# teams Published articles for teams. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Podcast: Signals and Levers: Building Thriving Engineering Organizations DevFeed: [Podcast: Signals and Levers: Building Thriving Engineering Organizations]() Original publisher: [Read original article]() Author: Elisabeth Hendrickson, Joel Tosi Published: 2026-09-18T09:05:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [systems](), [Culture & Methods](), [engineering-leadership](), [engineering-culture](), [Software](), [Artificial Intelligence]() Tags: [agile](), [building-thriving-engineering-organizations](), [collaboration](), [culture](), [culture-methods](), [distributed-team](), [engineering](), [engineering-culture](), [engineering-culture-podcast](), [engineering-leadership](), [infoq](), [leadership](), [podcast](), [psychological-safety](), [software-craftsmanship](), [software-delivery](), [systems](), [team-collaboration](), [teams](), [teamwork](), [technical-debt]() ### AI overview This podcast discusses systems thinking as a way to understand software delivery and engineering organizations. Elisabeth Hendrickson and Joel Tosi explain how leadership policies, everyday cultural behavior, context, and continuous course-correction shape sustainable engineering teams, including amid AI-driven change. ### Source excerpt In this podcast Shane Hastie, Lead Editor for Culture & Methods spoke to Elisabeth Hendrickson and Joel Tosi about systems thinking as a lens for software delivery, the cultural "levers" leaders and teams can pull to shape organizations, and building thriving, sustainable teams amid AI-driven change. By Elisabeth Hendrickson, Joel Tosi ## 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. ## What a Technical Program Manager actually does DevFeed: [What a Technical Program Manager actually does]() Original publisher: [Read original article]() Author: Dean Hume Published: 2026-09-14T15:54:31Z Content type: opinion Language: en Sources: [Dean Hume]() Topics: [Software Engineering]() Tags: [delivery](), [dependency](), [engineering](), [execution](), [meetings](), [ownership](), [project](), [software-engineering](), [teams](), [technical](), [technical-program-manager]() ### AI overview This article explains the role of a Technical Program Manager, focusing on the gaps between engineering teams, explicit ownership, cross-team dependencies, and proactive identification of release-blocking problems. ### Source excerpt What does a Technical Program Manager actually do? A look at the gaps they fill, the decisions they chase, and the reactive vs proactive split. ## An open Letter about the implosion of Cambium Networks DevFeed: [An open Letter about the implosion of Cambium Networks]() Original publisher: [Read original article]() Author: j2sw Published: 2026-09-13T15:06:01Z Content type: opinion Language: en Sources: [Justin Wilson (j2sw)]() Topics: [Networks]() Tags: [cambium](), [company](), [corporate](), [networks](), [product](), [teams](), [technology-industry-commentary](), [wisp]() ### AI overview An open letter argues that Cambium Networks' corporate leadership failed employees, customers, investors, operators, distributors, and advocates. It distinguishes corporate-level failures from the work of Cambium's product and operations teams, while noting uncertainty about the company's next steps. ### Source excerpt To the product teams, support organization, partners, and remaining employees of Cambium Networks: First and foremost, my heart goes out to you during these tough, uncertain times. Over the past couple of years, Cambium has taken a beating in the forums and groups. Those of us who understand how the world works always knew that ... Read more The post An open Letter about the implosion of Cambium Networks appeared first on Justin Wilson (j2sw). ## AI is making it easier for managers to build software without involving their teams DevFeed: [AI is making it easier for managers to build software without involving their teams]() Original publisher: [Read original article]() Author: Stephane Moreau Published: 2026-09-13T11:12:28Z Content type: opinion Language: en Sources: [Blog for Engineering Managers]() Topics: [Artificial Intelligence](), [Software]() Tags: [ai](), [software](), [teams]() ### AI overview The article argues that AI has reduced the effort required for managers to build software, making them less likely to consult their teams first. ### Source excerpt AI removed the effort that used to make them ask first. ## Blizzard union workers ratify historic contract covering 1,900 employees DevFeed: [Blizzard union workers ratify historic contract covering 1,900 employees]() Original publisher: [Read original article]() Author: Chris Kerr Published: 2026-09-09T12:59:56Z Content type: news Language: en Sources: [gamedeveloper]() Topics: [Microsoft](), [Generative AI](), [Xbox]() Tags: [ai](), [company](), [generative](), [layoff](), [microsoft](), [news](), [remote](), [teams](), [work](), [xbox]() ### AI overview Blizzard union workers ratified a contract covering 1,900 employees. The agreement includes generative AI usage safeguards, wage increases, hybrid and remote-work benefits, layoff protections, severance, and recall rights. ### Source excerpt Union members have secured guardrails around generative AI usage, layoff protections, remote and hybrid work benefits, and more. ## Nuxt co-creator Alexandre Chopin joins Encore DevFeed: [Nuxt co-creator Alexandre Chopin joins Encore]() Original publisher: [Read original article]() Author: Marcus Kohlberg Published: 2026-09-07T00:00:00Z Content type: news Language: en Sources: [Encore Updates]() Topics: [Provisioning](), [Cloud](), [Vue.js](), [Command-line interface](), [GitHub](), [Open Source](), [SDKs](), [AI-assisted coding]() Tags: [agentic-development](), [ai-agents](), [cli](), [developer](), [developer-relations](), [github](), [infrastructure](), [open-source](), [provisioning](), [sdk](), [teams](), [vue]() ### AI overview Encore announces that Alexandre Chopin, co-creator of Nuxt, has joined the company as Head of Developer Relations. He will work with developers and engineering teams and help shape Encore's roadmap around automated infrastructure. ### Source excerpt Alexandre joins as Head of Developer Relations to help bring automated infrastructure to more developers and teams. ## The Pulse: Meta wanted to reduce teams by 60% because of AI DevFeed: [The Pulse: Meta wanted to reduce teams by 60% because of AI]() Original publisher: [Read original article]() Author: Ivan Klaric Published: 2026-09-03T17:01:49Z Content type: opinion Language: en Sources: [The Pragmatic Engineer]() Topics: [Meta](), [Artificial Intelligence](), [AI Strategy](), [Job]() Tags: [ai](), [ai-training](), [company](), [engineering](), [facebook](), [instagram](), [job-cuts](), [leadership](), [meta](), [organization](), [reporting](), [teams]() ### AI overview The article examines Meta's proposed Project Organization Transformation, which envisioned reducing many existing teams by 60% as AI took over more daily work. It also discusses earlier layoffs, reassignment of engineers to AI training, low morale, and operational outages, while noting that the larger layoff plan did not proceed. ### Source excerpt An in-depth report by Reuters details how Meta's leadership decided to slash team sizes by 60%. Zuckerberg changed his mind, and now the company is stuck with low morale and culture turned mercenary. ## Why AI Transformations May Repeat the Failures of Earlier Digital and Agile Transformations DevFeed: [Why AI Transformations May Repeat the Failures of Earlier Digital and Agile Transformations]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-08-27T14:03:51Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [Artificial Intelligence](), [Agile](), [digital]() Tags: [agile](), [ai](), [alignment](), [collaboration](), [culture](), [strategy](), [teams](), [transformation]() ### AI overview This opinion article argues that many AI transformation efforts may repeat the failures of earlier digital and Agile transformations. It attributes these failures to organizations neglecting foundational issues such as culture, strategy, alignment, collaboration, and clarity, and argues that AI can amplify existing confusion and organizational problems. ### Source excerpt Different approach, same outcomes. ## How Redis Builds AI-Native Engineering Teams DevFeed: [How Redis Builds AI-Native Engineering Teams]() Original publisher: [Read original article]() Author: Gregor Ojstersek Published: 2026-08-27T12:35:46Z Content type: opinion Language: en Sources: [Engineering Leadership]() Topics: [Redis](), [Artificial Intelligence]() Tags: [ai](), [engineering](), [insights](), [redis](), [teams]() ### AI overview The article presents insights from a conversation with Redis Distinguished Engineer Eric Sammer about how Redis builds AI-native engineering teams. The supplied evidence does not provide specific practices or findings beyond that subject. ### Source excerpt Insights from my conversation with Eric Sammer, Distinguished Engineer at Redis. ## Using Data Contracts to Coordinate Data Evolution at Enterprise Scale DevFeed: [Using Data Contracts to Coordinate Data Evolution at Enterprise Scale]() Original publisher: [Read original article]() Author: Keerthipriyan Published: 2026-08-25T20:22:17Z Content type: article Language: en Sources: [Walmart Global Tech]() Topics: [data-platforms](), [Data Quality](), [DataOps](), [systems]() Tags: [architecture](), [data](), [data-engineering](), [data-governance](), [data-platforms](), [data-quality](), [data-science](), [enterprise](), [organizational](), [pipeline](), [schema](), [software-engineering](), [teams](), [technology]() ### AI overview The article explains how data contracts help large enterprises coordinate changes across independently evolving data teams and downstream consumers. It argues that schema validation alone cannot identify ownership, downstream impact, or migration responsibilities, and presents data contracts as machine-enforceable coordination agreements. ### Source excerpt Coauthored by Satyajeet Coordinating Data Evolution at Enterprise Scale When you operate data platforms on a global enterprise scale, hundreds of engineering teams ship improvements every week, each moving independently to deliver value at the pace of the business demands. This velocity is a competitive advantage. The challenge: How do you enable hundreds of teams to evolve their data products independently while maintaining reliability for thousands of downstream consumers? Traditional coordination methods (messages, wiki updates, shared spreadsheets) work at small scale but break at Walmart scale. A source team ships an enhancement, perfectly valid within their domain, but that change ripples through fifteen downstream pipelines owned by different teams with different release schedules. Without a formal coordination mechanism, you discover the impact after it reaches production. The gap isn't technical debt or fragile systems. It's the absence of machine-enforceable agreements that scale with organizational complexity. Data contracts solve this: enabling teams to move fast independently while maintaining coordinated reliability across organizational boundaries. Here's the architecture we built. Why Schema Validation Alone Isn't Enough When data quality issues surface in production, the first instinct is often added to more schema validation. If a field is missing or has the wrong type, the pipeline catches it. This works for many data quality problems, but not all of them. Consider a scenario where a source team enhances their data model by restructuring field names to support new business capabilities. The schema still validates perfectly: every field exists; every type is correct; the data is well formed. But downstream consumers who depend on the original field names now receive empty results. Schema validation checks whether data has the right shape. It tells you that a field is missing. It does not tell you who owns that field, which downstream teams will bre ## Automated Incident Response: Nobody Should Be the Scribe DevFeed: [Automated Incident Response: Nobody Should Be the Scribe]() Original publisher: [Read original article]() Author: Ryan Taylor Published: 2026-08-25T00:00:00Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [incident](), [Incident response](), [Artificial Intelligence](), [Slack](), [Python](), [Shell]() Tags: [agent](), [ai](), [automated-incident-response](), [blog](), [harness](), [incident](), [incident-response](), [jira](), [product](), [python](), [shell-script](), [slack](), [teams]() ### AI overview A Harness product lead argues that incident teams should not rely on a human scribe. The article describes automated runbooks that create communication channels, video bridges, and tickets, while an AI Scribe Agent captures incident events and produces timelines, postmortems, and synchronized action items. ### Source excerpt A Harness product lead on why humans shouldn't be the status-tracking layer during incidents, and how automated summaries and postmortems replace the scribe. | Blog ## Harness RT Agents Detect Resilience Risks and Generate Tests for CD Pipelines and Kubernetes Workloads DevFeed: [Harness RT Agents Detect Resilience Risks and Generate Tests for CD Pipelines and Kubernetes Workloads]() Original publisher: [Read original article]() Author: Uma Mukkara Published: 2026-08-24T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [Resilience](), [Testing](), [Continuous Delivery (CD)](), [Kubernetes](), [dashboards]() Tags: [agentic](), [agents](), [blog](), [chaos](), [configuration](), [continuous-delivery](), [dashboards](), [deployment](), [experiments](), [infrastructure](), [insights](), [kubernetes](), [load](), [pipelines](), [product](), [resilience](), [services](), [teams](), [testing](), [tests]() ### AI overview Harness announces an update to Resilience Testing called RT Agents. The agents analyze CD pipelines and Kubernetes workloads for resilience risks, recommend the testing needed to confirm those risks, and can generate and run chaos experiments or load tests and interpret the results. ### Source excerpt RT Agents detect resilience risk in your CD pipelines and Kubernetes workloads, then generate and run chaos experiments or load tests to confirm it. | Blog ## How Shutterstock Builds AI-Native Engineering Teams DevFeed: [How Shutterstock Builds AI-Native Engineering Teams]() Original publisher: [Read original article]() Author: Gregor Ojstersek Published: 2026-08-20T15:08:01Z Content type: opinion Language: en Sources: [Engineering Leadership]() Topics: [Artificial Intelligence]() Tags: [ai](), [engineering](), [insights](), [teams]() ### AI overview The article presents insights from a conversation with Jefferson Frazer, Shutterstock's Director of AI Metadata and Delivery, about how Shutterstock builds AI-native engineering teams. ### Source excerpt Insights from my conversation with Jefferson Frazer, Director, AI Metadata and Delivery, Shutterstock. ## Leadership Council September 2026 Representative Selections DevFeed: [Leadership Council September 2026 Representative Selections]() Original publisher: [Read original article]() Author: Jakub Beránek Published: 2026-08-18T00:00:00Z Content type: release Language: en Sources: [Inside Rust Blog]() Topics: [Rust]() Tags: [2026](), [leadership](), [rust](), [september-2026](), [teams]() ### AI overview The Rust Leadership Council representative selection process is beginning for the Infra, Lang, Libs, and Mods teams. Teams are expected to confirm representatives by September 23, 2026, with new members joining council meetings from September 25. ### Source excerpt The selection process for representatives on the Leadership Council is starting today. Every six months, half of the council terms end. The following teams are up to choose their representative for the next year: Infra Lang Libs Mods We are aiming to have the teams confirm their choices by September 23, 2026, and for any possible new members to be ready to join the council meetings starting September 25th. Criteria for representatives Any member of the top-level team or a member of any of their subteams is eligible to be the representative. See candidate criteria for a description of what makes a good representative. There is a limit of at most two people affiliated with the same company or other legal entity being on the council 1. During the selection process, the council will consider the affiliation of candidates to decide if all choices will be compatible with that constraint. Representatives may serve multiple terms if the team decides to choose the same representative again. There is a soft limit of three terms.2 It is recommended that teams rotate their representatives if possible to help avoid burnout and to spread the experience to a broader group of people. What do Representatives do? A representative provides a voice on the council to represent the interests of their teams and contribute to the long-term success of the Rust Project. A detailed description of the role may be found at the Representative Role Description. How should teams make their selection? The Leadership Council has put together a Representative Selection Guide with recommendations for teams on how to go about choosing a representative. It is not a requirement that teams follow this guide; top-level teams may choose their own process. See Limits on representatives from a single company/entity ↩ See Term limits. ↩ ## AI Enables Software Experiences Tailored to Roles, Teams, and Work DevFeed: [AI Enables Software Experiences Tailored to Roles, Teams, and Work]() Original publisher: [Read original article]() Author: Ben Haefele Published: 2026-08-18T00:00:00Z Content type: opinion Language: en Sources: [Webflow Blog]() Topics: [Artificial Intelligence](), [Software](), [App]() Tags: [ai](), [app](), [inside-webflow](), [interface](), [software](), [teams](), [work]() ### AI overview The article argues that standardized, one-size-fits-all software interfaces are limited because users and workflows vary. Drawing on HyperCard and the analogy of tailored clothing, it suggests that AI can make software experiences more adaptable to different roles, teams, and work. ### Source excerpt One fixed interface is no longer the default. AI makes it possible to shape the experience around the role, the team, and the work at hand. ## Hiring, tools, overload, automation: one cost wearing four names DevFeed: [Hiring, tools, overload, automation: one cost wearing four names]() Original publisher: [Read original article]() Author: The Team @ Giant Swarm Published: 2026-08-14T14:19:01Z Content type: opinion Language: en Sources: [Giant Swarm Blog]() Topics: [Kubernetes](), [Open Source](), [Tool](), [Automation]() Tags: [automation](), [cost](), [engineering](), [enterprise](), [kubernetes](), [open-source](), [operational](), [platform-engineering](), [teams](), [tools]() ### AI overview The article argues that hiring difficulties, tool overload, operational pressure, and insufficient automation are connected manifestations of a platform integration tax. It describes the cost of assembling and operating production platforms from open-source components, including time, specialist staffing, and enterprise operating expense. ### Source excerpt Four challenges platform teams keep naming turn out to be one cost: the platform integration tax. Giant Swarm's framework for where to start. ## How to Prevent RBAC Role Explosion with Nested Access Lists DevFeed: [How to Prevent RBAC Role Explosion with Nested Access Lists]() Original publisher: [Read original article]() Author: info@goteleport.com (Paul Curtis) Published: 2026-08-13T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [Access Control](), [Authorization](), [Development]() Tags: [access-control](), [development](), [how-to](), [permissions](), [teams]() ### AI overview This tutorial explains RBAC role explosion, how repeated role changes create inflexible and duplicated roles, and how nested access lists can apply inherited permissions while keeping roles fixed. ### Source excerpt Learn how to use access lists to prevent RBAC role explosion. ## Agile Leadership DevFeed: [Agile Leadership]() Original publisher: [Read original article]() Author: Dave Ogle Published: 2026-08-10T00:00:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [Agile](), [Self-organizing Team](), [trust](), [Development]() Tags: [agile](), [delegation](), [delivery](), [frameworks](), [leadership](), [self-organizing-teams](), [software-development](), [teams](), [trust]() ### AI overview This opinion argues that organisational agility depends on leadership as well as agile frameworks and processes. It highlights empowerment, trust and delegation, and examines Agile2 and the British Army's Mission Command philosophy as relevant perspectives. ### Source excerpt Agile teams are built on more than frameworks and processes. This article explores why effective leadership, trust and delegation are fundamental to true organisational agility, drawing lessons from the British Army's Mission Command philosophy. ## Why Readiness Should Be a Habit, Not a Final Gate DevFeed: [Why Readiness Should Be a Habit, Not a Final Gate]() Original publisher: [Read original article]() Author: Dean Hume Published: 2026-08-03T08:41:00Z Content type: opinion Language: en Sources: [Dean Hume]() Topics: [Testing](), [Software](), [systems](), [bug](), [Network](), [API]() Tags: [api](), [bug](), [game-development](), [habits](), [launches](), [network](), [quality](), [teams](), [technical-leadership](), [technical-program-manager](), [testing]() ### AI overview The article argues that software quality is established through everyday team habits rather than a final testing phase. It emphasizes defining expected behavior for interruptions and failures early, instead of labeling unspecified behavior as bugs during release pressure. ### Source excerpt Quality isn't decided in the final review. The teams with the smoothest launches built it through daily habits, long before anyone thought about a release date. ## How building software is changing at Anthropic DevFeed: [How building software is changing at Anthropic]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-07-28T15:49:30Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [anthropic](), [Artificial Intelligence](), [Code review](), [Testing](), [engineering-culture]() Tags: [ai](), [anthropic](), [code-review](), [teams](), [testing]() ### AI overview This article examines changes in how Anthropic builds software, including increased use of AI for code review and testing and the continued use of two-pizza teams. ### Source excerpt A deepdive on what's changed in how the leading AI lab makes software. Ever more code review and testing is done by AI, two-pizza teams very much alive, and more. Details from inside of Anthropic ## What Interviewers Listen for in Behavioral Interviews DevFeed: [What Interviewers Listen for in Behavioral Interviews]() Original publisher: [Read original article]() Author: Stephane Moreau Published: 2026-07-19T11:12:27Z Content type: tutorial Language: en Sources: [Blog for Engineering Managers]() Topics: [Job](), [incident](), [service](), [connection pool]() Tags: [connection-pool](), [engineering](), [guide](), [how-to](), [incident](), [influence](), [interviews](), [junior](), [scope](), [service](), [teams]() ### AI overview This article explains how interviewers assess behavioral answers, emphasizing the scope of ambiguity, influence across teams, trade-offs, and whether candidates actively discovered problems. It argues that these signals can distinguish mid-level engineers from Staff-level engineers before the stated outcome. ### Source excerpt What I'm actually listening for when you answer. ## When the bug slips through: How we built an AI feedback loop to strengthen our safety net DevFeed: [When the bug slips through: How we built an AI feedback loop to strengthen our safety net]() Original publisher: [Read original article]() Author: Vasilescu Andreea Published: 2026-07-14T07:16:00Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [Artificial Intelligence](), [bug](), [incident](), [Monitoring](), [Software](), [data](), [Azure]() Tags: [ai](), [analysis](), [artificial-intelligence](), [automation](), [bug](), [data-science](), [engineering](), [errors](), [incident](), [incident-management](), [issue](), [llm](), [monitoring](), [outage](), [production](), [software](), [teams]() ### AI overview The article describes how a large-scale engineering organization built an AI feedback loop to investigate production regressions, compare them with pre-production alerts, and identify gaps in its quality platform. It explains that manual incident sampling was valuable but covered less than 10 percent of incidents, making broader forensic analysis impractical at Azure scale. ### Source excerpt Figure 1: When the bug slips through, or how we built an AI feedback loop to strengthen our safety net. A regression reached production last fall. Not dramatically, no war-room scramble, no cascading outage. It showed up as a cluster of customer-facing errors, traced back through monitoring data to a change that had passed through every pre-production check without triggering a single alert. Somewhere upstream, the safety net had a hole that nobody knew was there. This scenario is not hypothetical. It happens. And when it does, the instinctive response is the same: investigate the incident, fix the issue, and then ask the uncomfortable follow-up question, Why didn't we catch this before it reached production? Answering that question is deceptively hard. A pre-production quality platform generates a constant stream of signals, warnings, and anomalies, many of them noisy, overlapping, or ultimately irrelevant. The regression in question might be buried among production incidents, false signals, and failures the platform was never designed to catch in the first place. While manual sampling and investigation can provide meaningful insight, understanding what was genuinely missed, why it was missed, and what needs to change at platform scale requires the kind of careful forensic analysis that is slow, expert-intensive, and -- at the scale of Azure -- practically impossible to do by hand. This is the problem we set out to solve. A safety net with blind spots Modern large-scale engineering organizations invest heavily in pre-production quality platforms, systems that evaluate software changes before they reach customers. These are systems designed to catch regressions before any code change reaches customers, running controlled experiments, executing targeted tests, monitoring key signals, and alerting engineering teams when something looks wrong. But a safety net is only as good as its coverage. And knowing how good your coverage actually is requires a feedback mechanism: a ## 5 takeaways from the State of Software Delivery Q2 Pulse report DevFeed: [5 takeaways from the State of Software Delivery Q2 Pulse report]() Original publisher: [Read original article]() Author: Jacob Schmitt Published: 2026-07-08T13:00:00Z Content type: release Language: en Sources: [The CircleCI Blog Feed | CircleCI]() Topics: [Software](), [Development](), [Artificial Intelligence]() Tags: [ci-cd-benchmarks](), [circleci-news](), [cost](), [developer-productivity](), [efficiency](), [merge-efficiency-ratio](), [popular](), [report](), [software-delivery](), [software-delivery-metrics](), [software-delivery-pulse](), [software-delivery-report](), [state-of-software-delivery](), [teams](), [velocity](), [workflows]() ### AI overview CircleCI releases its 2026 State of Software Delivery Q2 Pulse report, based on more than 20 million workflows analyzed in March 2026. The report finds that the gap between high-performing teams and the median team widened, while feature-branch throughput increased and main-branch throughput remained flat. ### Source excerpt The velocity gap is widening. See what 20M+ workflows reveal about merge efficiency, delivery costs, and the teams pulling ahead in the Q2 Pulse report. [Next page]()