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Nov 13Fri
  1. Claude Blog40

    The Claude Tag guide for startups

    Anthropic's guide describes how eight high-growth startups use Claude Tag (beta) in Slack, covering common use cases such as on-call triage, turning threads into pull requests, a company knowledge base, self-serve data questions, and routine reporting. It includes example channels, connectors, starter instructions, and reported results such as a PR opened 21 minutes after a Slack mention and 134 merged PRs at NavigateAI. A getting-started checklist notes Claude Tag is in public beta on Team and Enterprise plans and requires an org Owner and Slack admin to connect.

Nov 12Thu
  1. Hugging Face Blog38

    The Definitive Fine-Tuning Guide for 3D Generation Models

    This blog provides fine-tuning scripts and Docker containers for adapting TRELLIS.2 to custom 3D assets, runnable locally or on Hugging Face Jobs. Using LoRA adapters on only the sparse-structure and shape flow DiTs, trained on 188 GSO footwear objects, the authors report the mean F-score on nine held-out objects rising from 0.276 to 0.803, Chamfer distance falling from 0.174 to 0.033, and normal consistency rising from 0.530 to 0.804. The texture flow DiT is frozen because the dataset lacks the separate material maps that stage requires.

  2. PyTorch Blog40

    Integrating SGLang into Spyre in Under a Week with AI

    A two-person team used AI coding agents to integrate SGLang with IBM Spyre in under a week, producing a plugin of roughly 1,500 lines built on existing PyTorch Spyre support and SGLang's extension points. SGLang delivered about 3.6× the throughput of the reference PyTorch inference implementation using torch-spyre and was on par with vLLM on the measured workloads for Granite 3.3 8B and Gemma 4 26B A4B. Prefix sharing through RadixCache worked without any Spyre-specific changes.

  3. PyTorch Blog58

    Training a 1T-Parameter MoE on EU public infrastructure with PyTorch and TorchTitan

    The EUROPA consortium (Domyn, Fraunhofer IAIS and IIS) describes scaling pretraining of a ~951.9B-parameter MoE model (32.8B active per token) on JUPITER, the EuroHPC exascale supercomputer at Forschungszentrum Jülich, raising throughput from 90 to ~1,300 tokens/sec per GPU across 384 NVIDIA GH200 GPUs per DP replica. Key optimizations built on PyTorch and TorchTitan include FlexShard placement for the Muon optimizer, activation-checkpoint offloading to the Grace CPU, and running FSDP's all-gather setup on the compute stream via unshard_async_op, freeing ~14 GiB of HBM per GPU. The fixes were upstreamed to PyTorch and TorchTitan, with training reaching ~1,300 tokens/s per GPU on 1,536 GPUs across 4 DDP replicas.

  4. AWS Machine Learning Blog36

    Build autonomous AI agents with Amazon Bedrock and Spring AI

    This post shows how Java teams can build autonomous AI agents on the JVM using Spring AI and Amazon Bedrock AgentCore, with a single-agent travel assistant demonstrating tool calling, streaming, and memory, and a multi-agent intelligent document processing system with five specialist agents, a SpEL rules engine, and alerting. It covers the AgentCore runtime contract (@AgentCoreInvocation for POST /invocations and GET /ping), @Tool methods, structured output to Java records, memory advisors, and production patterns including Resilience4j retries, twelve-factor configuration, least-privilege IAM, Bedrock Guardrails, and Micrometer observability. Deployment uses an ARM64 image pushed to Amazon ECR and provisioned through modular Terraform, with an optional Amazon EKS and IRSA path.

  5. OpenRouter Announcements58

    Self-Hosted LLM vs API: When Does Self-Hosting Pay Off

    At October 8, 2026 prices, a 27B model on one rented RTX 3090 breaks even against API pricing at roughly 6–12% load, while DeepSeek V4.1 Flash on four rented H200s never beats the cheapest zero-data-retention API endpoint. The post computes break-even load as GPU hourly rent divided by the API cost of the same hour of tokens, using live per-provider prices and benchmark throughput, and notes that published self-hosting costs near a cent per million tokens mostly count cached input tokens that are also nearly free via API. It also covers privacy options such as provider.zdr and EU/US in-region domains, and suggests self-hosting a steady base load while sending peaks to the API.

  6. OpenRouter Announcements36

    How to Benchmark LLMs

    A guide to benchmarking LLMs by writing tasks with script-checkable answers, grading with code, and comparing models on identical tasks via OpenRouter using graded accuracy, billed cost, and request time. On 60 synthetic tasks, GPT-6.1 Sol passed 180/180 trials while DeepSeek had the lowest response cost at 157/180 passes, and agreement with the public Artificial Analysis ranking was weak (Spearman correlation 0.109). The article includes a downloadable Python companion package with offline checks, configuration examples, and guidance on repeats, missing answers, and run comparison.

  7. OpenRouter Announcements58

    How to Stop an AI Agent Stuck in an Infinite Loop

    OpenRouter describes four guards against runaway agent loops, tested on six models with its Agent SDK (@openrouter/agent 0.11.0): doom-loop detection, a loopKey that normalizes tool arguments, stopWhen caps on steps and cost, and a credit limit on the API key. With doomLoop: true, all six models were blocked on their third identical tool call, and across 18 runs detection cut tool calls from 314 to 64 and spend from $0.29 to $0.05. The article notes that loops vary between runs and that only the key-level limit caps total spend when agent code is buggy or caps are missing.

  8. AWS Machine Learning Blog46

    Detecting and recovering from silent data corruption on Amazon SageMaker HyperPod

    This post describes adding three checks to a PyTorch DDP training loop to detect silent data corruption (SDC), where hardware such as a GPU returns a wrong but plausible number without raising an error: a non-finite gradient check, a ring replay audit that recomputes another node's micro-batch and compares loss bits and gradient fingerprints, and a weight fingerprint comparison after the optimizer step. When a check fires, the suspect rank is mapped to its node via the Kubernetes node name, the node is cordoned, and SageMaker HyperPod replaces it, with training resuming from a checkpoint saved before the suspect window. The checks were tested with software-injected faults on a HyperPod cluster with 4 ml.p5.48xlarge instances (32 NVIDIA H100 GPUs) and repeated on three instances, with full code in an accompanying GitHub repository.

  9. AWS Machine Learning Blog34

    Semantic diffing of formal policies with anti-unification

    The post describes an open source engine that computes semantic diffs of formal policies by anti-unification over an e-graph, using declared algebraic laws and domain rewrites so that shared structure appears once and each genuine disagreement is kept as a Variants node. Worked examples cover SMT-LIB-style rules, Cedar and Dogwood authorization policies, and OpenShell and CEL encodings, showing how presentational differences are absorbed while real bugs such as a weakened conjunction or a dropped guard remain visible. The engine builds from source with a pinned Rust toolchain and reads a single S-expression file.

  10. AWS Machine Learning Blog38

    OpenAI Codex on Amazon Bedrock

    This AWS post explains how developers can run OpenAI Codex on the workstation while routing model inference through Amazon Bedrock using supported OpenAI models such as GPT-5.6 Sol, Terra, and Luna. It compares three access paths—Native AWS Access with IAM Identity Center, AgentCore Gateway, and an LLM Gateway—and recommends starting with a small, least-privilege pilot before expanding. It also covers validation steps, identity-isolation testing, OpenTelemetry and CloudWatch observability, cost allocation, plugins, hooks, MCP servers, phased rollout, and cleanup.

  11. Databricks Blog28

    Detecting credential-driven attacks on Databricks

    Databricks describes how credential-driven attacks unfold on its platform in six non-linear stages—initial access with valid credentials, environment mapping, credential harvesting, execution via UDFs and jobs, persistence, and data collection with cleanup—and what to look for in verbose audit logs at each stage. The post explains how to investigate these attacks with Genie and the Detection App, how to respond once a compromise is confirmed, and which preventive controls such as context-based ingress, least privilege, and credential rotation limit what a stolen credential can do.

  12. ElevenLabs Blog22

    Voice cloning for content creators: Scale your content workflows with voice AI

    This guide explains how voice cloning turns a recorded voice sample into a digital replica that can narrate new scripts for videos, podcasts, courses, and social clips, covering the capture-modeling-generation process and the difference between Instant and Professional Voice Cloning. It lists criteria for choosing a tool—voice fidelity, delivery control, language coverage, consent safeguards, export options, and commercial licensing—and outlines workflows for podcasters, YouTube, short-form content, courses, and audio articles. It also describes ElevenLabs' consent confirmation, voice verification, restricted voices, and traceability safeguards, plus which plan tiers include cloning and commercial rights.

  13. Browser Use Blog34

    How to Use Custom Proxies with Browser Use

    This tutorial shows how to connect a third-party (bring-your-own) proxy to Browser Use Cloud by supplying the provider's host, port, protocol, and credentials when creating an agent run or cloud browser, then verifying the browser's exit IP and country via ipwho.is. It also demonstrates using the same credentials with Playwright both locally and against a Browser Use cloud browser over CDP, and compares managed proxy pricing ($5/GB) with BYO (provider rate plus $0.20/GB Browser Use egress). The guide includes setup steps, code samples, a troubleshooting table, and production tips such as preserving cookies alongside sticky sessions and stopping unused browsers.

  14. Pydantic AI Articles34

    What we found testing OpAMP clients against the spec

    Pydantic describes how its Logfire Fleet Management OpAMP clients for Rust, Go, Node.js, Python, Java and .NET share one Rust protocol core, run as WebAssembly or a native library, and are checked against roughly 300 normative spec statements, TLA+ models, Kani and Verus proofs, black-box conformance and fault testing. Running the same scenarios against upstream OpenTelemetry clients surfaced bugs in the Collector's OpAMP extension and Supervisor and in the Java, Python and .NET clients, including instance UID handling, reconnect and status reporting, capability declarations and startup panics, plus one critical security vulnerability that the core is not affected by.

  15. AWS Machine Learning Blog40

    How DXC Technology built a multi-model AI substrate on Amazon Bedrock

    DXC Technology built DXC OASIS, a harness for mission-critical IT operations, on a governed multi-model substrate in Amazon Bedrock that provides access to Anthropic Claude, Amazon Nova, Meta Llama, and open-weight models through one managed interface, with Bedrock Guardrails, AWS PrivateLink, and serverless scaling as system-level controls. DXC separates the substrate from its own orchestration, governance, and assurance layer, and reports that since general availability in March 2026, DXC OASIS has reduced resolution time by 95% on average across 56 measured use cases. The post describes the architecture, model routing, and authority tiers, and states that DXC is evaluating Amazon Bedrock AgentCore alongside its current orchestration layer.

  16. AWS Machine Learning Blog34

    Automating AWS Agent Registry updates with push-based architecture

    This post describes an event-driven pipeline using AWS CloudTrail, Amazon EventBridge, and AWS Lambda that detects Amazon Bedrock AgentCore UpdateAgentRuntime events and syncs MCP server tool schemas into AWS Agent Registry records, supporting both single- and multi-account setups. The Lambda extracts the runtime ARN, obtains an OAuth bearer token via AgentCore Identity, calls the MCP server's initialize and tools/list methods, then updates the matching APPROVED registry record only when normalized tool definitions differ. Updated records revert to Draft status and require curator approval before becoming discoverable, and the post notes limitations such as no automatic registration of new servers and no retry on cold starts.

  17. AWS Machine Learning Blog42

    Accelerating generative AI delivery with reusable assets

    AWS's Generative AI Innovation Center, now part of AWS Frontier AI Engineering & Services, offers a catalog of 23 production-ready open source reusable assets such as RAG pipelines, document processors, and agent scaffolding. The post cites DoorDash and Lyft using the Contact Center GenAI Agent and Ricoh using the GenAI Intelligent Document Processing accelerator, reporting results including a 50x increase in testing capacity and a 7-fold throughput increase. It also walks through deploying the Contact Center GenAI Agent and lists the full asset catalog on GitHub and AWS Labs.

  18. Comfy Blog58

    Doubling LLM token rates in ComfyUI

    ComfyUI rewrote the hot path of local text generation for Qwen3.x LLMs, letting the same checkpoints on the same GPU generate tokens up to twice as fast with no change to model or output quality; on an RTX 5090, Qwen3.5 9B went from 106 to 219 tokens per second. The changes include a re-laid-out GEMV kernel, fused prologue operations, new attention and DeltaNet decode kernels, and an adaptive-tree MTP speculative decoding algorithm, all available in ComfyUI v0.45.0 with the Text Generate node's mtp option left on auto.

  19. Amazon Science33

    Using mathematics to validate network configurations

    AWS describes using its automated-reasoning tool Tiros to statically validate network segmentation in Cloud WAN by converting configurations from route tables, attachments, and policy documents into logical propositions and querying which attachments can reach which with which destination IPs. The system checks that no path exists between segments the policy declares off limits, succeeding if actual reachability matches expected segmentation and alerting an operator if they differ, with analyses possible as often as hourly. The post illustrates this with a production/development segmentation example across us-east-1 and us-west-2 and notes the approach extends to tools like IAM Access Analyzer and VPC Reachability Analyzer.

  20. Meta Engineering40

    Intelligent Delivery: Making Every Unit of Compute Serve Better Ads

    Meta describes Intelligent Delivery, an ML-driven system that allocates compute across its ads serving stack toward a system-wide optimum, contributing roughly 10% incremental ranking performance on top of model and system advancements from 2017 through the first half of 2026. It combines a shared Resource Core Model (6B sparse and 20M dense parameters) that predicts each request's outcome, compute consumption, and latency impact, with a hierarchy of allocation engines spanning region, hardware SKU, and host-level decisions. Agentic AI now helps propose and test optimizations, contributing to about 20% of Intelligent Delivery optimizations in the first half of 2026 and about 80% of engineering effort on the most automated mechanism, with engineers approving every change.

  21. AWS Machine Learning Blog42

    Accelerating product development with AI-driven lifecycle management (AI-PDLC) in AWS Quick

    This AWS blog post describes AI-PDLC, a framework of six AWS Quick agents (an Orchestrator plus Discovery, Prioritize, Working Backwards, Spec Generator, and Designer) that guide product teams through the Discover, Decide, Design, and Prototype phases, with human approval required at every gate. The agents pull context from enterprise connectors such as Slack, Jira, Salesforce, SharePoint, ServiceNow, and Outlook, store artifacts in shared Quick Spaces, and produce a prototype_spec.md that can be handed to coding agents like Kiro or Claude Code via the Agent Client Protocol (ACP). The post includes step-by-step setup instructions, Deep Discovery and Fast Track pacing modes, and three scenarios: new product development, feature additions, and bug fixes.

  22. Databricks Blog38

    Air gapped, not data dark: how Bright Machines gives customers live factory dashboards without opening the plant

    Bright Machines captures PostgreSQL changes at the factory edge with Debezium, writes them as Apache Iceberg, pushes them out over a single auditable outbound path, and reads them in place in Databricks via Unity Catalog federation, with zero inbound ports to the plant. A row committed on the line is queryable in Databricks in one to two minutes, dashboards refresh every ten minutes, and a heartbeat check detects a broken pipeline in under thirty minutes. The first customer-visible dashboard went live about two months after kickoff, replacing nine loosely coupled services with one governed platform plus one edge path.

Nov 11Wed
  1. Hugging Face Blog38

    Token Pooling-Aware Training for Multi-Vector Embedding Models

    This post explains token pooling for multi-vector embedding models, which merges similar document token vectors via Ward hierarchical clustering to cut storage, and introduces pooling-aware training in Sentence Transformers that applies the retrieval loss to both original and pooled representations. At pool_factor=4, the pooling-aware model retains 97.8% of its unpooled NanoBEIR nDCG@10 versus 94.6% for a normally trained model, with a 4x reduction in document vectors. It includes a reproducible training example, optional kernel acceleration, and evaluation results across pool factors, fixed caps, and calibrated adaptive thresholds.

  2. vLLM Blog58

    Encoder Disaggregation in vLLM: Scaling Multimodal Serving with EPD

    vLLM details its Encoder/Prefill/Decode (EPD) disaggregation work, including metadata-only requests that let downstream instances reuse remote embeddings without repeating media preprocessing, a dedicated encoder-only Model Runner V2 path, and remote embedding transfer via NIXL and Mooncake. It also covers cross-encoder output reuse through Mooncake Store and orchestration support in vLLM Router, SMG, and llm-d, with core capabilities in vLLM v0.32.0 and DeepSeek V4.1 Flash metadata-only support on main via #62802. A DeepSeek V4.1 Flash benchmark on GB200 found EPD delivered 6.6–9.5% higher per-GPU throughput and 44–47% lower P90 TTFT than PD at concurrency 64–128, but lower per-GPU throughput at concurrency 16.

  3. OpenRouter Announcements67

    MCP vs CLI vs Skills: What Each Costs in Context

    The article measures input-token costs for giving an agent GitHub access via the GitHub MCP server, the gh CLI, or a gh skill across Claude Haiku 5.5, Claude Sonnet 5.5, GPT-6 Luna, and Gemini 3.8 Flash; on Sonnet 5.5 the CLI used 1,421 tokens, the skill 3,315, and the MCP server 59,417 with its 46 default tools. It reports that the GitHub MCP server's 46 default tools add 19,436 tokens to every request on Sonnet 5.5, and that a single tool result (GitHub's release object) cost about 20,400 tokens. It also describes the beta Tool Search server tool, which cut the up-front cost of 46 tools to under 600 tokens and halved whole-task cost on Sonnet 5.5 while raising it on Haiku 5.5.

    Why it matters: It offers concrete, model-specific token and cost measurements for choosing between MCP, CLI, and skills, plus practical mitigations like scoping tools and trimming results.

  4. OpenRouter Announcements58

    LLM Quantization: What fp8 and fp4 Do to Quality and Price

    OpenRouter tested Qwen3.8 27B across six endpoints at fp16, fp8 and fp4 on 240 exact-answer tasks and found no detectable accuracy difference, with all endpoints scoring between 98.3% and 99.2%. Across 1,455 endpoints, fp8 was the most common labeled precision (23.6%) while 59.5% reported none, and lower precision was usually but not always cheaper. The post explains fp8 and fp4 formats and shows how to pin precision with provider.quantizations, noting that a narrow filter can leave a single endpoint exposed to rate limits.

  5. OpenRouter Announcements58

    LLM API Rate Limits: How to Fix OpenAI and Claude 429 Errors

    A guide to diagnosing 429 Too Many Requests errors from OpenAI, Anthropic and OpenRouter by reading rate-limit headers and limit_source, with tested fixes: retrying at the server-provided reset time, routing overflow to a fallback model, and letting another provider serve the same model. In the author's runs, default OpenAI SDK retries left 23 of 60 requests failed, while reset-aware retries completed all 60 in 89–92 seconds (Python) and 106–218 seconds (TypeScript), and a models fallback finished all 60 in 13 seconds for $0.000066. It also covers provider failover, which turned runs with up to 32 failures per 150 requests into 150 successes, plus habits for avoiding limits.

  6. AWS Machine Learning Blog40

    Build a daily dependency watcher with LangGraph and Amazon Bedrock AgentCore web search

    The Web Search API in Amazon Bedrock AgentCore is now available, returning current web results with title, URL, publication date and query-relevant extract, signed with existing AWS credentials and served from an Amazon-operated index. The post walks through building a LangGraph dependency watch that researches npm, PyPI and container base images in parallel, keeping the date window, domain blocklist and search budget in code and checking each finding against its cited sources. In the test run, three branches made 12 searches and finished in 36 seconds versus 99 seconds sequentially, and the watch can be hosted on AgentCore runtime or offered through AgentCore Gateway.

  7. ElevenLabs Blog12

    WAV vs MP3: How to choose the right audio file format

    WAV is an uncompressed, lossless format best for recording, editing and archiving, while MP3 is compressed and lossy, producing much smaller files suited to sharing and streaming. One minute of CD-quality stereo audio is about 10.6 MB as WAV, versus about 0.96 MB as a 128 kbps MP3 and about 2.4 MB as a 320 kbps MP3. Converting MP3 to WAV makes the file larger but cannot restore the data lost during compression.

  8. AWS Machine Learning Blog40

    The agent evaluation playbook: Built-in, DeepEval, and AutoEval on Amazon Bedrock AgentCore Evaluations

    This AWS post explains how to evaluate production agents on Amazon Bedrock AgentCore by starting from business outcomes, breaking them into observable behaviors, and mapping each to a trace- or session-level evaluator. It covers the built-in AWS evaluators, managed third-party metrics from DeepEval and AutoEval, custom and multi-dimension LLM-as-a-judge evaluators, and suggested evaluator stacks for customer support, research, and domain-content agents. It also describes three evaluation modes—on-demand, online, and batch—that share the same evaluator IDs, plus the ListEvaluators API for retrieving the full catalog.

  9. Pydantic AI Articles38

    Turn down OpenTelemetry noise without a redeploy

    Pydantic Logfire's Fleet Management lets you connect a service and its host Collector via OpAMP and change log level, attribute redaction and trace sampling while both keep running. The post walks through a FastAPI app with fleet=True, publishing SDK policies to drop debug logs, mask *token* attributes and sample 10% of traces, plus installing a host Collector and adding nginx logs. It also covers early-access rollouts, Kubernetes and existing Collectors, and troubleshooting.

  10. Pydantic AI Articles60

    Code mode is not a sandbox

    This post distinguishes code mode, where the model writes a short Python program calling your tools as functions via a single run_code tool, from a sandbox, which is an isolated environment where code runs. It describes three places a sandbox boundary can go (the whole agent, only the tools, or only the model-written code) and how Pydantic AI workspaces let you move that boundary without rewriting tools. It also covers Monty, a Rust-written Python interpreter that runs run_code next to the agent with no filesystem, network, or environment variables.

    Why it matters: Useful for teams deciding where to place isolation boundaries when combining code mode with sandboxed tool execution.

  11. n8n Blog22

    Batch processing patterns for workflow automation at scale

    This guide explains how batch processing in workflow orchestration differs from stream processing and presents three patterns—chunking, rate limiting, and per-item error handling with retry—for handling large loads without hitting memory limits or API quotas. It describes how n8n's Split Out, Aggregate, Loop Over Items, HTTP Request, Wait, and Remove Duplicates nodes support these patterns, and notes n8n was tested with up to 220 workflow executions per second on a single c5a.large instance running a two-node workflow. It also covers idempotency, checkpointing, and choosing batch sizes, plus sub-workflows and queue mode for scaling.

  12. n8n Blog15

    Microservices best practices every automation engineer should know

    This article outlines microservices best practices, including defining clear service boundaries via domain-driven design and the single responsibility principle, and building resilience into service-to-service calls with circuit breakers, retries with exponential backoff, and bulkheads. It also covers routing traffic through an API gateway and making failures observable with distributed tracing and centralized logging, noting how n8n workflows can orchestrate and monitor service calls.

  13. AWS Machine Learning Blog34

    Autonomous treasury agents: How Trovata built an AI-native solution on Amazon Bedrock and AgentCore

    Trovata, an AI-native cash and liquidity management platform connected to over 10,000 banks, describes how it built autonomous treasury agents on Amazon Bedrock and Amazon Bedrock AgentCore, using role-based access control and a verified API layer within a bank-branded deployment model. The post outlines use cases such as cash forecasting, natural language querying, anomaly detection, automated reconciliation and persistent agentic workflows, and cites customer results including $1M+ in annualized cost savings, 5+ hours per day saved per accounting team, a 77 percent reduction in annual TMS costs, and reporting reduced from hours or days to under a minute. It also notes a library of ten agent patterns from real customer deployments and Trovata's plans to expand agent patterns, ERP integrations and global bank connectivity.

  14. Claude Blog36

    How Curative is building a national provider network with agents on Claude

    Curative built a hierarchy of agents on Claude, led by a provider-contracts agent named Gwen, to sign provider contracts roughly 50 times faster than its team did in 2025, reaching 2,000 to 2,400 new contracts a week versus 2,100 in all of 2025. Gwen handles one provider per run on Claude Opus, with a Sonnet rate reviewer, a Haiku judge that checks every outbound message, and escalation to humans for non-standard pricing. The company's contracting team grew from 45 to about 55 people, each about 2.5 times as productive, and its agents run on bespoke claims, prior authorization and finance systems that 13 engineers rebuilt with Claude Code.

  15. AWS Machine Learning Blog40

    Amazon Bedrock tokenomics data lake

    AWS describes a centralized observability data lake that pulls Amazon Bedrock model invocation logs from linked accounts into a central account via CloudWatch subscription filters, processes them through Lambda and Amazon Data Firehose into Parquet in S3, and exposes them through Athena as a single queryable table. Per invocation, the flattened table captures the prompt, response, input and output token counts, model, stop reason, account, timestamp, and IAM role, and can be joined to CUR 2.0 data on the IAM ARN for per-invocation cost attribution. The solution is deployed with two CloudFormation templates from the AWS Sample Bedrock Invocation Data Lake GitHub repository, with an optional Amazon Quick Sight dashboard.

  16. AWS Machine Learning Blog36

    How TCS built an agentic test orchestrator with Amazon Bedrock AgentCore

    TCS built a multi-tenant Agentic Test Orchestrator on AWS that turns Jira stories, BRDs, Swagger specs, and GitLab sources into test scenarios, Gherkin files, and executable Java or Python tests, using specialized agents hosted on Amazon Bedrock AgentCore runtime with the Strands Agents SDK. The platform separates a shared control plane from per-tenant application planes with isolated Cognito user pools, KMS keys, S3 buckets, and DynamoDB tables, and routes API tests to Amazon ECS on Fargate and Selenium UI tests to an EC2-backed ECS cluster. TCS reports that in an internal benchmark of about 100 scenarios, average effort fell from roughly two hours to about 14 minutes per scenario, an 88 percent reduction.

  17. Wiz Research58

    How to Evaluate AI Agent Cybersecurity Capabilities

    Wiz Research outlines the principles behind the relaunch of its Cyber Model Arena, a benchmark of 26 models, four general-purpose harnesses, 59 agents and 327 challenges across code vulnerabilities, API, web and cloud security, arguing that benchmarks should fix their objective first, use real-world tasks, grade deterministically with code rather than LLM judges, isolate agents to prevent cheating, and weigh success against cost. The post reports that agents perform very differently by category (average pass@1 of 78.0% on API security versus 38.2% on code vulnerabilities) and that pairing harness with model matters, with a mean absolute gap of 5.1 pass@1 points across 22 models run on both Claude Code and ADK React. It also lists limitations including simple harnesses, string-equality grading on code tasks, public web tasks that may be contaminated, a 1,000-second time cap and unpublished confidence intervals.