iCreatAI News
Back to main site
Skip to content
Only First party#Deployment
Oct 30Fri
  1. Pydantic AI Articles42

    Rendreal takes print orders inside the assistant you already use

    Rendreal, built with Vstorm using Pydantic AI, lets customers describe a print job and receive print-ready artwork, a quote and a checkout link without leaving a chat, via web chat, a WhatsApp bot or any external assistant connecting to its MCP server. The system covers five product types across three channels, with Pydantic AI validating tool arguments and structured outputs before orders reach validation. Rendreal says assistants get access to its own integrated services, not an open network of independent supplier agents.

  2. OpenRouter Announcements52

    Guardrails Now Separate Organization Limits from Workspace Defaults

    OpenRouter rebuilt Guardrails so organization admins can set workspace-wide limits (budget caps, ZDR requirements, model and provider access) that apply to every member and API key, while workspace admins set defaults and per-person exceptions inside those limits. Guardrails are now split into three separate policy types—budget, model and provider access, and content moderation—so each can be changed independently. Existing guardrails keep their IDs and rules, and the guardrails API and Terraform resources continue to work as before.

  3. Factory Blog78

    Droid 1.0: Building a Frontier Harness

    Factory released Droid 1.0, an agent harness it says outperforms native harnesses built for their models by up to 20% more efficiently on the same tasks, with programmatic tool calling (Script mode) now the default for models including GPT-6.1 Sol, Claude Opus 5.5 and Kimi K3. The post reports that Script mode cuts Droid's cost by 15% on Claude Opus 5.5, that the base prompt shrank 37% between versions 0.170.0 and 0.250.0, and that median time to first paint fell from 1,839 to 488 ms. It also describes the internal Agent Factory loop, in which agents propose harness changes that engineers review before shipping.

    Why it matters: A detailed first-party account of harness design choices, benchmark methodology and latency work that is useful for teams building or evaluating coding agents.

  4. Databricks Blog42

    Announcing General Availability of Real-Time Mode for Spark Declarative Pipelines on Databricks

    Databricks announced that Real-Time Mode (RTM) in Spark Declarative Pipelines (SDP) is now Generally Available, bringing millisecond-range end-to-end latency to SDP while retaining its orchestration, incremental processing, failure recovery and data quality features. RTM streams data continuously and runs all query stages simultaneously, and Databricks reports it was up to 92% faster than Apache Flink on customer feature-computation workloads. Users enable it by setting the pipeline to continuous mode with spark.databricks.streaming.realTimeMode.enabled set to true and defining an update flow with pipelines.trigger set to "RealTime", and can write to custom sinks such as Lakebase or Redis via a ForEach sink.

  5. ElevenLabs Blog36

    What is turn taking in conversational AI: A guide to natural flow

    This ElevenLabs guide explains turn taking in conversational AI as the rules that determine when a voice agent should reply, hold the floor, or stay silent, relying on adaptive timing, voice activity detection, and semantic analysis rather than strict silence timers. It describes how ElevenAgents detects speech, silence, and intent, handles interruptions and backchannels, and lets teams tune settings like turn eagerness and silence timeouts to avoid premature endpointing, dead air, and false triggers. It also covers measuring quality through interruption rate and response latency, citing a 142 ms median time from request to audible speech with Eleven v4 Turbo and a 200 ms average human turn gap from Journal of Cognition research.

  6. AWS Machine Learning Blog38

    Building intent-driven UI testing with Amazon Nova Act

    Epsilon's Agentic Platform team built a multi-tenant UI testing service on Amazon Nova Act and Amazon Bedrock AgentCore Browser Tool, layering natural-language, intent-based tests on top of its existing Playwright regression suite. The service runs each workflow's plain-English steps through Nova Act over a CDP WebSocket in managed, ephemeral browser sessions, capturing per-step screenshots and pass/fail results. Epsilon reports 4-6x faster test authoring (an 18-step workflow took 5-10 minutes versus 30-90 minutes for an equivalent 80-150-line Playwright script) and a 100 percent pass rate on saved trajectory replays.

  7. Prime Intellect Blog34

    ResearchOCT 29TH, 2026Prime Mega MoE: Fast Fused MoE Kernels for Training

    Prime Mega MoE extends DeepSeek's Mega MoE kernel, which previously had only a forward implementation, with a fast overlapping backward pass so it can be used for training. It reports up to 3.11× faster MoE kernels than the default PyTorch-based implementation and up to 1.34× faster end-to-end training than the Prime-RL baseline (34% higher throughput). Supported data types are bfloat16, MXFP8 (Blackwell only) and blockwise FP8 (Hopper only).

  8. AWS Machine Learning Blog55

    Build a repeatable PHP-to-Java migration engine with Kiro

    This AWS post describes a PHP-to-Java migration engine packaged as versioned Kiro skill files (php-model-discovery and php-to-java-migration) that can be run either interactively in Kiro or agentically at portfolio scale via AWS Transform. The engine uses a dependency-free PHP scanner to emit a JSON manifest of models, fields, relationships and review flags, then follows an eight-phase, parity-first workflow in which a parity harness is built before any Java is written and the PHP code serves as the specification. A sample project migrates four Laravel Eloquent models to Spring Boot 3.4.1 on Java 21 with Maven and H2, and the post notes the parity harness was not built for that slice, calling it "the single biggest gap."

  9. Baseten Blog30

    Baseten and Postman: Building reliable, cost-efficient agents for the enterprise

    Postman's Agent Stack (Passport, Fabric, Astropods, Fern) provides tools to build, secure, and govern enterprise agents, while Baseten serves as its recommended inference partner, offering fast model APIs, multi-cloud capacity management with zero data retention by default, and post-training infrastructure. Baseten cites seven 2026 model launches ranking in the top two on Artificial Analysis for throughput and latency, and notes Harvey post-trained a 27B open model to match closed-source frontier performance on its legal agent benchmark. The post describes post-training via supervised fine-tuning and reinforcement learning, with customers retaining model weights, evaluations, and training scripts.

  10. Claude Platform Release Notes22

    Claude Platform release notes — November 12, 2026

    The Claude Platform release notes list changes to the Claude API, client SDKs, and Claude Console, newest first, with entries from September 1 through October 29, 2026. Recent items include the beta Usage and Cost Analytics API, a raised 128 MB maximum request size for the Messages and Token Counting APIs, the beta Claude Tag API for Claude Enterprise, Claude Models on Google Cloud (Anthropic managed), and API version 2026-10-21 with new SDK and CLI releases. Earlier entries cover the launches of Claude Opus 5.5, Claude Sonnet 5.5, Claude Haiku 5.5, and Claude Fable 5.1, along with user profiles, organization webhooks, and OpenTelemetry export.

  11. Amazon Science38

    Speeding up language models for travel applications

    Researchers combined knowledge distillation, which trains a compact student model on a large teacher's outputs, with Medusa, a framework that predicts multiple tokens in parallel, and applied the pipeline with Booking.com to three travel features. The final model matched the quality of a 70-billion-parameter teacher while running 10 to 20 times as fast, with reported gains including 2.9% higher engagement versus GPT-3.5 for AI Trip Planner and 15-times-faster Smart Filters responses for 99% of queries. The work received the Industry Track best-paper award at ACL.

  12. AWS Machine Learning Blog58

    How MoEngage turned over 400 APIs into agent tools with AgentCore Gateway

    MoEngage used Amazon Bedrock AgentCore Gateway to expose roughly 400 internal REST APIs across seven AWS Regions as permission-scoped agent tools, avoiding over 20 separate MCP servers and per-API networking. Cedar policies filter tools server-side per agent identity, managed egress over Amazon VPC Lattice keeps APIs private, and teams expose operations by annotating OpenAPI specs rather than writing MCP code. MoEngage estimates the approach saved about six months of platform engineering effort, with new API onboarding now taking hours instead of days.

  13. AWS Machine Learning Blog36

    Lend and borrow resources across teams with AWS Batch’s quota management for SageMaker Training jobs

    AWS Batch now offers quota management, letting administrators divide compute capacity into quota shares per team or project with RESERVE, LEND, or LEND_AND_BORROW sharing strategies, plus cross-share and optional in-share preemption. The post walks through configuring a Scheduling Policy, Service Environment, and job queue, then submitting SageMaker Training jobs to quota shares via the SageMaker Python SDK v3 aws_batch module.

  14. Meta Engineering58

    Catching Bugs Before They Land: JiTTesting’s Human-on-the-Loop Approach at Full Scale

    Meta reports that its Catching JiTTests system, which uses LLMs to generate executable tests that fail on risky code diffs before they land, is now part of its production infrastructure rather than a research prototype. The system combines intent-aware and "dodgy diff" test-generation workflows with an LLM-as-Judge ensemble and a rule-based assessor, and uses a human-on-the-loop model where engineers review outcomes at an aggregate level. Meta says 589 engineers (64% of outreach) engaged with the system, which showed a 60% true-positive rate and helped catch 74 serious privacy bugs at the diff stage in the first half of 2026, lowering privacy SEVs by 20%.

  15. AWS Machine Learning Blog41

    Accelerate agentic AI with NVIDIA Nemotron models on Amazon EKS and SageMaker HyperPod EKS

    NVIDIA Nemotron 3.5 Super VL is a 120B-parameter mixture-of-experts model activating 12B parameters per token that handles text, image, document, and video input, joining other Nemotron models already on AWS. The post describes its hybrid Mamba-2, MoE, and attention architecture with a RADIO vision encoder and up to 1M-token context, then shows deployment on Amazon EKS, EKS Auto Mode, and SageMaker HyperPod EKS using NVIDIA Dynamo and vLLM, with validation and performance results on B200 and H200 instances.

  16. NVIDIA Blog40

    Into the Omniverse: How Developers Turn Ideas Into Simulations With Frontier AI Agents

    NVIDIA describes a series of developer projects in which frontier AI models such as GPT-6 Astra are directed through natural-language instructions to build simulations using Omniverse libraries for GPU-accelerated physics, rendering and sensor simulation. Examples include recreating an OP-1 synthesizer in Houdini, testing robotic disassembly in Isaac Sim, reconstructing buildings from site scans, and a gas-turbine what-if application. Other projects cover SimReady asset scene assembly, warehouse performance scaling, cable routing, CAD-to-SimReady conversion, humanoid simulation, autonomous-driving testing, sensor-based digital twins, Robo Olympics, an ISS browser model and room reconstruction.

Oct 29Thu
  1. vLLM Blog62

    Day 0 support for Mistral Large 4 on vLLM

    vLLM announces day-0 support for Mistral Large 4, a 1T-parameter MoE multimodal model with 49B active parameters per token, running on the V2 model runner with breakable CUDA graphs and a new vllm/models/mistral/ package. The work adds cross-layer KV sharing, DCP/PCP context parallelism, DSpark speculative decoding, NIXL-based disaggregated serving and HiSparse KV offload, plus day-0 ROCm support on AMD Instinct MI350 Series GPUs with MXFP4 experts. Support ships in the vllm/vllm-openai:mistral-large-4 Docker image, with FP8 and NVFP4 checkpoints and serving commands documented.

    Why it matters: A detailed engineering account of what it takes to land a frontier model on an inference engine, useful for teams planning long-context or disaggregated deployments.

  2. Hugging Face Blog36

    Granite Switch 4.1.1 — Update models like you update software

    Granite Switch 4.1.1 is a backwards-compatible patch release to Granite Switch 4.1 that adds an updated Granite Speech ASR model covering multilingual speech-to-text across 10 languages with native punctuation and capitalization, plus an updated Decision adapter in granitelib-core with improved performance on the Jev Decision Index test set. Apart from training the new ASR model and Decision adapter, nothing was recomputed and the base model is untouched. The team says it is working on tutorials and an Adapter SDK so developers and third-party providers can build their own adapters.

  3. ElevenLabs Blog25

    How we use ElevenAgents to save 1,200 hours a month in daily operations

    ElevenLabs says its internal agents save teams over 1,200 hours a month, answering more than 5,000 questions monthly and taking actions such as building customer demos and opening over 150 tickets in the last month. The agents span sales (GTM and FDE bots), support and engineering triage, and product data analysis, with lessons including schema curation over prompt engineering, injecting Salesforce picklist values into tool type definitions, pruning overlapping procedures, and verifying tickets after creation.

  4. n8n Blog22

    Why we're moving n8n's automation team beyond the service desk model

    Romina da Costa, n8n's head of internal AI and automation, argues that a centralized automation team that only builds workflows shifts the bottleneck downstream rather than fixing it, and that with over 12,000 internal workflows no team can build and maintain them all. She describes reframing the team's goal from throughput to building an automation culture, guided by four principles: build the builders, require a baseline before building, choose the tool for the problem, and make what's built resilient. She notes the team still operates like a service desk more often than she'd like, and that shifting culture requires leadership buy-in and cross-functional change.

  5. Amp Chronicle58

    macOS Satellites

    Amp now offers macOS Satellites: a cloud Mac with Xcode and simulators, booted in about 30 seconds, attached to the thread's Linux orb so Amp can build, run, and test iOS or Mac apps while code stays synced on the orb. Macs start fresh with nothing saved, stay running for 20 minutes after Amp finishes so follow-ups can reuse the same Mac and build cache, and are then deleted. Pricing is $2/hour for mac.medium (6 CPUs, 14 GB) and $4/hour for mac.large (12 CPUs, 28 GB), drawn from included orb usage and then Amp credits; the feature is in beta with a waitlist in Settings → Experimental Features.

  6. Sakana AI Blog38

    Sakana API Platform、エンタープライズ向けにアーリーアクセスプログラムを開始

    Sakana AI announced the Sakana API Platform for enterprise customers and opened its Early Access program, offering its LLMs via an OpenAI-compatible API with management features for handling usage costs and members by organization and workspace. Sakana Fugu and Sakana Fugu Ultra are available first, with Sakana Namazu and Sakana Fugu Max to follow, and features include role-based access control, audit logs with CSV download, a commitment not to use inputs for training, budget limits, invoice payment, and MSA and DPA support.

  7. OpenRouter Announcements60

    How we rebuilt provider routing for better, faster inference

    OpenRouter announced a new default provider routing algorithm that scores endpoints on realized price, time to first token, throughput, error and rate-limit rates, and output quality rather than input token price alone. In a week-long switchback experiment over 24 million requests, the new router cut TTFT p50 by 16.9%, raised throughput p50 by 16.3%, and reduced requests hitting at least one 429 by 67%, with cost per K requests up 6.0%. Explicit sorts, provider.order, and cache-aware conversation pinning still work as before, and price-only routing remains available via provider.sort "price" or the :floor suffix.

    Why it matters: Useful for teams on OpenRouter deciding whether to keep default routing or opt back into price-only sorting, since it quantifies the speed, reliability, and cost tradeoffs.

  8. Databricks Blog28

    How Procore is powering construction analytics with OpenSharing

    Procore rebuilt its Procore Analytics product on a Databricks medallion architecture and OpenSharing, replacing a SQL Server backend that capped refreshes at one to three per day and relied on costly, brittle recursive API calls. The new design shares hundreds of curated, Unity Catalog-governed tables per customer at schema, view, table, and partition level, with Change Data Feed for incremental syncs and automated per-user, per-project entitlements. Procore says the catalog expanded to hundreds of tables in less than half the time the first generation took, and customers can now refresh on their own cadence.

  9. Databricks Blog28

    How Procore is powering construction analytics with OpenSharing

    Procore rebuilt its Procore Analytics product on a Databricks medallion architecture and OpenSharing, replacing a SQL Server backend that capped refreshes at one to three per day and relied on costly, brittle recursive API calls. The new design shares hundreds of curated, Unity Catalog-governed tables per customer at schema, view, table, and partition level, with Change Data Feed for incremental syncs and automated per-user, per-project entitlements. Procore says the catalog expanded to hundreds of tables in less than half the time the first generation took, and customers can now refresh on their own cadence.

  10. ElevenLabs Blog23

    Conversational AI in banking: Use cases, benefits, examples

    This guide explains how banks use conversational AI—NLP-based chat and voice agents that understand natural language, pull account context, and escalate to humans when needed—for 24/7 support, onboarding, disputes, and fraud alerts. It cites CFPB findings on early bank chatbots, McKinsey figures on reduced handling time and call volume, and ElevenLabs customer examples from Customers Bank and Klarna, while detailing security standards such as SOC 2 Type II, ISO 27001, PCI DSS, and Zero Retention Mode.

  11. Google Developers Blog58

    Smaller apps and faster GPU inference with LiteRT in Google Play services

    LiteRT in Google Play services now offers GPU inference about 1.4x faster than the legacy GPU delegate via ML Drift and the new CompiledModel API, and apps that switch are about 8 MB smaller because the runtime ships with Play services instead of inside each app. The runtime supports phones back to Android 7, plus Wear OS 4+ and Android Automotive OS (Android 11+), and the legacy GPU delegate and custom delegate APIs are deprecated, with NNAPI to be removed starting in 2027.

  12. Sierra Blog42

    Introducing Compliance Console

    Sierra announced Compliance Console, built into its Horizon platform, which gives companies configurable guardrails so their agents follow outreach rules across channels, use cases, states, and countries. The controls cover disclosures, contact windows based on recipient local time, holiday exclusions, frequency limits, and opt-outs, with Insights dashboards showing how many outbound calls and messages were allowed, deferred, blocked, or held for review.

  13. PyTorch Blog47

    PyTorch 2.15 Release Blog

    PyTorch 2.15 makes the rewritten NCCL backend the default for all nccl process groups, with a flight recorder that dumps diagnostics on hangs and TORCH_DIST_USE_NCCL2=0 or nccl-legacy as rollback paths. It adds one-sided transports for torch.distributed with a NIXL backend, finer FSDP2 and pipeline parallelism memory and communication controls, NVFP4 decode improvements on Blackwell, FP8 and faster decode on Apple Silicon, and broader platform support including FlyDSL on AMD gfx950, ROCm 10.0 wheels, CUDA 13.2 as the default wheel, and torch.compile on Python 3.15. The release contains 3,117 commits from 583 contributors since 2.14, and Python 3.10 and macOS 14 are no longer supported.

  14. ElevenLabs Blog55

    How your transcript knows where each word is spoken

    ElevenLabs introduced Cue, a forced alignment model that places every character in an audio file on a precise timeline, powering its Scribe speech-to-text family and available through a Forced Alignment API. ElevenLabs says Cue was more accurate than every aligner it tested across all 29 benchmark languages, handles recordings of any length in a single pass, and aligns an hour of audio in under a second. It also aligned songs in English, French, Spanish and German in one pass, placing 92.7% of words within 0.3 seconds of the label.

  15. AWS Machine Learning Blog23

    Accelerating software delivery with agentic QA automation using Amazon Nova Act – Part 3

    This third post in the series describes two QA Studio capabilities for scaling agentic QA across an organization: applications, which group use cases and test suites under an owner and report health via pass rate, a flaky-adjusted 0–100 score, trend charts, and failure triage built on Amazon CloudWatch custom metrics; and team-based user management, which assigns five cumulative roles (Admin, Team Lead, Editor, Executor, Viewer) through Amazon Cognito groups, with cross-team sharing capped by the recipient's role. The backend enforces access through a Lambda authorizer resolving Cognito groups into scopes plus a resource check on application ownership or sharing, and the reference solution is deployed with the AWS CDK.

  16. AWS Machine Learning Blog36

    Distributed training with Ray Train DLCs and EFA on EKS

    This AWS post shows how to fine-tune Qwen2.5-1.5B with PyTorch FSDP across eight NVIDIA L4 GPUs on two EFA-enabled g6.12xlarge instances on Amazon EKS, using the published Ray Train DLC (public.ecr.aws/deep-learning-containers/ray:train-ml-cuda-v1.1) without building any image. KubeRay creates the Ray head and worker pods from one RayCluster manifest, while hardware, transport and per-job Python packages are configured from outside the image via resource requests, environment variables and Ray runtime_env. The sample repository includes idempotent deploy scripts that locate g6.12xlarge capacity, provision the cluster and node group, install the GPU and EFA device plugins and KubeRay, and submit the job from inside the head pod, with the script failing if NCCL did not use EFA.

  17. AWS Machine Learning Blog42

    Building an Agentic AI Platform on Amazon Bedrock AgentCore

    This AWS post describes an enterprise Agentic AI Platform blueprint on Amazon Bedrock AgentCore that separates a centrally governed Platform control plane from repeatable, team-owned Workstream cells, with separate inference and tool gateways and a federated operating model. It details multi-account topology, service control policies, a CI/CD pipeline with deployed-runtime evaluation gates and adversarial denial tests, and dependency-ordered retirement, using the eu-west-1 (Ireland) Region. The companion GitHub blueprint lets teams deploy a representative Workstream cell and validate its security and lifecycle contracts end to end.

  18. Pydantic AI Articles74

    Code mode: make your agents 2x faster and 2x cheaper on long tasks

    A benchmark of 5,276 graded runs across eight models on five small Pydantic AI apps found that code mode, where the model writes a program that calls tools inside a sandbox, cut cost by 58% and wall time by 51% on long tasks while raising success by 16 points. Gains grew with task length: at 200 items code mode was 47% to 75% cheaper and 42% to 67% faster, whereas at 5 items it cost 21% to 38% more on three of five tasks. The author attributes the win not to fewer round trips, since models already batch calls, but to less content per request, and runs the model's code in Monty, a Rust Python interpreter with no host access by default and 30-second, 256 MiB limits.

    Why it matters: Offers a detailed, methodology-heavy benchmark with per-model cost, latency and correctness numbers, plus an honest list of where code mode costs more.

  19. Claude Blog34

    Smart reports, surveys, and pooled group budgets for Claude Enterprise

    Anthropic announced that smart reports, in-app surveys, and pooled group budgets are now generally available for Claude Enterprise. Smart reports analyze a sample of a team's transcripts to group work into workstreams with spend, deliverables, friction points, and recommendations, while in-app surveys collect team feedback and pooled group budgets give a group one shared monthly budget with member limits as a guardrail. Availability varies by configuration, including HIPAA configurations and customer-managed encryption keys.

  20. Baseten Blog55

    The fastest, lowest-cost batch transcription with Qwen3-ASR

    Baseten optimized Qwen3-ASR 1.7B, an Apache 2.0 open-weight speech recognition model supporting over 30 languages and 22 Chinese dialects with a 5.4% average WER on the Open ASR Leaderboard, for batch transcription on a single RTX PRO 6000. The optimized stack transcribes 7x more audio per second than out-of-the-box vLLM at high concurrency, reaching 2,645x real-time factor on 5-minute clips and 3,128x on hour-long files, with median latency for 5-minute clips at 64 concurrent requests falling from 50 seconds to 7.3 seconds. Baseten reports a cost of about $0.0013 per audio hour, 8x lower than the same model on vanilla vLLM and roughly 277x less than popular closed-weight providers.

  21. Databricks Blog36

    Voice Agents are here. Context turns them into real customer value.

    Databricks argues that voice agents are now practical because synthetic voices sound natural and can respond within roughly 200 milliseconds, but that most enterprise AI investment has focused inward on employee assistants rather than customer conversations. It describes a prototype in which LiveKit carries calls, Deepgram handles speech-to-text, ElevenLabs adds voices, and Databricks supplies the "brain" that looks up caller context, applies business rules, and records each decision, built on context, ontology, and governance. The post outlines three pillars—grounding answers in business data, fast and slow AI decision-making including the ai_decide capability, and a learning loop from recorded calls—and points to the open-source DIVA blueprint on GitHub and a suggested 90-day pilot.

  22. Mastra Blog36

    Introducing Span Queries

    Mastra now offers span queries that filter and group individual spans across traces, letting you return only matching spans rather than whole traces. Span queries are available in Mastra platform and Studio, the Mastra CLI ([email protected] or later), the Mastra Client SDK, and HTTP observability endpoints, and work on self-hosted projects where storage.observability uses DuckDB, PostgreSQL, or ClickHouse. Each query can combine up to 100 conditions, returns up to 1,000 spans per page, and span groups can group by up to 2 dimensions over a time range of up to 365 days.

  23. Replit Blog42

    Replit’s Architecture Behind Secure Agent Work

    Replit outlines four principles for securing AI agents in its Agent Habitat: least-privilege access with a credential broker that keeps secrets outside the agent's context, isolation via microVMs and separate development/production databases, engineering review of agent-proposed production changes, and audit logging with encryption and BYOC options. It says its existing microVM architecture prevented exploitation of recently disclosed Linux kernel vulnerabilities and that it patched affected Node.js, Python, and PHP tooling and deployed firewall rules centrally. Replit also describes round-the-clock AI red teaming and plans for finer-grained per-task permissions and eBPF-based sandbox monitoring.

  24. OpenRouter Announcements38

    Claude 529 Overloaded: What It Means and How to Fix It

    A 529 overloaded_error means Anthropic's API is temporarily out of capacity for the requested model across all users, unlike a 429, which reflects your own usage limits. The article recommends retrying with exponential backoff and jitter, then routing the same model to another provider via OpenRouter, which serves Claude Sonnet, Opus and Haiku 5.5 through five providers each, and optionally listing a fallback model with `models`. For Claude Code, it suggests switching models with `/model` or running Claude Code on OpenRouter via Ori Harness or three environment variables.