free and Pro users in claude.ai and Claude Cowork, and keeps Sonnet 4.5’s starting price of $3/$15 per million tokens. Early testing reports that users preferred it over Sonnet 4.5 in Claude Code roughly 70% of the time and over Opus 4.5 59% of the time. The release also reports gains on OSWorld computer use, prompt-injection resistance, OfficeQA document comprehension, and long-horizon planning, while adding adaptive and extended thinking, beta context compaction, and generally available code execution, memory, programmatic tool calling, tool search, and tool-use examples.
The last mile: why great first-party data still doesn't make great marketing
Modern data platforms and sophisticated marketing systems can still fail to produce timely customer experiences when no bridge connects first-party data to campaign activation. The post explains Scott Brinker’s “composable canvas,” a five-ring architecture centered on a unified data core, with semantic layer, CaaS, decisioning, and apps and agents operating on shared data without repeated movement. In contrast, batch files, dashboards, and disconnected teams can leave autonomous agents unable to trigger campaigns or propensity models unused. It recommends closing this last mile through activation-ready data architecture, self-service marketing analytics, and one narrowly scoped AI agent tied to a measurable campaign outcome. Examples in the post include a 4X model conversion rate, a four-week account scoring launch, and grocery offer flows that changed weekly preparation from hours to minutes.
Zone-Failure-Resilient OpenSearch® at Uber
Uber describes how it made OpenSearch deployments zone-failure resilient so querying and ingestion remain available after a complete zone loss. The design combines OpenSearch’s shard-allocation awareness with Odin-based isolation groups that distribute nodes predictably across failure domains and balance roles. Clusters maintain at least three shard copies across three isolation groups, while forced awareness keeps shards for a missing group unassigned instead of triggering heavy rebalancing; this trades immediate full replication for stability in surviving zones. For manager resiliency, five cluster manager nodes and cluster.auto_shrink_voting_configuration allow quorum after a zone failure plus one additional manager failure. Uber reports that isolation groups replaced uneven physical zone IDs, enabling complete shard assignment, preventing disk skew and hot nodes, and supporting green cluster health.
How Dow Built a Carbon Footprint Ledger on Databricks to Accelerate Sustainability at Scale
Dow built the Carbon Footprint Ledger (CFL) on Databricks to provide a transparent, verifiable way to track and communicate product carbon footprints amid customer Scope 3 commitments and tighter environmental disclosure requirements. The system combines carbon-accounting methodology assured against ISO 14067 and the GHG Protocol Product Standard with a calculation engine and certificate-issuing ledger that maintains residual balances. Within Dow’s Integrated Data Hub, Apache Spark unifies supply-chain, sustainability, operations, and other source data, while Unity Catalog governs access and Delta tables provide ACID transactions, time travel, and efficient upserts. An advanced optimization model, developed and deployed with MLflow, identifies lowest-greenhouse-gas production pathways and sends versioned, monitored results through the pipeline with lineage tracking. The implementation reduced PCF processing from weeks to a fraction of that, enabled portfolio-scale calculations, and supports verifiable certificates and commercial use of decarbonization investments.
Why R&D Data Belongs in the Lakehouse - and Why Agents Need It There
cellcentric describes how it built a governed lakehouse foundation for R&D data so industrial AI agents can reason over engineering context, not isolated source-system records. On Azure and Databricks, the Data Hub combines Unity Catalog governance, Lakehouse Federation, Delta Sharing, Declarative Automation Bundles, and an MCP interface for agents. Its Fuel Cell Passport unifies five enterprise systems, models seven hierarchy levels, refreshes daily, and uses a state-based temporal model for point-in-time configuration and rework-history questions. The platform treats context coverage as a data-quality metric, with 27 published products averaging 90% column-comment coverage, while authenticated user identity and Unity Catalog authorization constrain agent access. For many R&D and process-development workflows, work that once took weeks now ships in days, though complex multi-source investigations still require substantial domain review.
Announcing the Public Preview of Discover and Domains, powered by Unity Catalog
Databricks announces the Public Preview of Domains and the Discover page, powered by Unity Catalog, to help people and agents identify relevant, high-quality, and safe data and AI assets. Domains organize tables, dashboards, notebooks, queries, metric views, Genie Agents, and apps by business structure, while Discover provides an internal marketplace with search, certification signals, popularity and trending indicators, and AI-powered recommendations. Data stewards can create domains and subdomains, certify assets, add descriptions and contacts, and curate page sections and pinned content. Domains extend Unity Catalog Semantics and feed Genie Ontology, giving agents business context for narrowing retrieval, prioritizing trusted assets, and interpreting metrics within each user’s existing permissions. The features are available in Public Preview through a Databricks workspace, where organizations can create domains and curate assets.
Setting a World Record for MoE Pre-Training on NVIDIA GB300 NVL72
Frontier AI pre-training increasingly relies on mixture of experts architectures, which lower compute per token while introducing severe all-to-all communication bottlenecks. To address these scaling limits, the NVIDIA GB300 NVL72 rack-scale system co-designs Blackwell Ultra GPUs, fifth-generation NVLink scale-up fabrics, and 800 Gbps SuperNIC scale-out networking. Testing on DeepSeek-V3 671B across 256 GPUs achieved a pre-training benchmark of 1,648 TFLOPs per GPU using Megatron Core. In addition to hardware co-design, ongoing software optimizations across Megatron Core, TorchTitan, and JAX contributed major throughput gains over time. Scaling from 256 to 1,024 GPUs sustained over 97% per-GPU performance efficiency across all evaluated frameworks by overlapping scale-out gradient traffic behind compute windows.
Inside NVIDIA Rubin GPU Architecture: Powering the Era of Agentic AI
Agentic artificial intelligence workloads require sustained multistep inference, demanding low per-step latency, high decode throughput, and efficient long-context processing. NVIDIA designed the Rubin GPU to deliver up to ten times more agentic throughput per unit of energy than Blackwell. The hardware combines 336 billion transistors across two dies unified by NV-HBI, up to 288 GB of HBM4 memory providing 22 TB/s peak bandwidth, and the third-generation Transformer Engine reaching up to 50 petaflops of NVFP4 compute. Performance optimizations include inline descriptor updates for the Tensor Memory Accelerator, doubled K-dimension instruction throughput, 3-bit lookup-table weights, and structured 2:4 sparse attention compression. At the rack level, Vera Rubin NVL72 leverages Intelligent Power Smoothing and DSX MaxLPS to enable up to 40% more GPUs within a fixed power budget.
NVIDIA Vera CPU: Olympus Cores Built for Maximum Single-Thread Performance in Agentic AI
Agentic AI workloads shift critical execution paths onto CPUs through sandboxed environments that execute code, invoke tools, and manage runtime states. The NVIDIA Vera CPU addresses these compute requirements via Olympus cores built to maximize single-thread performance and instructions per cycle under full socket load. To handle irregular control flows and pointer-heavy structures, Olympus combines a ten-wide decode engine, neural branch prediction supporting two taken branches per cycle, dependency-breaking acceleration, and dedicated graph prefetchers. Across scale-up systems, dual-socket Vera configurations leverage second-generation NVLink-C2C interconnects to maintain a clean two-NUMA-node topology alongside PCIe 6.4, CXL 3.1, and Arm CCA confidential computing. These architectural choices provide predictable latency and memory bandwidth across highly concurrent reinforcement learning and agent loops.
Personalizing Airbnb search by learning from the guest journey
Airbnb replaced hundreds of hand-crafted ranking features with a Transformer-based sequence model that captures both long-term booking history and short-term browsing behavior. To keep latency low and throughput high, sequence representations are generated via daily batch jobs and combined with real-time queries using a co-trained setwise ranker.
How to structure a log
Logging arbitrary JSON objects often produces unstable messages and deeply nested data that complicate searching, aggregating, and debugging production incidents. Structured logging treats log output as real application data by establishing predictable, consistent schemas across services. Engineers should define stable event names using a domain.action pattern and capture dynamic dimensions with flattened, snake_case attributes formatted in dot notation. Attribute payloads must contain only primitive values or primitive arrays rather than arbitrary objects, utilizing low-cardinality result identifiers and explicit units for numeric measurements. Teams can enforce these structural conventions automatically with linting tools such as ESLint, while routing execution durations and timing measurements to tracing spans.
Supabase Pipelines is now in Public Alpha
Supabase Pipelines has moved into public alpha as a service for replicating Supabase Postgres data to external analytical systems in near real time. The release adds automatic application of supported schema changes, faster initial synchronization through parallelism, and a destination request form for ClickHouse, Snowflake, and DuckLake; Google BigQuery is the first destination available to everyone in public alpha. Powered by the open-source Rust-based Supabase ETL, Pipelines uses Postgres logical replication to copy existing tables, stream inserts, updates, deletes, and truncates, and resume from the last acknowledged position after interruptions. It provides at-least-once delivery and managed pipeline controls, while remaining an alpha product with ongoing work on throughput, batching, backpressure, broader schema support, and additional destinations.
Devin Outposts on Modal
Devin, an AI software engineer by Cognition, plans, writes, tests, and ships code semi-autonomously. Cognition now extends Devin with Outposts, moving execution into user-controlled environments while reasoning stays in Cognition’s cloud. Modal is a launch partner with modal-devin, an open-source integration running Outpost sessions in Modal Sandboxes with pre-built custom images, fast cold-starts, snapshotting, and cost-efficient bursting. For example, a task that requires a GPU fails immediately in the default sandbox but succeeds on a Modal-backed Outpost that spins up a GPU on demand and scales to zero when done. One command, uvx modal-devin init, sets up secure infrastructure with a scheduler and worker functions, and each team can have its own outpost with separate credentials.
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