# velocity Published articles for velocity. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Engineering Principles Can Help You Scale DevFeed: [How Engineering Principles Can Help You Scale]() Original publisher: [Read original article]() Author: Khan Academy Published: 2019-08-21T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [Development](), [Code](), [Processes]() Tags: [architecture](), [communication](), [decision-making](), [engineering](), [growth](), [news](), [process](), [professional-development](), [scale](), [velocity]() ### AI overview Marta Kosarchyn describes how Khan Academy's growing engineering team outgrew its initial principles. The article argues that evolving engineering principles can support scaling while preserving culture and engagement, and explains how the original approach eventually contributed to slower delivery, code clutter, and implicit architectural decisions. ### Source excerpt By Marta Kosarchyn Our engineering team has grown a lot over the past couple of years, and we're ... Read more ## Internal Developer Portals: Why Native CI/CD Drives Scale DevFeed: [Internal Developer Portals: Why Native CI/CD Drives Scale]() Original publisher: [Read original article]() Author: Rashmi Hegde Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Platform Engineering](), [CI/CD](), [developer velocity](), [Automation](), [Scalability]() Tags: [automation](), [ci-cd](), [developer-velocity](), [idp](), [scalability](), [self-service](), [velocity]() ### AI overview This article argues that internal developer portals need native CI/CD integration to move beyond service catalogs and documentation. It explains that integrated pipelines can reduce context switching, support self-service workflows, help enforce standards, and improve developer velocity and scalability. ### Source excerpt Internal developer portals need native CI/CD integration to scale effectively. Learn how integrated pipelines improve velocity. Explore Harness IDP. | Blog ## The State of AI Impact in Engineering DevFeed: [The State of AI Impact in Engineering]() Original publisher: [Read original article]() Author: Luca Rossi Published: 2026-07-22T12:06:40Z Content type: article Language: en Sources: [Refactoring]() Topics: [Artificial Intelligence](), [data]() Tags: [ai](), [ai-impact](), [data](), [engineering](), [velocity]() ### AI overview The article examines data from more than 500 teams to explore what it reveals about velocity gains associated with AI in engineering. ### Source excerpt What data from 500+ teams is revealing about velocity gains. ## Why teams can create more impact by stopping work estimates DevFeed: [Why teams can create more impact by stopping work estimates]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-07-09T12:45:21Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [Development](), [sessions]() Tags: [development](), [scrum](), [sessions](), [story-points](), [velocity]() ### AI overview The article argues that estimates, story points, velocity, and capacity planning can create false expectations and constrain teams. It describes an experiment removing estimates from a refined backlog to encourage smoother collaboration and greater focus on value. ### Source excerpt How teams maximize impact without wasting energy sizing work. ## Software Engineering in the Probabilistic Era of LLM-Assisted Development DevFeed: [Software Engineering in the Probabilistic Era of LLM-Assisted Development]() Original publisher: [Read original article]() Author: Mike Yerou Published: 2026-07-09T12:35:59Z Content type: opinion Language: en Sources: [Rock and Null]() Topics: [Software Engineering](), [Large Language Model](), [Artificial Intelligence](), [agentic workflows](), [AI Development]() Tags: [agentic](), [ai](), [artificial-intelligence](), [llms](), [quality](), [software-engineering](), [velocity](), [workflows]() ### AI overview The article argues that LLMs and agentic workflows are making software development less deterministic. It proposes managing uncertainty through verification, failure recovery, feedback loops, and processes that preserve both software quality and development velocity. ### Source excerpt Software engineering has entered a probabilistic era. As LLMs replace deterministic workflows with uncertainty, the challenge is no longer eliminating variability; it's building systems that deliver both high quality and high development velocity despite it. ## 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. ## AI Coding Tip 021 - Avoid Comprehension Debt DevFeed: [AI Coding Tip 021 - Avoid Comprehension Debt]() Original publisher: [Read original article]() Author: Maxi Contieri Published: 2026-05-23T15:24:29Z Content type: tutorial Language: en Sources: [Maximiliano Contieri - Software Design]() Topics: [ai-coding](), [coding](), [Learning]() Tags: [ai](), [ai-coding](), [audit](), [code](), [coding](), [debug](), [implement](), [onboarding](), [production](), [quality](), [team](), [technical](), [tests](), [velocity]() ### AI overview This tutorial explains how merging AI-generated code without understanding it creates comprehension debt: a growing gap between the codebase and what the team can genuinely explain and maintain. It recommends requiring explanations, documenting AI-made architectural decisions, testing understanding without the AI chat, and adding comprehension checks to pull requests. ### Source excerpt TL;DR: Merging code you don't understand creates comprehension debt that compounds until your team can no longer maintain it. Common Mistake ❌ You ask the AI to implement a feature. The code looks cl ## What Happened When We Treated AI Like an Engineering Teammate DevFeed: [What Happened When We Treated AI Like an Engineering Teammate]() Original publisher: [Read original article]() Author: Global Technology Published: 2026-04-28T13:23:51Z Content type: opinion Language: en Sources: [McDonald's Technical Blog - Medium]() Topics: [Artificial Intelligence](), [AI Engineering](), [maintenance](), [Security](), [Cloud](), [test-coverage](), [Documentation](), [Framework]() Tags: [agent](), [ai](), [ai-agent](), [ai-engineering](), [autonomous](), [cloud](), [development](), [documentation](), [maintenance](), [platform-engineering](), [security](), [software-engineering](), [test-coverage](), [the-result](), [velocity]() ### AI overview The Restaurant Topology Management team used an AI-powered, agent-based engineering assistant within its development workflows to handle repetitive maintenance work, including configuration updates, security patches, framework modernization, test coverage, and documentation. The article reports 20% faster sprint velocity, security updates completed twice as fast, and modernization delivered up to 10 times faster. ### Source excerpt How an AI engineering assistant boosted sprint velocity by 20% and modernized services up to 10x faster. by: Sumedha Shenoy, Director, Engineering Tech Lead, and Kamal Jackson, Sr Manager, Engineering Tech Lead Quick Bytes: A growing backlog of essential but repetitive "glue work" was slowing the Restaurant Topology Management (RTM) team's ability to modernize and deliver new features By treating an AI engineering assistant as an autonomous teammate -- not a chatbot -- the team offloaded end-to-end maintenance tasks at scale The result was 20% faster sprint velocity, security updates completed twice as fast, and modernization work delivered up to 10x quicker without sacrificing quality In fast-moving software environments, critical but repetitive "glue work" like configuration updates, security patches, and documentation often compete with time that could be spent delivering new features. For the Restaurant Topology Management (RTM) team -- who build and maintain the cloud-based platform that enables markets to create and deploy consistent restaurant device configurations -- this maintenance workload had grown quietly over time and began to limit the team's ability to innovate. To regain momentum and address mounting operational debt, the team turned to an AI-powered, agent-based engineering assistant embedded into our standard development workflows, capable of handling complex, repetitive tasks. The challenge: The "un-fun" backlog Before piloting the AI assistant, the RTM team was managing maintenance tasks that had persisted across multiple sprints: Security updates: Enhancing automated scanning workflows to maintain a strong security posture Framework modernization: Updating services to align with the team's current application framework Test coverage improvements: Strengthening unit test consistency across services Documentation updates: Refreshing internal service documentation to support smoother onboarding and troubleshooting This maintenance work was important b ## How to Reduce Payment Declines and Recover More Revenue DevFeed: [How to Reduce Payment Declines and Recover More Revenue]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-13T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [Transactions]() Tags: [churn](), [customer](), [fraud](), [how-to](), [implement](), [payments](), [recovery](), [reduce](), [retry](), [revenue](), [saas](), [strategy](), [subscription](), [timeout](), [velocity]() ### AI overview This guide explains how payment declines affect subscription businesses, including their contribution to involuntary churn. It distinguishes soft declines from hard declines and outlines strategies such as smart retry logic to recover recurring revenue. ### Source excerpt Payment declines silently drain SaaS revenue. Learn 12 proven strategies to reduce false declines, retry failed payments, and recover lost recurring revenue. ## The tech leader's mandate: Use engineering to accelerate sales velocity DevFeed: [The tech leader's mandate: Use engineering to accelerate sales velocity]() Original publisher: [Read original article]() Published: 2026-02-04T00:00:00Z Content type: opinion Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard](), [Security](), [supply-chain-security](), [Software Engineering](), [Development]() Tags: [chainguard](), [chainguard-containers](), [chainguard-for-startups](), [chainguard-libraries](), [chainguard-startups-pricing](), [container-images](), [containers](), [packages](), [sales](), [sboms](), [secure-by-default](), [security](), [security-compliance](), [software-supply-chain](), [velocity]() ### AI overview This article argues that security and compliance concerns can slow enterprise sales for startups. It recommends embedding security into the software development lifecycle and presents Chainguard's secure-by-default components as a way to reduce software supply chain friction while supporting product development and sales velocity. ### Source excerpt Chainguard can help your engineering team accelerate sales velocity by reducing security friction and enabling engineering to be a growth accelerator. ## In praise of the stacked pull request DevFeed: [In praise of the stacked pull request]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-02-04T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [stacked pull requests](), [Code review](), [Code quality](), [Development](), [Git]() Tags: [code-quality](), [code-review](), [git](), [merge](), [pull-request](), [queue](), [soc2](), [stacked-pull-requests](), [team](), [velocity]() ### AI overview An opinion piece argues that stacked pull requests help teams work on larger or dependent changes without creating oversized, difficult-to-review pull requests. It presents small, independent, testable pull requests as units of work that can be reviewed and merged incrementally. ### Source excerpt How fast you do code review is how fast you do everything. Your PR queue is the leading indicator of team velocity. Stacked PRs can help. ## How AI is Changing the Way FanCode Builds, Tests and Ships DevFeed: [How AI is Changing the Way FanCode Builds, Tests and Ships]() Original publisher: [Read original article]() Author: Dream Blog Published: 2026-01-21T12:17:03Z Content type: article Language: en Sources: [Dream11 Engineering]() Topics: [Artificial Intelligence](), [Development](), [pull-requests](), [Testing](), [Latency]() Tags: [ai](), [deployment](), [development](), [engineering](), [latency](), [pull-requests](), [software-engineering](), [sports](), [tech](), [testing](), [velocity]() ### AI overview FanCode describes how it has integrated AI into engineers' daily development workflows, including building, reviewing, testing, and shipping software for live sports. The article links this work to faster delivery and more reliable systems under load, while describing review delays and other growing pains in a lean engineering team. ### Source excerpt By Guruwinder Rishi and Pramod Kumar At FanCode, engineers work on problems that show up live -- in front of millions of fans -- and shape how sports are experienced in real time. If building, reviewing, testing, and shipping at scale excites you, we'd love to talk. Check out open roles at FanCode. At FanCode, engineering efficiency isn't an internal KPI. It's the difference between a fan watching a last-over finish without interruption and a stream dropping at the worst possible moment. When you're building for live sports, the margin for error is near zero. We stream 10,000+ live games every year, across sports, formats, and devices -- often with millions of fans watching at the same time. At that scale, every commit, every review delay, and every flaky test eventually shows up somewhere: in latency, stability, or how confident we feel pushing a change while a match is live. Behind the scale we operate is a lean 50 member engineering team across backend, frontend, and testing -- and that's by design. The problem we've been solving for years isn't scale through headcount. It's scale through clarity, velocity, and trust in our systems, while still giving engineers the space to learn, iterate, and do some fantastic work. We've been using AI across FanCode for a while now. But what's changed over the last year is how deeply we've started integrating it into the everyday lives of engineers. This blog outlines the changes we've made to our development workflows, how we used AI, and the results we saw in terms of faster delivery and more reliable systems under load. With Scale, the Cracks That Started to Show As FanCode scaled, we began facing the classic growing pains of a fast-moving, high-output tech team. Inefficiencies that were manageable earlier started compounding quickly, and a few patterns became impossible to ignore. I. The Review Bottleneck: Pull requests routinely took 2-3 days to move from "raised" to "merged". On their own, such delays didn't seem alarming. Bu ## PaaS + IaaS: Heroku and AWS for Cloud-Native Enterprise Agility DevFeed: [PaaS + IaaS: Heroku and AWS for Cloud-Native Enterprise Agility]() Original publisher: [Read original article]() Author: Gillian Gual Hinkle Published: 2025-12-04T19:50:28Z Content type: article Language: en Sources: [Heroku]() Topics: [Heroku](), [Amazon Web Services](), [Platform as a Service (PaaS)](), [Infrastructure as a Service (IaaS)](), [Cloud](), [Development]() Tags: [applications](), [aws](), [cloud](), [cloud-infrastructure](), [cloud-native](), [deploy](), [development](), [engineering](), [heroku](), [heroku-enterprise](), [infrastructure-as-a-service-iaas](), [maintenance](), [platform-as-a-service-paas](), [providers](), [scale](), [services](), [velocity]() ### AI overview The article explains how enterprises can combine Heroku's managed PaaS with AWS infrastructure to build and operate cloud-native applications. It highlights reduced operational burden, greater developer focus, and a Salesforce example involving applications used by more than 76,000 employees. ### Source excerpt For modern enterprises building cloud-native applications, success hinges on achieving maximum development velocity at every scale. Infrastructure as a Service (IaaS) providers like AWS provide hundreds of services with the unmatched reliability and scale needed for enterprise infrastructure, but they can require significant effort and expertise for organizations to be effective and efficient. To achieve [...] The post PaaS + IaaS: Heroku and AWS for Cloud-Native Enterprise Agility appeared first on Heroku. ## Accelerating Platform Adoption with Developer Trust DevFeed: [Accelerating Platform Adoption with Developer Trust]() Original publisher: [Read original article]() Published: 2025-11-06T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Developer Platform](), [code productivity](), [cloud-infrastructure](), [developer-productivity](), [software-development]() Tags: [chainguard-adoption-story](), [chainguard-containers](), [chainguard-images](), [chainguard-libraries](), [cloud-native](), [container](), [container-images](), [developer](), [golden-images](), [governance](), [internal-developer-platform](), [platform](), [site-reliability](), [velocity]() ### AI overview The article argues that internal development platforms need developer trust to achieve adoption. Platform teams should provide secure, standardized, and customizable paved paths that support diverse developer needs, including different programming languages and versions. It highlights the limitations of one-size-fits-most golden container images and the importance of balancing flexibility, governance, productivity, engineering velocity, and operational reliability. ### Source excerpt Chainguard helps Platform teams drive adoption with zero-CVE, customizable container images that make internal development platforms secure, fast, and trusted. ## How PayFit used Nx to revive its monorepo and improve development velocity DevFeed: [How PayFit used Nx to revive its monorepo and improve development velocity]() Original publisher: [Read original article]() Author: Juri Strumpflohner Published: 2025-07-24T00:00:00Z Content type: article Language: en Sources: [Juri Strumpflohner]() Topics: [monorepo](), [CI/CD](), [Development](), [Tooling](), [Deployment](), [Software as a service]() Tags: [ci-cd](), [company](), [customer-story](), [deployment](), [development](), [efficiency](), [europe](), [fragmentation](), [monorepo](), [nx](), [speed](), [startup](), [tooling](), [velocity]() ### AI overview This case study describes how PayFit's four-person platform team addressed monorepo abandonment, fragmented repositories, deployment complexity, and CI/CD inefficiencies by improving monorepo health with Nx and focusing on execution and support. ### Source excerpt Discover how a 4-person platform team at Payfit turned monorepo abandonment into a competitive advantage using strategic Nx implementation. ## Why We Believe in In-Person Onboarding at Teleport DevFeed: [Why We Believe in In-Person Onboarding at Teleport]() Original publisher: [Read original article]() Author: info@goteleport.com (Amanda Pessica) Published: 2025-07-10T00:00:00Z Content type: opinion Language: en Sources: [Teleport]() Topics: [trust](), [Job]() Tags: [3](), [behavior](), [ceo](), [culture](), [daily](), [distributed](), [money](), [new-job](), [onboarding](), [people](), [remote](), [screen](), [slack](), [team](), [travel](), [trust](), [velocity](), [zoom]() ### AI overview Teleport explains why it brings new hires together in person for onboarding despite the costs and logistics. The company says the three-day program helps employees connect, build trust, understand its culture, and ramp up faster. ### Source excerpt In a remote-first world, in-person onboarding is still invaluable. At Teleport, we see it as a strategic investment in connection, trust, and team velocity. ## Upsides and Downsides DevFeed: [Upsides and Downsides]() Original publisher: [Read original article]() Author: Calvin French-Owen Published: 2025-02-27T00:00:00Z Content type: opinion Language: en Sources: [Calvin French-Owen]() Topics: [Requirements](), [Security](), [audit](), [Latency](), [Artificial Intelligence]() Tags: [audit](), [churn](), [latency](), [requirements](), [risk](), [security](), [startups](), [velocity]() ### AI overview This opinion article presents a framework for understanding how startup priorities change as a company matures. Early-stage startups benefit from pursuing high-upside opportunities and accepting variance, while later-stage companies must reduce downside risks such as security issues, access-control gaps, audit requirements, uptime problems, and cost or performance concerns. The article also argues that this shift helps explain why AI demos often fail to become strong products. ### Source excerpt When to shift mentality from upsides to downsides. And why most AI demos don't make great products. ## Measure deployment velocity with Vercel and Tinybird DevFeed: [Measure deployment velocity with Vercel and Tinybird]() Original publisher: [Read original article]() Author: Alasdair Brown Published: 2025-01-13T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [Deployment](), [Vercel](), [Next.js]() Tags: [app](), [deployment](), [how-to](), [i-built-this](), [next-js](), [velocity](), [vercel](), [webhooks]() ### AI overview A tutorial on using Vercel webhooks, Tinybird, and Next.js to build a simple app that tracks deployment velocity. ### Source excerpt Learn how to use Vercel webhooks, Tinybird, and Next.js to build a simple app that tracks your deployment velocity. ## Securing the Digital Future: AppSec Best Practices in Digital Banking DevFeed: [Securing the Digital Future: AppSec Best Practices in Digital Banking]() Original publisher: [Read original article]() Author: Hiroko Nakano Published: 2025-01-06T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Application Security](), [Security](), [vulnerability management](), [Vulnerabilities](), [Development](), [Architecture & Design]() Tags: [application-security](), [awareness](), [blog](), [collaboration](), [community](), [complexity](), [compliance](), [data](), [developers](), [development](), [devops](), [devsecops](), [executive](), [external](), [financial-services](), [legacy](), [modernization](), [security](), [snyk](), [third-party](), [velocity](), [vulnerability-management]() ### AI overview This article summarizes a presentation on application security in digital banking. It discusses regulatory compliance, third-party integrations, sophisticated attackers, complex and legacy systems, limited security resources, insider threats, release velocity, and secure customer experiences. It also emphasizes collaboration, shared context, and business-aware vulnerability prioritization between developers and security teams. ### Source excerpt Application security is critical to safeguarding customer data and maintaining trust. Explore best practices for AppSec, including secure coding, vulnerability management, and integrating security into continuous development cycles. Protect your digital services and stay resilient against evolving threats, ensuring a secure future for your customers and business. ## Security, Maintainability, Velocity: Choose One DevFeed: [Security, Maintainability, Velocity: Choose One]() Original publisher: [Read original article]() Published: 2024-04-17T17:41:24Z Content type: opinion Language: en Sources: [Brave New Geek]() Topics: [Security](), [Development](), [Software Engineering](), [software-development]() Tags: [breach](), [cloud](), [development](), [governance](), [idp](), [internal-developer-platform](), [konfigurate](), [maintainability](), [platform-engineering](), [product-development](), [ransomware](), [security](), [software-development](), [software-development-triangle](), [software-engineering](), [tech-debt](), [velocity]() ### AI overview The article presents security, maintainability, and development velocity as competing priorities in software development. It argues that organizations often treat these priorities as a "choose one" trade-off, while suggesting that deliberate planning can help achieve all three. ### Source excerpt There are three competing priorities that companies have as it relates to software development: security, maintainability, and velocity. I'll elaborate on what I mean by each of these in just a bit. When I originally started thinking about this, I thought of it in the context of the "good, fast, cheap: choose two" project management triangle. But after thinking about it for more than a couple minutes, and as I related it to my own experience and observations at other companies, I realized that in practice it's much worse. For most organizations building software, it's more like security, maintainability, velocity: choose one. ## Evolution of Developer Productivity at Square - Part Two DevFeed: [Evolution of Developer Productivity at Square - Part Two]() Original publisher: [Read original article]() Author: Korhan Bircan Published: 2024-01-29T09:00:00Z Content type: article Language: en Sources: [Square Corner Blog RSS Feed]() Topics: [code productivity](), [build times](), [Android](), [remote-development](), [Continuous integration](), [Optimization](), [Caching](), [Gradle](), [Amazon EC2](), [ide](), [benchmarking]() Tags: [android](), [benchmarking](), [build-times](), [caching](), [cloud](), [continuous-integration](), [developer-productivity](), [development](), [engineering](), [gradle](), [ide](), [optimization](), [remote-development](), [velocity]() ### AI overview Square describes efforts to improve developer productivity and development velocity by reducing build, installation, IDE synchronization, and continuous integration times. The article reports development apps, remote Android development environments, configuration caching, CI shard skipping, emulator optimization, Gradle changes, and modularization work. ### Source excerpt Accelerating development velocity ## Chainguard's image tagging philosophy: enabling high velocity updates (pt. 1 of 3) DevFeed: [Chainguard's image tagging philosophy: enabling high velocity updates (pt. 1 of 3)]() Original publisher: [Read original article]() Published: 2023-11-13T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard images](), [Vulnerabilities](), [releases]() Tags: [chainguard](), [chainguard-images](), [container-image](), [cve](), [distro](), [dockerfiles](), [github](), [image-tagging](), [series](), [software-security-practices](), [velocity]() ### AI overview Part 1 explains Chainguard's image-tagging philosophy for delivering fast updates while keeping image identities clear and usable. It describes epoch-based package revisions, vulnerability fixes, and how updates to packages within multi-package images can be delivered without changing the upstream version. ### Source excerpt Explore Part 1 of Chainguard's image tagging philosophy series, focusing on enabling high-velocity updates. ## Ways to Decrease Performance: Velocity Tracking DevFeed: [Ways to Decrease Performance: Velocity Tracking]() Original publisher: [Read original article]() Author: tobi@techunicorn.builders (Tobias Mende) Published: 2023-07-15T06:00:00Z Content type: opinion Language: en Sources: [Tobias Mende]() Topics: [Agile](), [Software Engineering]() Tags: [agile](), [article](), [critical](), [measuring](), [metrics-practices-ways-to-decrease-performance-developer-productivity-engineering-excellence](), [performance](), [practices](), [story-points](), [teams](), [velocity]() ### AI overview This opinion article examines velocity tracking and estimation in Agile software teams. It explains how teams use story points, velocity trends, and burndown charts, then begins discussing how these practices can harm performance, including the limitations of human estimation. ### Source excerpt Ways to Decrease Performance: Velocity Tracking This is the first article of my series, "Ways to Decrease Performance", which intends to trigger critical reflection on common "Agile" practices, that is, practices that I see many "agile" teams doing. ## Stepping Out Of The Knowledge Zone DevFeed: [Stepping Out Of The Knowledge Zone]() Original publisher: [Read original article]() Published: 2022-04-13T00:00:00Z Content type: opinion Language: en Sources: [Artsy]() Topics: [Elixir](), [Learning](), [Ruby](), [Rails]() Tags: [backend](), [blog](), [blog-post](), [cypress](), [elixir](), [forque](), [front-end](), [horizon](), [integrity](), [learning](), [ruby](), [ruby-on-rails](), [teams](), [velocity]() ### AI overview A first-person reflection on moving beyond familiar Ruby on Rails work and embracing the challenge of learning Elixir. The author connects this transition to Artsy shipping work and a broader willingness to remain open to being a beginner. ### Source excerpt As I am writing my first blog post for Artsy, here is a short introduction on who I am: My name is Kaja and I am an engineer in our Berlin entity. As a Ruby-born programmer I am calling myself a backend engineer, but that also may change and is more about emotional identification and less about what I actually do (as you will see in this blog post). My background is not in engineering at all. In university I graduated in philosophy and historical linguistics, both the most impractical but most beautifully theoretical subjects I can think of. I really love being in the world of ideas and thought experiments that challenge the current status of what is, as opposed to what is thinkable. At Artsy I am currently working in the PX (Partner Experience) Team and since I joined 1 year ago, most of my time in the PX team has been dedicated to the implementation of the Artsy Shipping feature. In the backend I helped tying the ties between the external ARTA-API (a white glove shipping company) and our own service that is managing the shipping and order statuses of the artworks that are ordered online. In the front end I implemented some forms for that feature and some so called hooks. I felt like a fish in the water with these tasks, because the company that I had worked at before was a shipping company and I did the same thing from the other side of the API relationship. In the same language (Ruby on Rails). Here is another fact about me that is a premises for what comes next: I enjoy to be a learner of new things much more than being an expert about things that I already know. Of course being an expert is also flattering the ego, but after a while it can feel repetitive and make your soul feel old and tired. While the experience of being new to something and not an expert at all can give you a rejuvenating prickle. On the other side it can be scary to admit not knowing something and also the ego will feel small and hurt. But I learned that overcoming the hurt ego is my way to [Next page]()