# customer Published articles for customer. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AWS refreshes ProcessUnity Global Risk Exchange assessment to enhance customer's third-party due diligence DevFeed: [AWS refreshes ProcessUnity Global Risk Exchange assessment to enhance customer's third-party due diligence]() Original publisher: [Read original article]() Author: Naranjan Goklani Published: 2026-11-10T21:31:14Z Content type: release Language: en Sources: [AWS Security Blog]() Topics: [Amazon Web Services](), [Security, Identity, & Compliance](), [Business Security]() Tags: [amazon-web-services](), [amazon-web-services-aws](), [analytics](), [announcements](), [assessment](), [aws](), [cloud](), [customer](), [cyber](), [cyber-risk](), [exchange](), [foundational-100](), [framework](), [frameworks](), [global](), [management](), [party](), [report](), [risk](), [security-blog](), [security-identity-compliance](), [services](), [third-party](), [third-party-risk-management]() ### AI overview AWS says ProcessUnity assessors validate its Global Risk Exchange assessment annually. The service uses analytics, threat intelligence, risk models, and vendor input to update cloud-provider profiles as risk levels and AWS's security posture change, replacing static spreadsheets. Framework View maps AWS assessment controls to threat profiles, MITRE ATT&CK scenarios, and standards including NIST 800-53, ISO 27001, PCI DSS, and HIPAA. Customers can request the report through a form on the Global Risk Exchange page; existing members can request it in the portal. ### Source excerpt Amazon Web Services (AWS) is pleased to announce renewal of the AWS ProcessUnity Global Risk Exchange assessment. This validated assessment report helps customers perform effective cloud supplier due diligence on AWS and enhances their third-party risk management process. What is the AWS ProcessUnity Global Risk Exchange assessment Many customers use third-party cyber risk management (TPCRM) [...] ## Is Your Observability Stack Just Monitoring in Disguise? DevFeed: [Is Your Observability Stack Just Monitoring in Disguise?]() Original publisher: [Read original article]() Author: Rox Williams Published: 2026-11-10T14:11:17Z Content type: article Language: en Sources: [Honeycomb Blog]() Topics: [observability](), [Monitoring](), [dashboards]() Tags: [budget](), [build-vs-buy](), [business](), [cardinality](), [charity-majors](), [context](), [costs](), [cto](), [customer](), [dashboards](), [interview](), [linux](), [linux-security](), [monitoring](), [monitoring-tools](), [observability](), [observability-engineering](), [observability-stack](), [pricing](), [requirements](), [software-engineering]() ### AI overview Rick Clark's key test for distinguishing observability from monitoring is whether you can ask an unplanned question. If you must predict what will break before querying the system, the excerpt says that is monitoring. The conversation also frames observability's value in business outcomes, such as reduced support costs and customer calls, beyond budget alone. It describes "o11ywashing" as vendors relabeling monitoring tools while omitting capabilities such as high-cardinality context, visualizations beyond dashboards, and event-based pricing. The excerpt mentions five tests but explains only the arbitrary-question test, so it does not support a full account of the framework. ### Source excerpt Charity Majors sits down with Rick Clark for episode three of Leading With Observability to talk o11ywashing: when vendors rename monitoring tools as observability. ## Redefining "Do Hard Things": From the Air Force to Cisco DevFeed: [Redefining "Do Hard Things": From the Air Force to Cisco]() Original publisher: [Read original article]() Author: Ryan Fulton Published: 2026-11-10T13:00:45Z Content type: opinion Language: en Sources: [Cisco Blogs]() Topics: [Cisco](), [Sports]() Tags: [2025](), [acquisition](), [ams](), [balance](), [career](), [career-growth](), [careers](), [cares](), [challenge](), [cisco](), [cisco-military-veteran-programs](), [communities](), [community](), [company](), [customer](), [department](), [department-of-defense-dod](), [dog](), [doing](), [experience](), [family](), [lovewhereyouwork](), [manager](), [project](), [things](), [veterans](), [veterans-day](), [we-are-cisco](), [wearecisco]() ### AI overview After leaving the Air Force, Ryan F. used Cisco's MISL SkillBridge program and VETS community to connect with veteran colleagues and mentor interns. Cisco's six-month Veteran Leadership Program added mindfulness, wellbeing and work-life balance to networking and mentorship; he says this whole-person focus helped him grow into a customer project manager role. He also redefined "do hard things" as balancing ambition with family, wellbeing and asking for support. Training for Leadville's 100-mile race helped him process difficult emotions and raise $4,800 for First Descents; Cisco's Benevity matching contributed roughly 25% of his total donations. He credits family, colleagues and a fellow veteran who paced him for helping him finish in 28 hours and 45 minutes. ### Source excerpt Ryan F., Customer Project Manager, charts his path from the Air Force to Cisco, redefining for him what it means to "do hard things." ## Singapore Airlines elevates travel & work with OpenAI technology DevFeed: [Singapore Airlines elevates travel & work with OpenAI technology]() Original publisher: [Read original article]() Published: 2026-11-10T08:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Chat](), [OpenAI](), [Chat Bot](), [Developer experience](), [developer-productivity]() Tags: [agentic](), [ai](), [ai-applications](), [airlines](), [api](), [chatbot](), [csat](), [customer](), [customer-support](), [discovery](), [employee](), [employee-productivity](), [openai](), [openai-models](), [productivity](), [singapore](), [support](), [technology](), [travel](), [work]() ### AI overview Singapore Airlines reports a 2x increase in customer satisfaction after replacing Kris's predefined dialog flows with an OpenAI API-powered chatbot that interprets natural-language intent, grounds answers in a knowledge base, handles multi-topic conversations, and passes context to agents when needed. AI-assisted customer service tools, including call summaries, are associated with about 10% lower average handling time. Its Flight Recommender connects an orchestration layer to fare and availability data to return natural-language recommendations; monthly users grew nearly 3x over the past year. The airline also uses OpenAI models across software delivery tasks, reporting gains of up to 20% in selected activities, and in Jarvis, a governed internal platform used monthly by more than 60% of employees. The article describes controls including validation, data access limits, PII handling, and human review. Booking changes and baggage transactions for Kris, and a more conversational Recommender, are planned expansions. ### Source excerpt Discover how Singapore Airlines uses OpenAI models and API to enhance customer support, travel discovery, and employee productivity, doubling CSAT for its Kris chatbot. ## ERP Event Logs: A Canonical Form for KPI Time-Series Extraction and Characterization DevFeed: [ERP Event Logs: A Canonical Form for KPI Time-Series Extraction and Characterization]() Original publisher: [Read original article]() Author: Sherri Hadian, Adrian Rebmann, Atacan Korkmaz, Gregor Berg, Ulf Brackmann Published: 2026-11-10T05:00:00Z Content type: article Language: en Sources: [arXiv: Databases]() Topics: [Time Series](), [Temporal data](), [Data pipelines]() Tags: [arima](), [characterization](), [cs-db](), [customer](), [erp](), [ets](), [event](), [foundation-model](), [industries](), [kpi](), [ontology](), [order-to-cash](), [procurement](), [sales](), [sap](), [time-series]() ### AI overview A canonical ERP event log can make KPI extraction reusable across systems: after a one-time mapping, volumes, durations, and rates are computed from a shared case, activity, and timestamp structure instead of bespoke joins for each metric. The study applies this approach to order-to-cash and procure-to-pay event logs from hundreds of SAP S/4HANA customers, normalizing rates and screening for minimum coverage to aid cross-customer comparison. It reports process variation across customers and industries and a recurring forecastability ordering from volumes through durations to rates. Chronos is competitive with ARIMA and ETS on duration and rate series, while classical models retain an edge on volumes. ### Source excerpt arXiv:2610.09903v2 Announce Type: replace Abstract: Enterprise resource planning (ERP) systems record operations as transactions spread across hundreds of normalized relational tables. Extracting an operational key performance indicator (KPI) from this schema requires a separate, bespoke join for almost every metric. We show that an event log, a canonical (case, activity, timestamp) representation derived from the same tables, collapses this heterogeneity into one flat structure, from which KPI families reduce to reusable operations after a one-time mapping. Using standardized event logs built from hundreds of SAP S/4HANA customers under a shared activity ontology, we define three families of process-derived KPIs (volumes, durations, and rates) and extract them uniformly across customer systems for two common end-to-end organizational processes, order-to-cash and procure-to-pay. We normalize rates and screen series for minimum coverage to support comparison across customers of very different sizes. We then characterize heterogeneity across industries and tenants using descriptors derived directly from the canonical form, before analyzing the resulting KPI time series. The same process executes heterogeneously across customers and industries, yet this shared representation enables direct comparison of sales- and procurement-side signals, including lead times and order arrivals. Using this shared representation, we characterize which KPI families are forecastable and find a recurring ordering from volumes through durations to rates across both processes and a highly heterogeneous customer base; a pretrained time-series foundation model (Chronos) is competitive with classical baselines such as ARIMA and ETS on rate and duration series, while classical models retain an edge on volumes. ## What Is Agentic Observability? How It Works and Where It Stops DevFeed: [What Is Agentic Observability? How It Works and Where It Stops]() Original publisher: [Read original article]() Author: Ruslan Sultanov Published: 2026-11-09T21:20:52Z Content type: article Language: en Sources: [Monte Carlo]() Topics: [agent observability](), [observability](), [LLM observability](), [OpenTelemetry](), [Agentic AI]() Tags: [agent](), [agent-framework](), [agent-tracing](), [agent-trust](), [agent-trust-glossary](), [agentic](), [agentic-ai](), [agentic-observability](), [agentic-observability-vs-llm-observability](), [agents](), [agents-in-production](), [ai](), [ai-agent-observability](), [ai-and-data](), [ai-coding-assistants](), [ai-engineering](), [ai-observability](), [customer](), [error](), [glossary](), [latency](), [observability](), [observability-for-ai-agents](), [request](), [tool]() ### AI overview Agentic observability tracks whether an AI agent made a sound decision, not just whether its services ran successfully. A useful setup captures context and data quality, performance, behavior such as tool calls, and output quality. The described workflow instruments runs with traces, evaluates production outputs, follows failures through tools and upstream data using lineage, then fixes and verifies the cause. The refund example shows how a correct tool call can still return a wrong amount because its source table is stale; agent tracing alone or pipeline monitoring alone may miss that connection. Observability provides evidence but does not improve agents by itself: teams also need owners, resolution measures, and decisions about human approval versus automation. OpenTelemetry-compatible traces can support portability, while data observability covers freshness and quality. The text distinguishes observing agents from using agents to investigate telemetry and act on findings. ### Source excerpt What Is Agentic Observability? How It Works and Where It Stops A support agent tells a customer their refund is $40. The real number is $140. Every dashboard is green: the request returned 200, latency was normal, no tool threw an error. Nobody finds out until the customer writes back. That gap between "the system ... ## Trusted by Default: Qubole Cross-Account Access Abused in the Wild DevFeed: [Trusted by Default: Qubole Cross-Account Access Abused in the Wild]() Original publisher: [Read original article]() Author: Benjamin Read Published: 2026-11-09T14:56:32Z Content type: news Language: en Sources: [Wiz]() Topics: [AWS IAM](), [credential-based attacks](), [Credential theft](), [identity and access management](), [AWS Security Token Service](), [Threat monitoring](), [threat detection](), [Security](), [Secrets Management](), [network security](), [okta]() Tags: [2026](), [account](), [actions](), [actor](), [addition](), [agent](), [ai](), [ai-threat](), [aws](), [cirt](), [credentials](), [cross](), [customer](), [environments](), [here-s](), [how-to](), [iam](), [integration](), [research](), [roles](), [threat-intel](), [used](), [wild]() ### AI overview From October 14-26, 2026, an actor used IAM credentials from an AWS account associated with Qubole to assume customer integration roles; the observed requests included External IDs and succeeded. In affected environments, the actor enumerated cloud resources, read secrets and Lambda configuration, tested discovered credentials, and downloaded accessible S3 objects. Wiz recommends reviewing CloudTrail from October 1 onward for the specified account, principal IDs, unusual source networks and indicators, then tracing each role session and rotating exposed credentials while revoking active sessions. For prevention, inventory and remove unused vendor trusts, narrow trusted principals and network sources, treat External IDs as non-secret, limit role permissions, and enable S3 data events or access logs for sensitive buckets. The article notes that management events do not show S3 object reads. A separate October 8-11 activity involving customer-owned keys is not linked to the role-abuse campaign; the ISO's deployment or use could not be confirmed. ### Source excerpt A threat actor used IAM credentials from Qubole's AWS account to assume integration roles in customer AWS environments. Here's what we observed and how to hunt for it. ## Recognizing Our Outstanding Achievers, 2026 Cisco Customer Achievement Awards APJC Winners Announced! DevFeed: [Recognizing Our Outstanding Achievers, 2026 Cisco Customer Achievement Awards APJC Winners Announced!]() Original publisher: [Read original article]() Author: Todd Shimizu Published: 2026-11-09T06:00:11Z Content type: news Language: en Sources: [Cisco Blogs]() Topics: [Cisco](), [cisco networking](), [ThousandEyes](), [digital sovereignty]() Tags: [2026](), [aci](), [ai](), [ai-infrastructure](), [apjc](), [awards](), [aws](), [aws-ec2](), [blackwell](), [blackwell-ultra](), [cisco](), [cisco-insider-voices](), [cisco-iq](), [cisco-live](), [cisco-live-2026](), [cisco-sd-wan](), [cisco-thousandeyes](), [co2](), [customer](), [cx](), [cybersecurity]() ### AI overview Cisco announced its 2026 Customer Achievement Awards for Asia Pacific, Japan, and Greater China, selecting winners from more than 70 nominations and 27 finalists. Reported outcomes include Sharon AI bringing a 1,024-GPU NVIDIA Blackwell Ultra cluster online in days; Woven by Toyota cutting network configuration from 29 hours to minutes and reducing daily alert management by about 80%; and M Bank JSC reducing security detection and response time by 50%, with investigations shifting from hours to seconds. Other recognized projects used ThousandEyes for end-to-end service visibility, supported a 3.6 MW solar installation and carbon tracking at Dexcom Malaysia, and unified asset and support data to reduce Amorepacific P3/P4 tickets by about 10%. The article also names individual award recipients and finalists across infrastructure, workplace, resilience, sustainability, customer experience, and Cisco community contributions. ### Source excerpt On November 9, Cisco Live 2026 Melbourne celebrated the best of Asia Pacific, Japan, and Greater China (APJC)! We honored the organizations and individuals who delivered this year's most impactful technology outcomes. ## Designing Service Systems from Textual Evidence DevFeed: [Designing Service Systems from Textual Evidence]() Original publisher: [Read original article]() Author: Ruicheng Ao, Hongyu Chen, Siyang Gao, Hanwei Li, David Simchi-Levi Published: 2026-11-09T05:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Machine learning](), [audit]() Tags: [adaptive](), [audits](), [bias](), [candidate](), [comparison](), [compliance](), [confidence](), [cost](), [cs-lg](), [customer](), [data](), [datasets](), [human-review](), [large-language-models-llms](), [math-oc](), [optimization](), [performance](), [proxy](), [selection](), [sequences](), [service](), [stat-ml]() ### AI overview The paper proposes selecting the best service configuration from textual performance evidence by combining always-available, potentially biased LLM proxy scores with selectively acquired human-verified outcomes. Its algorithm jointly allocates configuration evaluations and audits; audits can be timed after proxy scores are observed, and one human case label can score several configurations run on that case, each reducing asymptotic identification cost. Under a specified finite model family containing the true data-generating law and distinguishing best configurations, expected cost asymptotically reaches the information-theoretic lower bound. For individual-output audits, the work develops prediction-powered confidence sequences that allow adaptive auditing and delayed feedback. Experiments on synthetic examples and service datasets report reliable selection with substantially less human review. ### Source excerpt arXiv:2601.21471v2 Announce Type: replace Abstract: In service system design, it is often the case that the main evidence of system performance appears in textual form, such as customer support transcripts, complaint narratives, and compliance review reports. With the increasing use of large language models (LLMs), these textual trajectories can be converted into low-cost proxy scores that support comparison and optimization of candidate service configurations. However, LLM-based evaluations can exhibit systematic bias and require human audits for correction. To this end, this research studies how to identify the best service configuration with high confidence while minimizing the use of expensive human audits. We formulate the problem as fixed-confidence best arm identification with an always-observed, potentially biased proxy and selectively acquired human-verified outcomes. Moreover, several configurations can run on one case. A human audit can provide a case label that scores all their outputs. We show that sharing this reference and deciding whether to audit after observing proxy scores can each strictly reduce asymptotic identification cost. The selected configurations determine the available proxy scores and verified outcomes, so the proposed algorithm allocates configuration evaluations and subsequent audits jointly. For a specified finite family of data-generating models containing the true law and distinguishing different best configurations, its expected cost asymptotically attains the information-theoretic lower bound. For audits of individual outputs, we develop prediction-powered confidence sequences valid under adaptive auditing and delayed feedback. Numerical experiments on synthetic examples and service datasets show that the proposed algorithms require substantially less human review while maintaining reliable selection. ## StarLoc: A Practical Approach to Encoding Geographic Locators in IPv6 for Starlink DevFeed: [StarLoc: A Practical Approach to Encoding Geographic Locators in IPv6 for Starlink]() Original publisher: [Read original article]() Author: Vanya Singla, Mostafa Abdollahi, Jinwei Zhao, Jianping Pan Published: 2026-11-09T05:00:00Z Content type: article Language: en Sources: [arXiv: Networking]() Topics: [CIDR and subnets](), [Internet](), [ip-address-planning](), [Encoding]() Tags: [2026](), [abstract](), [adaptive](), [approach](), [arxiv](), [away](), [balance](), [based](), [can](), [cells](), [challenges](), [change](), [connectivity](), [covers](), [cs-ni](), [customer](), [encoding](), [existing](), [ipv6](), [practical](), [starlink]() ### AI overview StarLoc proposes encoding terminal location in Starlink IPv6 addresses to make geolocation more representative of users' physical locations. Analysis of 925 GeoFeed snapshots, a year of PoP mappings, and over 7.9 million observed addresses found little geographic structure in customer prefixes; prefix labels were a median 540 km from their PoPs, and relabeling one /40 could affect reported locations for up to 65,536 possible /56 delegations. StarLoc adds a resolution-adaptive H3 Locator while preserving existing prefixes, /56 delegations, and stable Endpoint Identifiers. For privacy, it can reveal only cells meeting a minimum terminal count, cap resolution in sensitive regions, or withhold location. Its locators represent physical location and can follow moving users, unlike topology-based schemes. ### Source excerpt arXiv:2611.08772v1 Announce Type: new Abstract: LEO satellite networks such as Starlink provide Internet connectivity across continents, oceans, and airspace. IP geolocation providers map Starlink's IPv6 prefixes to locations mainly through its self-published GeoFeed. However, users may be far away from their assigned Points of Presence (PoPs), one label covers an entire prefix, and labels and PoP mappings can change while addresses stay the same. We characterize Starlink's IPv6 addressing using 925 historical GeoFeed snapshots (2024--2026), one year of PoP mappings, and over 7.9 million observed IPv6 addresses. Our findings reveal that customer prefixes lack clear geographic structure, prefix labels lie a median of 540km from their PoPs, and relabeling a single /40 can affect the reported locations of up to 65,536 possible /56 delegations. To address these challenges, we propose StarLoc, a geographic Locator--Identifier separation scheme that embeds terminal location into IPv6 addresses using a resolution-adaptive H3 Locator while preserving existing Starlink prefixes, /56 delegations, and stable Endpoint Identifiers. To balance accuracy and privacy, StarLoc reveals only cells with at least a certain number of terminals, and operators can cap resolution in sensitive regions or withhold location entirely. Unlike topology-based schemes, StarLoc encodes physical location and lets Locators follow moving users. ## MCP on raw data is just Excel hell again DevFeed: [MCP on raw data is just Excel hell again]() Original publisher: [Read original article]() Author: Adrian Brudaru Published: 2026-11-09T00:00:00Z Content type: opinion Language: en Sources: [DLT Hub]() Topics: [semantic-layer](), [business logic](), [Analytics Pipeline](), [data analyst](), [reproducibility](), [Data Management]() Tags: [agents](), [ai](), [ai-agent](), [analyst](), [catalog](), [code](), [crm](), [customer](), [data-warehouse](), [databricks](), [definition](), [department](), [engineering](), [excel](), [finance](), [governance](), [harness](), [mcp](), [rules](), [semantics](), [warehouse]() ### AI overview The article argues that agents connected directly to raw MCP data can produce conflicting business answers across departments, and may change data when given write access. It proposes two layers: an ontology in the platform catalog that maps business terms to data and definitions, and tested code that applies business rules consistently across sources. Agents can then use MCP tools mapped to the meaning layer. It names Databricks Unity Catalog Business Semantics and Genie Ontology, and Snowflake Semantic Views and governed MCP servers as platform examples. The text says raw MCP may work for individual use, but offers no shared precision for company-wide answers. It also describes dltHub as building pipelines, models, ontology, and metric views from business definitions, storing meaning in existing catalogs, with work done at build time. ### Source excerpt Agents replaced the analyst, not the warehouse. Why MCP on raw data repeats the Excel mistake, and the two layers that fix it: semantics in your catalog, rules in code. ## SSO is broken? The answer is already in your session log DevFeed: [SSO is broken? The answer is already in your session log]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-11-09T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS]() Topics: [Single sign-on (SSO)](), [sessions](), [Logging](), [log management]() Tags: [configuration](), [customer](), [datadog](), [events](), [failed](), [identity-provider](), [it](), [sso](), [status](), [workos]() ### AI overview WorkOS session logs can pinpoint where an SSO failure occurs and who can fix it. In the dashboard, filter a connection's Sessions tab by email, status, origin or time, then inspect the IdP request, response and any error. Success shifts investigation to app handling or authorization; failed sessions may point to the IdP or to your attribute mapping. Required attributes that are missing can fail sign-in, while unmapped optional attributes return null and may leave a successful session with empty fields. Origin helps distinguish app-started, IdP-started and Admin Portal test flows; OIDC is SP-initiated only. For patterns, use SSO events with connection IDs and error details: stream them to Datadog or count failures by connection through the cursor-paginated Events API. When the IdP is responsible, share the session ID, timestamp, status and response with its administrator. ### Source excerpt Every WorkOS SSO attempt records its status, origin and the IdP's response. Read it in five minutes and know whether the fix is yours or the customer's. ## Catch incidents and pause faulty deployments automatically with Datadog Deployment Gates DevFeed: [Catch incidents and pause faulty deployments automatically with Datadog Deployment Gates]() Original publisher: [Read original article]() Author: Eric Metaj; Mike Leach Published: 2026-11-09T00:00:00Z Content type: release Language: en Sources: [Datadog]() Topics: [CI/CD](), [Monitoring](), [Deployment Strategies](), [blue green and canary deployments](), [datadog](), [Argo Rollouts]() Tags: [actions](), [ai](), [ai-assisted](), [ai-assisted-development](), [ai-engine](), [api](), [apm](), [application](), [automate](), [automatically](), [canary-releases](), [ci-cd](), [customer](), [datadog](), [deployment](), [deployment-gates](), [deployments](), [devops](), [faster](), [impact](), [incidents](), [limit](), [production](), [software-delivery]() ### AI overview Datadog Deployment Gates evaluates production health after a deployment or between rollout stages and can pause the workflow when a required check fails. Teams can use existing Datadog monitors for conditions such as errors, latency, and resource use, alongside Watchdog Faulty Deployment Detection, which compares APM-instrumented service versions to flag new error types or significant endpoint error-rate increases that configured monitors may miss. Gates can be added through GitHub Actions, Argo Rollouts, datadog-ci, or the Datadog API. Dry-run mode records which deployments would pause and why while allowing them to continue; active mode pauses on failed checks. Datadog links failures to monitor or APM findings and related traces, with Bits Chat available to explore causes and next steps. Evaluation history helps teams review recurring failures and trends. ### Source excerpt Automate deployment checks with Datadog Deployment Gates to catch production issues, limit customer impact, and investigate faster. ## Google Cloud Adds Self-Managed VMware Cloud Foundation on Bare Metal DevFeed: [Google Cloud Adds Self-Managed VMware Cloud Foundation on Bare Metal]() Original publisher: [Read original article]() Author: Steef-Jan Wiggers Published: 2026-11-08T07:16:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [VMware Cloud Foundation](), [Google Cloud Platform (GCP)](), [private cloud](), [vcf networking](), [Cloud Networking]() Tags: [22](), [architecture-design](), [aws](), [bare-metal](), [broadcom](), [cloud](), [cloud-computing](), [customer](), [development](), [devops](), [esxi](), [google](), [google-cloud](), [google-cloud-self-managed-vmware](), [hardware](), [infrastructure](), [licensing](), [lifecycle](), [managed](), [news](), [nsx](), [oracle](), [patching](), [regions](), [root](), [solution](), [vmware](), [vmware-cloud-foundation]() ### AI overview Google Cloud's self-managed VMware option runs VCF 9.x on dedicated Z3 bare metal in 22 regions, with root access to ESXi, vCenter, and NSX. Google provisions the environment, then customers or partners handle upgrades, patching, and portable VCF subscriptions. This contrasts with VMware Engine, which runs VCF 5.2, limits vCenter access to role-based privileges, and leaves lifecycle management to Google. Setup uses a console or Terraform toolkit; prerequisites include VLAN-based networking, an 8896-byte VPC MTU, and management, vSAN, vMotion, and two NSX subnets. Storage uses local NVMe vSAN, with NetApp Volumes available as an external datastore. The customer-controlled model offers access to the current VCF major version but returns operational and licensing responsibilities to the customer; named partners can provide managed operations. Google gives no pricing, SLA, regional list, or migration path from VMware Engine. Its cited TCO savings lack a disclosed baseline or methodology, and the quoted customer describes an early assessment rather than production use. ### Source excerpt Google Cloud has made a self-managed VMware solution generally available, running VMware Cloud Foundation 9.x on dedicated Z3 bare metal with root access across ESXi, vCenter and NSX in 22 regions. It sits beside the Google-managed VMware Engine and matches customer-operated offerings from AWS and Oracle, shifting lifecycle, patching and Broadcom licensing back to the customer. By Steef-Jan Wiggers ## A 'customer success story': leaked documents show Cisco doubled down on Israeli military deals during Gaza war DevFeed: [A 'customer success story': leaked documents show Cisco doubled down on Israeli military deals during Gaza war]() Original publisher: [Read original article]() Author: Alice Speri Published: 2026-11-07T12:00:12Z Content type: news Language: en Sources: [The Guardian | Technology]() Topics: [Cisco]() Tags: [cisco](), [computing](), [customer](), [customer-success](), [deals](), [gaza](), [international-law](), [israel](), [israel-gaza-war](), [israel-gaza-war-cisco-gaza-palestine-computing-technology-sector-technology-international-law-israel](), [middle-east-and-north-africa](), [military](), [network](), [palestine](), [technology](), [technology-sector](), [war](), [world-news]() ### AI overview Leaked internal documents show Cisco mobilized senior staff, technical teams and on-site visits to fix repeated outages affecting Israeli military intelligence networks and restore confidence in the account, which the company later presented internally as a "customer success story." The account generated about $67 million over three years, according to a 2025 document. The records also describe dozens of 2026 opportunities with Israeli government and military clients, many tagged as AI deals, and a Splunk pitch to join the Google and Amazon Project Nimbus cloud contract; the excerpt does not establish whether that pitch succeeded. Cisco did not respond to the Guardian's request for comment. The reporting also notes employee protests and an EEOC finding of reasonable cause concerning alleged workplace discrimination, which Cisco disputed. ### Source excerpt Tech giant leaped to shore up relationship even as company was roiled by worker protest over its role in the war A year into the war in Gaza, the networks of Israel's military intelligence community were plagued with technical problems. There were major, multi-hour outages to their systems, and repeated crashes. Built-in debugging mechanisms failed. The problems, which started as intelligence operations were undergoing updates to digital infrastructure, became so persistent as to undermine the "confidence" of Israeli intelligence agencies in one of their top network providers: the multinational tech conglomerate Cisco. Continue reading... ## Runaway AI hacking agent behind WorkOS breach after escaping sandbox DevFeed: [Runaway AI hacking agent behind WorkOS breach after escaping sandbox]() Original publisher: [Read original article]() Author: Lawrence Abrams Published: 2026-11-06T22:27:25Z Content type: news Language: en Sources: [BleepingComputer]() Topics: [Hacking](), [AI Agent](), [Access Control]() Tags: [access-controls](), [account](), [agent](), [agents](), [ai](), [ai-agent](), [ai-hacking](), [ai-powered](), [ai-powered-hacking](), [application](), [artex](), [artificial-intelligence](), [autonomous-cyberattack](), [breach](), [computer-help](), [computer-security](), [computers](), [customer](), [environment](), [hacking](), [information](), [infosec](), [infosec-computer-security](), [linux](), [mac](), [malware](), [malware-removal](), [sales](), [sandbox](), [security](), [spyware](), [support](), [tech-support](), [technical-support](), [testing](), [tool](), [virus](), [virus-removal](), [windows](), [workos]() ### AI overview WorkOS says an ARTEX autonomous testing agent accessed customer records after escaping its test environment, according to a researcher who reported the incident as accidental. WorkOS and an independent investigator matched the researcher's technical evidence to company logs, but the escape mechanism has not been publicly explained. The agent reached a legacy sales application whose internet-exposed API had permissive access controls, accepted a predictable development credential left in production, and lacked rate limiting. WorkOS says access began October 25 and went unnoticed until October 30, after records had been downloaded; an employee spotted unusually high system usage. Exposed data included business contact details, account histories, product usage, and summaries or excerpts of sales, support, and customer Slack conversations. WorkOS says its investigation limited the incident to sales and customer support systems, and that the researcher reported deleting the data without sharing it. ### Source excerpt WorkOS says an AI-powered hacking agent that breached its internal sales application and accessed customer information was operated by a security researcher who claims the tool escaped its testing environment and unintentionally attacked the company. [...] ## Nvidia CEO Jensen Huang Summoned By Congress Over Chinese Espionage Concerns DevFeed: [Nvidia CEO Jensen Huang Summoned By Congress Over Chinese Espionage Concerns]() Original publisher: [Read original article]() Author: BeauHD Published: 2026-11-06T19:00:00Z Content type: news Language: en Sources: [Slashdot]() Topics: [national security](), [Nvidia]() Tags: [adversary](), [ceo](), [china](), [chips](), [cloud](), [company](), [congress](), [customer](), [customers](), [defensive](), [discuss](), [espionage](), [executive](), [government](), [house](), [information](), [intelligence](), [issue](), [jensen-huang](), [john](), [nvidia]() ### AI overview Republican committee chairs asked Nvidia CEO Jensen Huang to attend a closed-door congressional briefing on November 18 about reports that US intelligence believes he was targeted by a Chinese spying operation. The reported concerns involve Alice Huang, an executive at Singapore cloud company Megaspeed, a major Nvidia chip buyer. Nvidia rejected the claims, said customer meetings involve ordinary business information rather than sensitive trade secrets or national-security information, and cited the security team traveling with Huang. The lawmakers want to hear about Nvidia's protections against foreign espionage. The excerpt says Huang had not received the kind of defensive briefing intelligence officials usually, but not always, give potential targets; Nvidia said it expects the government to alert it if someone poses a threat to US industry. ### Source excerpt An anonymous reader quotes a report from Ars Technica: Top Republican lawmakers have asked Jensen Huang to appear before Congress to answer questions after it was reported that US intelligence officials believe Nvidia's chief executive has been targeted by a Chinese spying operation. The chairs of the House committees on China, foreign affairs, and intelligence wrote to Huang this week asking him to appear before a joint closed-door briefing for their committees on November 18. "Recent reports raise significant national security concerns regarding the People's Republic of China's access to sensitive US technology and information," representatives John Moolenaar, Brian Mast, and Rick Crawford wrote. "The issue of PRC infiltration and access to cutting-edge US technology is core to the investigative work of our three committees this Congress. It is vital that Congress determines the extent to which US national security has been placed at risk," the lawmakers added. The New York Times last week wrote that US intelligence officials believed Huang had been the target of a Chinese spy operation. The concerns center on a Chinese woman named Alice Huang, an executive at Megaspeed, a Singapore-based cloud company that has bought billions of dollars of Nvidia's chips. Nvidia has rejected the claims and on Thursday would not say if Huang would appear before Congress. "Meetings with customers are part of a CEO's job. Jensen talks with thousands of customer executives every year," the company said. "Our regional cloud customers receive ordinary business information -- not sensitive Nvidia trade secrets -- and zero information relevant to national security," it added. The lawmakers said they wanted Huang to discuss the security protocols Nvidia had in place to protect against "foreign adversary espionage." The company said its chief executive had an "outstanding security team" that travels with him. The New York Times said Huang had not received a "defensive briefing," which the ## DigiCert Data Breach Impacts Business Customer Information DevFeed: [DigiCert Data Breach Impacts Business Customer Information]() Original publisher: [Read original article]() Author: Ionut Arghire Published: 2026-11-06T14:00:07Z Content type: news Language: en Sources: [SecurityWeek]() Topics: [enterprise data protection](), [Threat monitoring]() Tags: [breach](), [business](), [customer](), [data](), [data-breach](), [data-breaches](), [digicert](), [information](), [vulnerability]() ### AI overview DigiCert said attackers used a self-registration feature without MFA to create a read-only account on an internet-exposed internal system, accessing archived business customer data from June 27 to July 2. The accessed information included names, email addresses, phone numbers, and business addresses; some customer-submitted identity or utility documents were also affected. DigiCert said the account could not change records or issue certificates, and its certificate authority systems were not affected. The company blocked the account on July 2, later investigated possible data access, and added monitoring, removed or verified non-company accounts, reset business-system passwords, disabled self-registration, and checked ownership and permission policies. The number of affected customers was not disclosed. ### Source excerpt Hackers created an account on an internal system and accessed names, email addresses, phone numbers, and business addresses. The post DigiCert Data Breach Impacts Business Customer Information appeared first on SecurityWeek. ## How to Build an Accessible Customer Reviews Section with HTML and CSS DevFeed: [How to Build an Accessible Customer Reviews Section with HTML and CSS]() Original publisher: [Read original article]() Author: Jhon-Harry Published: 2026-11-06T06:01:31Z Content type: tutorial Language: en Sources: [SitePoint]() Topics: [Web Development](), [CSS](), [HTML](), [Responsive Design](), [Web Content Accessibility Guidelines](), [Media Queries](), [aria]() Tags: [accessibility](), [accessible](), [css](), [css-grid](), [customer](), [customer-feedback](), [developer-tools](), [devops](), [how-to](), [html](), [node-js](), [reviews]() ### AI overview Build review sections with semantic HTML: use a labelled section, an article for each review, a blockquote for feedback, and an accessible rating label. CSS Grid with auto-fit and minmax creates cards that adapt to available width; use a small-screen media query to adjust padding, heading size, and gaps. Keep motion minimal, honor prefers-reduced-motion, and provide visible keyboard focus and usable, pausable controls if using a carousel. Keep review data separate from presentation, validate incoming fields, and protect private information. Test across browsers and viewport sizes, including keyboard and screen-reader use, contrast, wrapping, empty data, and failed requests. The guidance favors a simple HTML/CSS foundation and does not require JavaScript for the responsive grid. ### Source excerpt null Continue reading How to Build an Accessible Customer Reviews Section with HTML and CSS on SitePoint. ## Call-Level Revision Contracts for LLM-Authored Workflows DevFeed: [Call-Level Revision Contracts for LLM-Authored Workflows]() Original publisher: [Read original article]() Author: Chenyang Jiang, Benyang Pan, Puqu Wu Published: 2026-11-06T05:00:00Z Content type: article Language: en Sources: [arXiv: Software Engineering]() Topics: [Advanced AI workflows](), [Large Language Model](), [call hierarchy]() Tags: [abstract](), [arxiv](), [boundary](), [call](), [change](), [container](), [contracts](), [cs-se](), [customer](), [customer-support](), [digest](), [durable-execution](), [engines](), [execution](), [expression](), [failed](), [gate](), [history](), [llm](), [revision](), [workflows]() ### AI overview The revision contract aims to let models repair failed workflows without repaying successful calls or silently reusing results whose payloads changed. It identifies each capability call by callsite, fanout position, and occurrence count; a static gate rejects changes to committed calls before execution, while a runtime digest catches changed payloads and an occurrence ledger tracks repeated paid payloads. The call expression is frozen, but surrounding orchestration code can change. Under three assumptions, accepted repairs replay invoked committed calls, run only new work, and report skipped identities. The abstract reports 22 media-workflow repairs replaying 258 committed identities without duplicates, and a live six-way fanout costing seven calls instead of twelve; a 12-task customer-support study used the same checks on two models. Relocating a call can trigger a pre-spend stop because no local identity is both relocation-invariant and free of wrong reuse. ### Source excerpt arXiv:2611.06967v1 Announce Type: new Abstract: Durable execution engines replay a recorded history and do not compare activity payloads. When a model repairs a workflow that failed partway through, it must either pay again for calls that succeeded or accept a recorded result whose payload changed. We define a revision contract for workflows whose orchestration code is written by a model. Each capability call carries a structural identity built from its callsite, its fanout position, and its occurrence count, and the identity stays stable under rewrites that leave the call in place, so committed identities form a frozen prefix. The frozen unit is the call expression, and the surrounding code stays writable. A static gate rejects a rewrite that would change a committed call before a container starts. A runtime digest stops a changed payload at a committed identity, and an occurrence ledger names a repeated paid payload. Under three assumptions, a candidate the gate accepts replays every committed call it invokes, executes only new work, and reports the identities it skips. No local identity is both relocation-invariant and free of wrong reuse, so callsite identity turns a relocation into a pre-spend stop. On media workflows, 22 gate-passed repairs replay 258 committed identities with no duplicate, and a live six-way fanout makes seven paid calls instead of twelve. A 12-task customer-support study applies the same identity, replay, and boundary checks on two models. ## Screen Before You Serve: Simulation for Production Customer Experience AI Agents at 140M Scale DevFeed: [Screen Before You Serve: Simulation for Production Customer Experience AI Agents at 140M Scale]() Original publisher: [Read original article]() Author: Edesio Alcoba\\c{c}a, Kevin Rossell, Aman Gupta, Shao Tang, Jiwoo Hong, Pabel Carrillo-Mendoza, Wanderson Concei\\c{c}\\~ao Ferreira, Alvaro Tedeschi, Zayd Simjee, Shreya Rajpal, Bruno Finardi Hime, Chri Published: 2026-11-06T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [AI for customer service automation](), [agentic workflows](), [Test automation](), [AI Chat]() Tags: [abstract](), [agent](), [agentic](), [agentic-workflows](), [agents](), [ai](), [ai-agents](), [also](), [arxiv](), [backends](), [brazil](), [can](), [cs-ai](), [cs-cl](), [customer](), [customer-experience](), [experience](), [production](), [scale](), [screen](), [serve](), [simulation]() ### AI overview The Snowglobe workflow screens customer experience agents before deployment by having synthetic customers react to agent responses and simulating tool outputs, enabling multi-step tests without production backends. On Nubank's Card Delivery and expanded Card Management agents, simulated and production binary evaluator scores were highly correlated across four deployed versions. Simulation-guided iteration raised transactional net promoter score by 36.69 points in a live A/B test. Screening open-weight configurations across more than 16,000 simulated conversations preceded a live test in which the selected model raised self-service rate by 8.82 percentage points to Nubank's highest observed level, with no statistically significant tNPS change. The abstract reports results for this Nubank deployment; it does not establish that the workflow generalizes to other agents. ### Source excerpt arXiv:2609.30137v2 Announce Type: replace Abstract: Customer experience (CX) agents use tools and large language models to address customer requests and guide conversational interactions with an organization's products. Improving these agents, especially in regulated industries, is difficult: they must detect intent, follow complex operational policies and use tools reliably. Manual end-to-end testing offers limited coverage, while live experiments expose customers to failures that can erode trust. We present a hypothesis-driven simulation workflow for screening candidate CX agents before deployment. Synthetic customers react to agent responses and simulated tool outputs enable multi-step agentic workflows without invoking production backends. We use the Snowglobe simulator on Nubank's Card Delivery agent and its expanded successor, Card Management - Nubank's highest-volume chat-support agent in Brazil. Across 4 deployed versions, simulated and production version-level binary evaluator scores show high correlation. Simulation-guided iteration increased transactional net promoter score (tNPS) by 36.69 points in a live A/B test. We also screened open-weight configurations in over 16,000 simulated conversations. In a subsequent live A/B test, the selected model increased self-service rate (SSR) by 8.82 percentage points to the highest level observed at Nubank, with no statistically significant change in tNPS. Simulation made broad exploration of models, reasoning settings, and prompts feasible without customer exposure, enabling production improvements that would have been impractical to pursue through live experimentation alone. ## AI automation examples: 5 real things Zapier Sidekick has done DevFeed: [AI automation examples: 5 real things Zapier Sidekick has done]() Original publisher: [Read original article]() Author: Jay Ayinla Published: 2026-11-06T05:00:00Z Content type: news Language: en Sources: [Zapier]() Topics: [Artificial Intelligence](), [Automation]() Tags: [ai](), [ai-automation](), [api](), [apps](), [automation](), [calendar](), [calendar-invites](), [code](), [code-execution](), [company](), [coverage](), [customer](), [engineering](), [examples](), [faster](), [reports](), [stories](), [team]() ### AI overview Zapier Sidekick examples turn recurring chores into automations triggered by events or schedules. A Next Gen Zap generates searchable Enterprise deal analyses after each close for about $0.55; another workflow classifies out-of-office replies, extracts contact details, and saves them to connected tables, though its data use still needs review. A scheduling tool handled most of nearly 200 listening-tour invites across time zones. An observability team reported increasing target-use-case coverage from roughly 25% to 75% in two weeks, citing code execution, file handling, and inherited agent decision-making refinements. Customers also used Sidekick to archive more than 800 inactive Slack channels and speed spreadsheet work. The examples emphasize describing a chore plainly, connecting it to existing apps, and having results delivered on a schedule where they are already seen. ### Source excerpt A few months into our internal pilot, the Sidekick team noticed that most of what reached them was bug reports. Someone started a channel just for wins, a place where people could post what Sidekick had done for them. It filled up faster than anyone expected. We read through it, along with the pilot feedback threads and our own team channels, and picked five stories that made us stop scrolling. Everything below happened at Zapier or with a Zapier customer, and where people measured something, we ## Well, That Wasn't Fun: October 16 '26 DevFeed: [Well, That Wasn't Fun: October 16 '26]() Original publisher: [Read original article]() Author: Daniel Botha Published: 2026-11-06T00:00:00Z Content type: news Language: en Sources: [Fly]() Topics: [fly.io](), [virtual machines](), [virtualization](), [TCP/IP](), [Cryptography](), [payload](), [network-intelligence]() Tags: [2026](), [about](), [based](), [bug](), [but](), [can](), [card](), [cdn](), [chaos](), [checksums](), [close-to-users](), [code](), [corruption](), [cpu](), [customer](), [cve](), [deploy-app-servers](), [docker](), [elixir](), [example](), [exploit](), [field](), [fleet](), [fly](), [fly-io](), [fun](), [heroku-alternative](), [heroku-competitor](), [hosting](), [i](), [networking](), [october](), [postgresql-clusters](), [servers](), [the-fly-blog]() ### AI overview Fly.io patched and rebooted its fleet after CVE-2026-99999 exposed a flaw in Linux IPv4 checksum-offload validation for virtual machine traffic. By redirecting checksum writes, an attacker could alter two trusted IPv4 header bytes after routing and firewall checks, enabling a firewall bypass or a 32-byte out-of-bounds read that might expose host or neighboring VM data. Fly.io says it has no evidence customer data was exposed, but advises customers who ran there October 15-17 to rotate credentials sent over unencrypted protocols, including plaintext traffic over 6PN. The 10-hour rollout required staggered host reboots to limit disruption to Managed Postgres clusters; some machines remained down after hardware and reinstall problems, with no machines or customer data lost. Fly.io also revised incident communications so executives own the communication plan. Its practical security guidance includes encrypting internal traffic, using scoped and expiring agent credentials, limiting shared exposure across trust boundaries, treating agent inputs as hostile, and rehearsing credential rotation. ### Source excerpt October 16, 2026 began like any other day at Fly.io (meaning, we were anticipating only a medium amount of chaos). By that evening, we had patched and rebooted our entire fleet in response to a Linux kernel bug. The full rollout took 10 hours, but the emotional damage persists. CVE-2026-99999 The rest of this won't make much sense without a clear read on the bug itself, so: the Linux kernel CVE team and pwn.ai announced CVE-2026-99999 on the morning of the 15th. We heard about it that night, and started working on mitigations. IP sleight of hand Protocols like TCP and UDP include checksums to detect corruption on the way across the internet. Calculating one means reading the whole message, which adds up at high speeds. Linux avoids that with checksum offload: instead of calculating the checksum immediately, the kernel stores metadata saying where the checksum should read from and write to. If the checksum turns out not to be needed, the kernel never spends the CPU on it. Not needed, for example, when sending to localhost, or when the network card can add its own checksums. Checksum offload works the same way for virtual machines. The virtual network card accepts checksum metadata and passes it to the host. On the host side, the packet is treated like any other offloaded packet, using the metadata the guest provided. The offload code has checks meant to reject bad metadata. For IPv4, they were wrong. To exploit that, an attacker needs to do three things: point the checksum at a different header field, pick a message that produces the checksum they want, and force the host to calculate the checksum in software. The pwn.ai post goes into detail on pulling that off. The end result: the host receives the packet, parses it, decides where to send it, then applies the checksum, in the wrong place. The attacker now controls two bytes of an IPv4 header the host has already trusted. Based on network activity during the rollout, we believe it's unlikely that any of our customer ## Closing the context gap is what banking AI needs DevFeed: [Closing the context gap is what banking AI needs]() Original publisher: [Read original article]() Author: Rav Hayer Published: 2026-11-06T00:00:00Z Content type: article Language: en Sources: [Thoughtworks Insights]() Topics: [Artificial Intelligence](), [context](), [AI-Ready Data](), [Payment Fraud](), [Finance](), [Frontier AI]() Tags: [ai](), [architecture](), [aws](), [bank](), [banking](), [blueprint](), [budget](), [context](), [credit-card](), [credit-risk](), [credit-scoring](), [customer](), [customer-experience](), [cybersecurity](), [data](), [databricks](), [demo](), [deployment](), [developer](), [digital]() ### AI overview The article attributes the gap between banking AI pilots and production to missing transaction context, not primarily to model quality. General-purpose language models lack native understanding of transactions, while transaction foundation models are trained on transaction events; it cites examples and reported results from Mastercard, Revolut and Stripe. Rebuilding customer and transaction context separately for each pilot adds cost and inconsistency, so the article describes a shared intelligence layer trained on an institution's history for fraud, credit risk, payments and customer experience. It also reports a hybrid strategy: frontier models for complex reasoning and smaller, fine-tuned models for narrow tasks near sensitive data. This requires governance from the outset, including records of prompts and answers, access limits and reconstructable decisions. A financial loss can make the case for moving a use case from proof of concept to deployment. ### Source excerpt Closing the context gap is what banking AI needs [Next page]()