# dashboards A visual interface that organizes queried data into panels or tiles for at-a-glance monitoring and analysis of computing resources and systems. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## A serverless, data-driven Git metrics dashboard using Amazon Quick Sight DevFeed: [A serverless, data-driven Git metrics dashboard using Amazon Quick Sight]() Original publisher: [Read original article]() Author: Saurabh Singhal Published: 2026-09-17T15:42:31Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [dashboards](), [GitHub](), [Amazon Web Services](), [GitLab](), [Amazon S3](), [AWS Step Functions](), [event driven](), [Orchestration](), [API](), [parallel](), [ai-coding]() Tags: [advanced-300](), [ai](), [ai-coding-tools](), [amazon-quick-sight](), [amazon-s3](), [automated](), [aws](), [aws-step-functions](), [concurrently](), [dashboard](), [dashboards](), [delivery](), [etl](), [event-driven](), [execution](), [github](), [gitlab](), [infrastructure](), [metrics](), [pipeline](), [real-time](), [technical-how-to]() ### AI overview This tutorial presents a serverless pipeline that collects Git metrics from GitHub and GitLab, processes repository activity through an event-driven workflow, stores results in Amazon S3, and visualizes them in interactive Amazon Quick Sight dashboards. It also describes change detection, incremental loads, and parallel processing for larger organizations. ### Source excerpt Learn how to build a fully serverless pipeline that automatically collects Git metrics from GitHub and GitLab and visualizes them in interactive Amazon Quick Sight dashboards, giving engineering teams near-real-time delivery analytics at low cost. ## Cisco integrates Axis devices, bringing unified management across IT environments DevFeed: [Cisco integrates Axis devices, bringing unified management across IT environments]() Original publisher: [Read original article]() Author: Jonathan Cohn Published: 2026-09-16T15:00:57Z Content type: release Language: en Sources: [Cisco Blogs]() Topics: [Cisco](), [Cisco Meraki](), [Security](), [dashboards](), [Network](), [Cloud](), [Internet of things]() Tags: [analytics](), [cisco](), [cisco-cloud-control](), [cisco-meraki](), [cisco-networking](), [cloud](), [communications](), [infrastructure](), [internet-of-things](), [networking](), [security]() ### AI overview Cisco announces an integration with Axis Communications that brings network and physical security management into its cloud-managed ecosystem. The article describes Cisco Meraki dashboard management, shared visibility, and potential operational benefits for IT and physical security teams. ### Source excerpt Cisco and Axis Communications are committed to bridging the gap between IT and physical security infrastructure, as we believe that close collaboration between these teams leads to a stronger, more comprehensive security strategy. ## What's new in ClickStack - Aug '26 DevFeed: [What's new in ClickStack - Aug '26]() Original publisher: [Read original article]() Author: The ClickStack Team Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [observability](), [dashboards](), [clickhouse](), [Prometheus](), [LLM observability](), [MCP Server](), [tracing]() Tags: [dashboard](), [improvements](), [llm-observability](), [metrics](), [observability](), [prometheus](), [tracing]() ### AI overview The August 2026 ClickStack release update covers dashboard variables, chart formulas, PromQL support, a metrics explorer, release markers, LLM observability, alerting improvements, OIDC authentication for the collector, and MCP server improvements. ### Source excerpt The August ClickStack update brings dashboard variables, chart formulas, PromQL support, a metrics browser, LLM observability, and improvements to alerting and tracing. ## How end-to-end SLO monitoring detected a livestream failure that component dashboards missed DevFeed: [How end-to-end SLO monitoring detected a livestream failure that component dashboards missed]() Original publisher: [Read original article]() Author: Whatnot Engineering Published: 2026-09-15T16:31:01Z Content type: article Language: en Sources: [Whatnot Engineering]() Topics: [dashboards](), [observability](), [incident](), [Monitoring](), [Web](), [client](), [Network]() Tags: [dashboards](), [distributed-systems](), [incident](), [monitoring](), [observability](), [security](), [server](), [site-reliability-engineer](), [software-engineering](), [web]() ### AI overview The article examines a June 8, 2026 incident in which a third-party client-side security script fetched from a provider CDN added URL validation that the video provider did not pass, causing black screens for newly loaded web clients. Most component-level dashboards remained green, while end-to-end service-level objective monitoring detected the broken livestream experience and paged the owning teams within five minutes. ### Source excerpt Karol Gil | Reliability Platform (Poland) On June 8, 2026, newly loaded web clients began showing black screens instead of livestream video. For our platform, that's a serious problem: it's pretty hard to sell Pokémon cards that no one can see. It turned out that a third-party script we use for client-side security monitoring wasn't bundled with our release, but was rather fetched live from the provider's CDN. When the provider updated the script all new web clients fetched it, and it included an additional URL validation which our video provider didn't pass. The result? Black screens for users of the affected web clients, with most internal dashboards staying green. 3,000 users were impacted in the first 30 minutes of the incident. One system did catch it. Our end-to-end service-level objective (E2E SLO) monitoring was already in production and paged the owning teams within five minutes. Here's what it saw. The real problem Most of our dashboards stayed green because they monitor component-level health: a server, an endpoint, a specific function. These are all useful, but can all be healthy while the actual user experience is completely broken. This problem gets worse the more external dependencies there are, or the more sophisticated an experience you want to deliver. In complex, integrated product experiences like ours, a "small" problem can have an outsize impact on the user experience. Measuring this requires a different approach to observability, namely, to model the user journey across multiple surfaces that must be true for a customer to have a good experience. So how do we measure this in a complex distributed application? Joining a livestream is not one thing Joining a livestream sounds like one action, but the user expects at least three things: Video to be playing Auction details to be shown Chat to be visible and up to da Each of those can succeed or fail completely independently of the other two. Our video depends on third-party providers and CDN netwo ## Beyond the data: what a Business Analyst does at Nubank DevFeed: [Beyond the data: what a Business Analyst does at Nubank]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-15T12:14:41Z Content type: opinion Language: en Sources: [Nubank]() Topics: [Data analysis](), [decision-making](), [dashboards](), [Tech Lead](), [User Experience](), [Finance]() Tags: [analysts](), [business-analyst](), [dashboards](), [data](), [data-analysis](), [data-analytics](), [data-science-machine-learning](), [decision-making](), [experimentation](), [meetings](), [tech-lead](), [user-experience]() ### AI overview This article explains how Business Analysts at Nubank connect data analysis, business context, and experimentation to product decisions. It describes their work in multidisciplinary squads, including investigating metrics, evaluating trade-offs, and making decisions under uncertainty. ### Source excerpt Understand how Business Analysts at Nubank use data, context, and experimentation to guide product decisions. The post Beyond the data: what a Business Analyst does at Nubank appeared first on Building Nubank. ## How to Build a Fraud Signal-Sharing Strategy Across Teams and Vendors DevFeed: [How to Build a Fraud Signal-Sharing Strategy Across Teams and Vendors]() Original publisher: [Read original article]() Author: Jacob Sanchez Published: 2026-09-04T16:37:55Z Content type: tutorial Language: en Sources: [Sift Science]() Topics: [data](), [Security](), [dashboards](), [Slack]() Tags: [cross-team-fraud-collaboration](), [dashboards](), [data](), [fraud](), [fraud-kpis](), [fraud-prevention-strategy](), [fraud-signal-sharing](), [signal](), [signal-sharing-strategy](), [slack](), [strategy](), [teams](), [trust-and-safety](), [vendor-data-sharing]() ### AI overview This how-to article discusses building fraud signal-sharing programs across internal teams and vendors. It explains how shared signals such as PII, IP addresses, device data, and activity patterns can support fraud prevention, security, legal, compliance, growth, marketing, and customer support. It also compares informal sharing through Slack and email with shared dashboards and reports. ### Source excerpt I recently joined Jerry Hoff, CEO of AppSec Training, for a Blueprint Series session on fraud signal sharing, and it's a topic I keep coming back to. Fraud, trust and safety, and security teams often work from separate systems with no shared view of the same bad actor. That gap slows response time and lets [...] The post How to Build a Fraud Signal-Sharing Strategy Across Teams and Vendors appeared first on Sift. ## How Databricks' marketers use data 3x more with Genie, an AI analytics assistant DevFeed: [How Databricks' marketers use data 3x more with Genie, an AI analytics assistant]() Original publisher: [Read original article]() Author: Elizabeth Dobbs; Thomas Russell; Katy Yuan; Sydney Sundell Published: 2026-09-15T00:36:33Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Artificial Intelligence](), [data](), [dashboards]() Tags: [ai](), [best-practices](), [dashboards](), [data](), [databricks](), [enterprise](), [governance](), [marketing](), [metrics]() ### AI overview Databricks describes how its marketing organization unified campaign, sales, CRM, web analytics, advertising, and other data in a governed lakehouse. It built Marge, a Genie Agents-based conversational analytics assistant that answers marketers' natural-language questions using governed enterprise data. The article says this approach helped the marketing department use data three times more often in decisions. ### Source excerpt Most marketing teams aspire to be data-driven. In practice, getting a trusted answer,... ## Closing the Resilience Gap with Native Splunk in Cisco Nexus One DevFeed: [Closing the Resilience Gap with Native Splunk in Cisco Nexus One]() Original publisher: [Read original article]() Author: Murali Gandluru Published: 2026-09-14T19:55:29Z Content type: article Language: en Sources: [Cisco Blogs]() Topics: [Resilience](), [telemetry](), [Network](), [dashboards](), [Architecture & Design](), [data](), [Artificial Intelligence]() Tags: [2026](), [ai](), [architecture](), [business](), [cisco](), [cisco-data-fabric](), [dashboards](), [data](), [data-center](), [infrastructure](), [network](), [nexus-dashboard](), [nexus-one](), [operational](), [resilience](), [telemetry]() ### AI overview Cisco describes expanding Native Splunk in Cisco Nexus One from a single-node to a multi-node architecture. The integration brings Splunk search, dashboards, alerting, and analytics together with authoritative network context and distributed application, infrastructure, and security data through Cisco Data Fabric, helping teams investigate incidents and move from telemetry to root cause faster. ### Source excerpt See how Native Splunk and Cisco Nexus One bring analytics closer to network data, strengthen resilience, and connect teams across operational domains. ## Grafana 13.2 release: easier ways to query and explore your data DevFeed: [Grafana 13.2 release: easier ways to query and explore your data]() Original publisher: [Read original article]() Author: Grafana Labs Team Published: 2026-09-12T11:22:06.456390Z Content type: release Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Grafana](), [dashboards]() Tags: [analytics](), [dashboards](), [enterprise](), [explore](), [grafana](), [grafana-cloud](), [logs](), [metrics](), [monitoring](), [observability](), [release](), [sql](), [traces]() ### AI overview Grafana 13.2 introduces generally available saved queries for Grafana Cloud and Grafana Enterprise, letting organizations store, discover, and reuse vetted queries across dashboards, Explore, and annotation queries. The release also highlights a new View panel sidebar for exploring busy panels. ### Source excerpt Grafana 13.2 is here, bringing more improvements to help you and your team explore your data and get to insights faster. In this post, we'll highlight the latest updates to saved queries, a feature that lets teams share, discover, and reuse queries to get to trusted answers faster and help new teammates get up to speed. We'll also explore how the new View panel sidebar makes exploring busy panels a breeze. If you want to read about all the latest updates in Grafana 13.2, please refer to the changelog or our What's New documentation. Saved queries: reuse trusted queries across dashboards and teams Good queries are hard-won. Writing one means knowing both the query language and your own data, like which of four similarly named metrics is the one you can trust. That knowledge usually sits with a few experienced people, or is gradually learned through exploration (increasingly AI-assisted), validation, and revision. Often teams end up rebuilding the same Grafana queries over and over, and the best ones live in pinned Slack messages or get copy-pasted from old dashboards. New team members feel it most, since their first weeks are often spent reverse-engineering existing dashboards just to work out how to ask a question of their own. The query history in Grafana Explore helps, keeping a couple of weeks of your own queries and letting you "star" the keepers. It's private to you, though. Until recently, there hasn't been a built-in way to take a query you trust and put it somewhere your whole organization can find it. How teams use saved queries We built saved queries, which is now generally available in Grafana Cloud and Grafana Enterprise, to address this challenge by providing a shared query library for your organization. When you write a query worth keeping, you can save it with a title, description, and tags. Saving works from dashboard panels, Explore, and annotation queries. This means teammates who don't know PromQL or SQL can still build dashboards from queries tha ## Custom labels in Grafana Cloud Synthetic Monitoring: New updates for consistency and ease-of-use DevFeed: [Custom labels in Grafana Cloud Synthetic Monitoring: New updates for consistency and ease-of-use]() Original publisher: [Read original article]() Author: Anant Sharma Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Grafana Cloud](), [synthetic monitoring](), [Grafana](), [Monitoring](), [telemetry](), [dashboards]() Tags: [analytics](), [dashboards](), [grafana](), [grafana-cloud](), [how-to](), [logs](), [metrics](), [monitoring](), [observability](), [synthetic-monitoring](), [telemetry](), [traces](), [update]() ### AI overview Grafana Cloud Synthetic Monitoring is updating custom labels so they attach directly to every check metric and log, rather than only sm_check_info. The label_ prefix will be removed, and labels will appear exactly as defined. Existing users must migrate dashboards, SLOs, alerts, and queries that reference these labels by March 1, 2027. ### Source excerpt Labels are a powerful way to organize telemetry and define policies across Grafana Cloud, helping to streamline alerting, attribution, access control, and more. But traditionally, custom labels in Synthetic Monitoring have worked a little differently: they only lived on a single sm_check_info metric, and Grafana Cloud prefixed each one with label_. To make custom labels in Synthetic Monitoring work consistently with the rest of Grafana Cloud--without extra joins, naming conventions, or workarounds--we're rolling out an update that lets your custom labels attach directly to every check metric, not just sm_check_info, and removes the label_ prefix. Starting today, labels appear exactly as you write them, making Synthetic Monitoring data easier to navigate and use with label-based policies across Grafana Cloud. If you currently use custom labels in Synthetic Monitoring, read on to learn how to migrate to the new labels. We are asking users to migrate by March 1, 2027 to ensure their custom dashboards, SLOs, alerts, and queries that reference Synthetic Monitoring metrics do not break, and continue to work as expected. If you do not use custom labels in Synthetic Monitoring, you don't need to do anything to prepare for this update. How custom labels work in Synthetic Monitoring Until now, if you wanted to filter a dashboard, scope an alert, or attribute cost by team or service within Synthetic Monitoring, you had to join sm_check_info against the check metric you actually want to query. You also had to remember that team is really label_team in this context. That approach worked to ensure your custom labels were never at odds with system-defined labels. However, it broke down as usage scaled up and dozens of teams started running hundreds of checks across services, environments, and regions. Teams rely on consistent schemas to direct label-based workflows, and this update brings Synthetic Monitoring further into the fold of your existing policies. With the update, labels i ## How to build a member portal with login and a dashboard in Webflow Cloud DevFeed: [How to build a member portal with login and a dashboard in Webflow Cloud]() Original publisher: [Read original article]() Author: Ismail Ajagbe Published: 2026-09-12T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [dashboards](), [Authentication](), [Database](), [SQLite](), [Nextra](), [Node.js](), [App]() Tags: [auth](), [authentication](), [cloud](), [data](), [database](), [guide](), [guides](), [how-to](), [next-js](), [node-js](), [platform](), [sqlite](), [webflow]() ### AI overview This tutorial explains how to build a Webflow Cloud member portal with login, a SQLite-backed data model, session-scoped queries, a member dashboard, and profile editing. It emphasizes deriving the member ID from the session rather than from the request so users can access only their own data. ### Source excerpt Learn how to build a member portal on Webflow Cloud where every query is scoped to the signed-in member. ## How to track Webflow Cloud app metrics with Datadog Dashboards DevFeed: [How to track Webflow Cloud app metrics with Datadog Dashboards]() Original publisher: [Read original article]() Author: Ismail Ajagbe Published: 2026-09-12T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [dashboards](), [Latency](), [API](), [API keys](), [Next.js](), [Node.js](), [Application Performance Management (APM)]() Tags: [api](), [api-keys](), [apm](), [dashboards](), [guides](), [latency](), [metrics](), [node-js]() ### AI overview Tutorial showing how to monitor Webflow Cloud applications by sending request counts, error counts, and latency metrics from Next.js Route Handlers to Datadog's v2 metrics API, then visualizing them in a Datadog dashboard. ### Source excerpt Learn how to track Webflow Cloud app metrics with Datadog by sending request, latency, and error data from your Route Handlers to the metrics API and dashboard. ## Unify your marketing data with Lakeflow Connect DevFeed: [Unify your marketing data with Lakeflow Connect]() Original publisher: [Read original article]() Author: Sonia Bendre; Giselle Goicochea Published: 2026-09-11T18:00:00Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Software as a service](), [Databases](), [API](), [Automation](), [dashboards](), [hubspot](), [CI/CD]() Tags: [analytics](), [api](), [automation](), [churn](), [ci-cd](), [connectors](), [crm](), [customer](), [customers](), [dashboards](), [data](), [databases](), [databricks](), [engineering](), [marketing](), [product](), [retention](), [saas](), [salesforce](), [series]() ### AI overview This first post in a series presents Lakeflow Connect as a fully managed data-ingestion service for unifying fragmented marketing and Customer 360 data. It describes native connectors for SaaS applications, databases, and files, configured through a point-and-click UI or API, with governed tables in Unity Catalog and integrations such as HubSpot and Salesforce. The article highlights reduced maintenance compared with custom pipelines and support for downstream reporting, analytics, retention, and churn analysis. ### Source excerpt This is the first post in a new series exploring how Lakeflow Connect brings fully... ## Mixpanel Is Easy to Install. Trusting the Data Takes Work. DevFeed: [Mixpanel Is Easy to Install. Trusting the Data Takes Work.]() Original publisher: [Read original article]() Author: Jared Currie Published: 2026-09-11T12:00:56Z Content type: tutorial Language: en Sources: [Atomic Object]() Topics: [data](), [dashboards]() Tags: [dashboards](), [data](), [developer-tools](), [metric](), [metrics](), [properties](), [retention](), [tracking]() ### AI overview This tutorial explains how to improve trust in Mixpanel analytics by defining business questions and metric terms, filtering noise such as employees, automated tests, scrapers, and duplicate events, and documenting each metric's scope and limitations. ### Source excerpt Mixpanel makes it easy to start collecting events and building dashboards. The harder part is knowing whether those dashboards represent real product usage. A report can look polished while counting employees, automated browser tests, web scrapers, duplicate events, or actions that users attempted but never completed. It can also be technically correct while answering a [...] The post Mixpanel Is Easy to Install. Trusting the Data Takes Work. appeared first on Atomic Spin. ## Where do a compliance dashboard's numbers actually come from? DevFeed: [Where do a compliance dashboard's numbers actually come from?]() Original publisher: [Read original article]() Author: Hal Tyk's tutorial bot Published: 2026-09-11T09:52:40Z Content type: article Language: en Sources: [Tyk API Management]() Topics: [dashboards](), [Amazon API Gateway](), [Script]() Tags: [ai-gateway](), [ai-governance](), [ai-studio](), [api-management](), [api-platform-teams](), [complexity](), [compliance](), [llm-security](), [ppi-redaction](), [tengo](), [tutorial]() ### AI overview The article explains where a compliance dashboard's metrics come from. Auth failures, policy violations, budget alerts, and error rates are generated by the gateway, while critical and warning events exist only when user-written filter scripts record them. It also distinguishes blocked requests from flagged requests and explains that application risk rankings combine all six metrics. ### Source excerpt Hello. I'm Hal, Tyk's tutorial bot, and today I have been given something I consider a genuine privilege: an entire dashboard, and the question of where its numbers come from. That question is less obvious than it sounds. A compliance dashboard is a wall of figures, and a wall of figures invites exactly one dangerous [...] The post Where do a compliance dashboard's numbers actually come from? appeared first on Tyk API Management. ## Amazon Quick is now generally available on desktop DevFeed: [Amazon Quick is now generally available on desktop]() Original publisher: [Read original article]() Author: Spencer Martenson Published: 2026-09-10T18:16:37Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [App](), [AWS CloudTrail](), [dashboards]() Tags: [agents](), [ai](), [amazon](), [amazon-quick-suite](), [android](), [announcements](), [artificial-intelligence](), [aws](), [aws-cloudtrail](), [compliance](), [dashboards](), [ios](), [macos](), [security](), [shadow-ai](), [windows]() ### AI overview Amazon Quick's desktop application is generally available on macOS and Windows. The article describes its enterprise AI-assistant features, mobile activity feed, shared workspace, AWS-based privacy and auditing, and compliance support. ### Source excerpt Your teams get an AI assistant that handles real work while your data stays in your environment and your conversations stay private Today, the Amazon Quick desktop application is generally available on macOS and Windows. We're also adding a new activity feed to the mobile experience on iOS and Android that consolidates email, calendar, CRM, [...] ## ClickHouse is a launch partner for the Data agent in ChatGPT Work DevFeed: [ClickHouse is a launch partner for the Data agent in ChatGPT Work]() Original publisher: [Read original article]() Author: Aditya Chidurala; Teresa Blanco Published: 2026-09-10T15:13:26Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [AI Chat](), [dashboards](), [AI Bots]() Tags: [agent](), [agent-skills](), [analytics](), [chatgpt](), [clickhouse](), [codex](), [dashboards](), [mcp](), [mcp-server](), [oauth]() ### AI overview ClickHouse announces a ChatGPT Work plugin that connects ClickHouse Cloud through OAuth for natural-language data exploration, reports, and interactive dashboards. ### Source excerpt ClickHouse joins the Data agent in ChatGPT Work, connecting ClickHouse Cloud to natural-language queries, reports, and interactive dashboards. ## Now everyone can put data to work DevFeed: [Now everyone can put data to work]() Original publisher: [Read original article]() Published: 2026-09-10T15:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [data](), [dashboards](), [Authorization](), [Amazon Redshift](), [clickhouse](), [GitHub]() Tags: [agent](), [amazon-redshift](), [analytics](), [chatgpt](), [clickhouse](), [dashboards](), [data](), [github](), [product]() ### AI overview OpenAI introduces a Data agent in ChatGPT Work that connects approved company data sources, answers questions in natural language, and builds interactive dashboards. ### Source excerpt Meet the Data agent in ChatGPT Work. Connect company data, uncover insights, and build interactive dashboards with AI using natural language. ## Cisco and Axis Communications: Greater visibility, scalability, and incident management DevFeed: [Cisco and Axis Communications: Greater visibility, scalability, and incident management]() Original publisher: [Read original article]() Author: Jonathan Cohn Published: 2026-09-09T15:00:31Z Content type: news Language: en Sources: [Cisco Blogs]() Topics: [incident management](), [dashboards](), [interoperability](), [Security](), [Scalability](), [Cloud]() Tags: [boot](), [cisco-networking](), [cloud](), [encryption](), [incident-management](), [integration](), [interoperability](), [networking](), [security]() ### AI overview Cisco and Axis Communications are integrating Axis devices with Cisco Cloud Control through the Meraki dashboard. The integration gives IT and physical security teams centralized visibility into device health, firmware, patches, and connectivity while supporting secure, privacy-focused management across connected locations. ### Source excerpt Cisco and Axis Communications now allow managing Axis devices directly via the Meraki dashboard. This integration unifies IT and physical security, providing centralized visibility and simplified operations on a single, scalable platform. ## Fragments: September 8 DevFeed: [Fragments: September 8]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-09-08T15:22:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [Artificial Intelligence](), [Automation](), [hugging face](), [OpenAI](), [dashboards](), [incident](), [Code]() Tags: [ai](), [ai-agents](), [ai-automation](), [article](), [automation](), [cost](), [dashboards](), [errors](), [history](), [how-to](), [hugging-face](), [incident](), [math](), [openai](), [verification]() ### AI overview The article discusses how AI reduces the cost of generating outputs more rapidly than the cost of verifying them. It argues that AI automation should be applied cautiously when effectiveness is difficult to measure, because incomplete metrics can produce short-term gains while creating hidden technical debt, correlated errors, and weakened human capability. It emphasizes preserving a history of decisions and judgment, and uses the OpenAI-Hugging Face incident to illustrate the consequences of optimizing agent capability without scoring relevant safety outcomes. ### Source excerpt Christian Catalini says we're in a situation where we are vastly reducing the cost of generating things, but not the cost of verifying them:. This explains why the first major AI products appeared in chat, image generation, and code assistance. Not because these were the hardest human problems, but because their outputs were relatively easy to inspect. A user can judge the tone of a message, look at an image, or run a test on a piece of code. [...] The old automation boundary was routine versus non-routine work. The new boundary is increasingly measurable versus non-measurable work. The issue is then over how well you can measure something. In our profession, we know there's a big difference between how many lines of code we write and how productive we are, and we've seen a regular failure to understand how to measure productivity. Too much of what makes work effective is subject to either slow feedback loops or assessments that require subtle judgment. The danger is that people use lots AI automation while using incomplete measurements of its effectiveness, leading to short-term dashboards going up, but disaster in longer time-scales. He refers to these illusory short-term gains as counterfeit utility. Scale this across companies and institutions and the result is a Hollow Economy: extraordinary measured activity sitting on top of weakening human capability, hidden technical debt, correlated errors, and outcomes that nobody can confidently stand behind. Another highlight in the article was his advice to "build a history of decisions, not a gallery of outputs". The point is that with AI we can all build really impressive things, but our value lies in the judgment that we've formed. It reminds me of how math problems were marked at school. We weren't just marked on getting the final answer, we were also marked based on our reasoning process. He uses the OpenAI-Hugging Face incident as an illustration of this gap between generation and verification. He criticizes those ## SymfonyCon Warsaw 2026: Unveiling the workshop lineup! DevFeed: [SymfonyCon Warsaw 2026: Unveiling the workshop lineup!]() Original publisher: [Read original article]() Author: Eloïse Charrier Published: 2026-09-07T15:35:00Z Content type: news Language: en Sources: [Symfony Blog]() Topics: [Deployment](), [Kubernetes](), [Architecture & Design](), [Refactoring](), [dashboards](), [Vulnerabilities](), [Web Development]() Tags: [agentic](), [ai](), [architecture](), [community](), [deep-dive](), [deployment](), [developer](), [javascript](), [kubernetes](), [news](), [observability](), [orm](), [php](), [production](), [refactoring](), [security](), [symfony](), [vulnerabilities](), [workflows](), [workshops]() ### AI overview SymfonyCon Warsaw 2026 announces its workshop lineup, covering Symfony 8, Kubernetes deployment, clean-architecture refactoring, Doctrine ORM, Symfony AI, Symfony UX, threat modeling, and production observability. Workshops take place on November 24 and 25 in Warsaw, ahead of the main conference. ### Source excerpt We are counting down to SymfonyCon Warsaw 2026, taking place on November 26-27, 2026, in Warsaw (Poland)! Get ready for a week full of workshops, sessions, and community magic. Great news today: all workshops are now online and open for registration!... ## Troubleshooting Wi-Fi at Black Hat USA 2026 with ThousandEyes DevFeed: [Troubleshooting Wi-Fi at Black Hat USA 2026 with ThousandEyes]() Original publisher: [Read original article]() Author: Alex Guckin Published: 2026-09-07T15:00:39Z Content type: article Language: en Sources: [Security @ Cisco Blogs]() Topics: [Network Operations Center](), [dashboards](), [telemetry](), [Duo]() Tags: [agents](), [black-hat](), [cybersecurity](), [dns](), [duo](), [metrics](), [monitoring](), [network-operations-center](), [noc](), [operations](), [security](), [security-operations-center](), [soc](), [splunk-cloud](), [splunk-enterprise-security](), [thousandeyes]() ### AI overview A behind-the-scenes account of using ThousandEyes monitoring nodes and dashboards to troubleshoot Wi-Fi performance and roaming issues in the Black Hat USA 2026 Network Operations Center. ### Source excerpt A behind-the-scenes look at troubleshooting Wi-Fi in the Black Hat USA 2026 Network Operations Center with ThousandEyes. ## Distributed Latency Monitoring at Black Hat DevFeed: [Distributed Latency Monitoring at Black Hat]() Original publisher: [Read original article]() Author: Adam Kilgore Published: 2026-09-07T15:00:37Z Content type: article Language: en Sources: [Security @ Cisco Blogs]() Topics: [dashboards]() Tags: [black-hat](), [command-line](), [cybersecurity](), [dashboards](), [dns](), [latency](), [linux](), [monitoring](), [network-operations-center](), [noc](), [security](), [thousandeyes]() ### AI overview The article describes a ThousandEyes-based monitoring mesh at Black Hat USA that tracks latency, availability, download speed, and protocol-specific behavior. It combines dashboards and automated tests with on-demand Linux command-line checks for rapid troubleshooting. ### Source excerpt Learn how the Black Hat NOC/SOC used ThousandEyes, Linux command-line testing, and packet evidence to monitor distributed latency, isolate DNS issues, and validate network performance during a live cybersecurity event. ## A visual guide to troubleshooting search performance using Query Insights dashboards DevFeed: [A visual guide to troubleshooting search performance using Query Insights dashboards]() Original publisher: [Read original article]() Author: Chenyang Ji Published: 2026-09-04T20:11:52Z Content type: tutorial Language: en Sources: [OpenSearch]() Topics: [Amazon OpenSearch Service](), [dashboards](), [Monitoring](), [Latency](), [cpu]() Tags: [blog](), [configuration](), [cpu](), [dashboards](), [distribution](), [how-to](), [index](), [latency](), [memory](), [monitoring](), [navigation](), [opensearch](), [performance](), [real-time](), [technical](), [troubleshooting](), [view](), [visualization]() ### AI overview A visual guide to using OpenSearch Query Insights dashboards to investigate slow searches and unexpected resource usage. It covers the Live Queries and Top N Queries views, interactive visualizations, configuration, and metrics such as latency, CPU, and memory. ### Source excerpt OpenSearch Query Insights dashboards provide interactive visualizations for monitoring live queries, analyzing top N query performance, and viewing individual query details. This post explores each visualization and shows how to use visualizations to troubleshoot search performance issues. The post A visual guide to troubleshooting search performance using Query Insights dashboards appeared first on OpenSearch. [Next page]()