# developer velocity Developer velocity is a measure or broader concept for how quickly and effectively software development teams deliver valuable software, encompassing the systems, processes, tools, and organizational conditions that support developers. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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 ## Whatnot at Snowflake Summit 2026 DevFeed: [Whatnot at Snowflake Summit 2026]() Original publisher: [Read original article]() Author: Whatnot Engineering Published: 2026-08-12T17:54:54Z Content type: article Language: en Sources: [Whatnot Engineering]() Topics: [data](), [datasets](), [Artificial Intelligence](), [developer velocity]() Tags: [ai](), [ai-agent](), [analytics](), [data](), [data-science](), [datasets](), [developer-velocity](), [developers](), [machine-learning]() ### AI overview Whatnot's Data Platform team describes the data platform behind its live marketplace and its use of Snowflake Cortex AI and Semantic Views. The approach grounds AI in governed business definitions and trusted metadata so developers can find datasets and metrics and investigate issues using natural-language questions. ### Source excerpt Earlier this summer, several members of the Whatnot Data Platform team joined Snowflake on stage at Snowflake Summit to share how we're building the data platform behind Whatnot's live marketplace. This post covers what we talked about, why we built it, and future direction. Caption: The Whatnot team were everywhere at the Summit! Snowflake recently published a recap of our sessions, which you can read here: Observability at Scale: Whatnot at Snowflake Summit. Data at Whatnot Every live auction, bid, chat message, purchase, moderation action and recommendation produces data. That translates into billions of events each day powering recommendations, seller analytics, and customer support. For us, data isn't just for internal analysis. We use it to power the product while millions of people are buying and selling in real time. That creates an unique set of engineering challenges. As Whatnot has grown, we've had to evolve our platform to support thousands of datasets, hundreds of developers, and an increasingly diverse set of workloads while maintaining reliability, governance, and developer velocity. At Summit, we talked about the investments we have made to get engineers to vetted data faster Accelerating Builders with AI The Whatnot data platform is a living system. Schemas evolve, pipelines are deployed continuously, business definitions change, and new data products are published every day. Keeping up with that pace of change has become one of the biggest challenges for developers. In our first session, we shared how we're using Snowflake Cortex AI and Semantic Views to make that knowledge more accessible. By grounding AI in governed business definitions and trusted metadata (see this earlier post for more details), builders can ask natural language questions about their data, quickly identify the right datasets and metrics, and investigate issues without manually searching documentation or relying on the Data team. Whatnot's full-time employees now use AI weekly, ## Scaling Kubernetes governance: A platform engineer's guide to Kyverno and CEL DevFeed: [Scaling Kubernetes governance: A platform engineer's guide to Kyverno and CEL]() Original publisher: [Read original article]() Author: Koray Oksay Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Kubernetes](), [Platform Engineering](), [Security](), [developer velocity](), [Containers](), [supply-chain-security](), [YAML](), [opa](), [rego]() Tags: [common-expression-language](), [compliance](), [container-images](), [containers](), [cosign](), [deployment](), [developer-velocity](), [governance](), [hipaa](), [integration](), [kubernetes](), [kyverno](), [pci-dss](), [platform](), [policy](), [security](), [security-policies](), [sigstore](), [soc-2](), [supply-chain-security]() ### AI overview A guide to using Kyverno and its Common Expression Language support for Kubernetes governance. It explains how platform engineering teams can enforce policies, automate resource changes, generate resources, verify image signatures, and maintain security and compliance while preserving developer velocity. ### Source excerpt Kyverno with CEL support provides Policy-as-Code for Kubernetes governance. Enforce security, automate guardrails, and boost developer velocity for platform engineering teams. ## The missing link in developer velocity: Virtual registries and the rise of the Artifact Platform DevFeed: [The missing link in developer velocity: Virtual registries and the rise of the Artifact Platform]() Original publisher: [Read original article]() Author: Adrian Herrera Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Caching](), [CI/CD](), [developer velocity](), [Latency](), [Developer Platform]() Tags: [article](), [caching](), [ci-cd](), [cost](), [developer](), [developer-velocity](), [latency](), [packages](), [performance](), [platform](), [registry](), [saas](), [time]() ### AI overview The article explains how artifact retrieval slows CI/CD pipelines and reduces developer velocity. It presents virtual registries combined with high-performance caching as an acceleration layer that serves artifacts locally, reduces repeated downloads and latency, and lowers cloud egress costs. ### Source excerpt Virtual registries and high-performance caching cut CI/CD artifact download time, reduce egress costs, and boost developer velocity by turning artifact delivery into a core platform capability. ## How Spyne simplified their CDC pipeline with ClickPipes and ClickHouse Cloud DevFeed: [How Spyne simplified their CDC pipeline with ClickPipes and ClickHouse Cloud]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-06-22T11:37:29Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [schema-evolution](), [Kafka](), [MongoDB](), [MySQL](), [Streaming](), [developer velocity](), [Serverless](), [Cloud](), [real-time](), [DevOps]() Tags: [clickhouse](), [cloud-infrastructure](), [data](), [developer-velocity](), [devops](), [kafka](), [mysql](), [open-source](), [real-time](), [schema-evolution](), [serverless](), [streaming]() ### AI overview Spyne migrated its change data capture pipeline from a self-managed Debezium and Kafka stack to serverless ClickPipes and ClickHouse Cloud. The change reduced table onboarding time from more than 1.5 hours to minutes and automated schema evolution, improving data consistency and developer velocity. ### Source excerpt Spyne cut table onboarding from 1.5+ hours to minutes and eliminated schema drift by migrating their CDC pipeline from a self-managed Debezium/Kafka stack to ClickPipes and ClickHouse Cloud. ## Reducing our monorepo size to improve developer velocity DevFeed: [Reducing our monorepo size to improve developer velocity]() Original publisher: [Read original article]() Author: Yasmin McDowell,Lawrence Good,Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura,Mike White,Kevin Altschuler,Facundo Agriel,Ishan Mishr Published: 2026-03-25T17:00:00Z Content type: article Language: en Sources: [Dropbox Tech Blog]() Topics: [developer velocity](), [Git](), [GitHub](), [ci](), [Back end]() Tags: [ai](), [backend](), [ci](), [developer-velocity](), [developers](), [development](), [git](), [github](), [infrastructure](), [onboarding](), [performance](), [scale](), [storage](), [workflows]() ### AI overview Dropbox reduced its server monorepo from 87GB to 20GB, cutting full-clone time from more than an hour to under 15 minutes. The article explains how repository growth affected developer onboarding, CI pipelines, synchronization systems, and operational risk near GitHub's 100GB limit. ### Source excerpt Monorepos will continue to grow as products evolve, but growth doesn't have to mean friction. ## Introducing automatic, short-lived credentials for Chainguard Libraries for Python DevFeed: [Introducing automatic, short-lived credentials for Chainguard Libraries for Python]() Original publisher: [Read original article]() Published: 2026-01-15T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard libraries for python](), [chainguard libraries](), [Authentication](), [pip](), [developer velocity](), [Usability]() Tags: [authentication](), [chainguard-libraries](), [chainguard-libraries-for-python](), [chainguard-libraries-python](), [chainguard-packages](), [chainguard-python-containers](), [developer-velocity](), [github-actions](), [pypi](), [python-libraries](), [python-packages](), [secure-python-packages](), [security](), [usability]() ### AI overview Chainguard announces integrated authentication for Chainguard Libraries for Python through a keyring package. The package provides short-lived credentials that refresh automatically, enabling secure pip access without repeatedly creating, copying, or managing long-lived tokens. It supports local development and GitHub Actions environments with assumable identities. ### Source excerpt New integrated authentication for Python Libraries with a keyring: use short-lived credentials for pip installs to stay secure without slowing developers down. ## Build Better Agents With MorphLLM DevFeed: [Build Better Agents With MorphLLM]() Original publisher: [Read original article]() Published: 2025-08-25T00:00:00Z Content type: article Language: en Sources: [The Fly Blog]() Topics: [AI Agent](), [Code](), [developer velocity](), [Tooling]() Tags: [agents](), [ai-agent](), [cdn](), [close-to-users](), [code](), [cost](), [deploy-app-servers](), [developer-velocity](), [docker](), [elixir](), [fly](), [fly-io](), [hallucinations](), [heroku-alternative](), [heroku-competitor](), [hosting](), [i](), [networking](), [postgresql-clusters](), [servers](), [tooling]() ### AI overview The article presents MorphLLM's Morph Fast Apply as a semantic, structure-aware code-editing tool for AI agents. It addresses the inefficiency and unreliability of full-file rewrites by enabling precise, context-aware edits that reduce token use, compute, cost, and time. ### Source excerpt I'm an audiophile, which is a nice way to describe someone who spends their children's college fund on equipment that yields no audible improvement in sound quality. As such, I refused to use wireless headphones for the longest time. The fun thing about wired headphones is when you forget they're on and you stand up, you simultaneously cause irreparable neck injuries and extensive property damage. This eventually prompted me to buy good wireless headphones and, you know what, I break fewer things now. I can also stand up from my desk and not be exposed to the aural horrors of the real world. This is all to say, sometimes you don't know how big a problem is until you solve it. This week, I chatted to the fine people building MorphLLM, which is exactly that kind of solution for AI agent builders. Slow, Wasteful and Expensive AI Code Changes If you're building AI agents that write or edit code, you're probably accepting the following as "the way it is": Your agent needs to correct a single line of code, but rewrites an entire file to do it. Search-and-replace right? It's fragile, breaks formatting, silently fails, or straight up leaves important functions out. The result is slow, inaccurate code changes, excessive token use, and an agent feels incompetent and unreliable. Full file rewrites are context-blind and prone to hallucinations, especially when editing that 3000+ line file that you've been meaning to refactor. And every failure and iteration is wasted compute, wasted money and worst of all, wasted time. Why We Aren't Thinking About This (or why I wasn't) AI workflows are still new to everyone. Best practices are still just opinions and most tooling is focused on model quality, not developer velocity or cost. This is a big part of why we feel that slow, wasteful code edits are just the price of admission for AI-powered development. In reality, these inefficiencies become a real bottleneck for coding agent tools. The hidden tax on every code edit adds up and your ## Explore engineering solutions from Temporal Replay 2025 speakers DevFeed: [Explore engineering solutions from Temporal Replay 2025 speakers]() Original publisher: [Read original article]() Author: Lauren Bennett Published: 2025-02-13T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Orchestration](), [Architecture & Design](), [Back end](), [legacy](), [Microservice](), [Resilience](), [Automation](), [developer velocity](), [Tech Debt]() Tags: [2025](), [architecture](), [automation](), [backend](), [community](), [developer-velocity](), [engineering](), [finance](), [legacy](), [microservices](), [orchestration](), [payments](), [php](), [resilience](), [ruby](), [solutions](), [workflow]() ### AI overview Temporal Replay 2025 features engineering talks from Salesforce, Vinted, and Datadog about workflow orchestration, durable execution, modernization, scaling, resilience, and developer velocity. ### Source excerpt Explore engineering solutions for workflow orchestration from Temporal Replay 2025, featuring experts from Salesforce, Vinted, and Datadog. ## Meet BaseHub: Developer Velocity And Efficiency Right Down to The Database DevFeed: [Meet BaseHub: Developer Velocity And Efficiency Right Down to The Database]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2024-09-10T07:12:25Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [developer velocity](), [Content Management System](), [Databases](), [autoscaling](), [Serverless](), [Supabase](), [Next.js](), [migration]() Tags: [autoscaling](), [case-studies](), [database](), [developer](), [developer-velocity](), [efficiency](), [migration](), [next-js](), [postgres](), [serverless](), [web]() ### AI overview This article profiles BaseHub, a headless CMS focused on fast, collaborative content management and developer-friendly APIs. It explains how the team moved beyond Supabase as production usage grew, seeking autoscaling, more efficient resource usage, and safer database operations. Neon's serverless Postgres architecture is presented as a way to handle variable traffic and reduce the cost of non-production environments. ### Source excerpt "Databases have always been such a pain in terms of the migration flow, backups, scaling, risk of breaking production... In Neon, everything feels easier and much safer to do" Julian Benegas, CEO of BaseHub BaseHub is a new kind of headless CMS, designed to make content managemen... ## Neon: A New Approach to Database Development DevFeed: [Neon: A New Approach to Database Development]() Original publisher: [Read original article]() Author: Nikita Shamgunov Published: 2024-04-15T13:06:18Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Databases](), [Database](), [developer velocity](), [Provisioning](), [Developer experience](), [systems]() Tags: [company](), [data](), [database](), [databases](), [developer](), [developer-velocity](), [efficiency](), [github](), [postgres](), [provisioning](), [vercel]() ### AI overview Neon announces general availability of its Postgres-based database platform, describing improvements to its internals and experience scaling to more than 700,000 databases. The article presents Neon as a solution for business-critical workloads that emphasizes database scalability, fast provisioning, branching with copied data, point-in-time restoration, and improved developer velocity. ### Source excerpt Neon is now Generally Available. We've shipped major improvements to Neon internals that, combined with our operating experience scaling up to 700,000+ databases over the past year, give us the confidence that Neon is ready to support your business-critical workloads. If you're b... ## The Apollo Supergraph Architecture Framework DevFeed: [The Apollo Supergraph Architecture Framework]() Original publisher: [Read original article]() Author: Lucas Leadbetter Published: 2024-01-24T13:00:00Z Content type: article Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [Architecture & Design](), [developer velocity](), [Security]() Tags: [announcement](), [architecture](), [best-practices](), [developer-velocity](), [graphql](), [high-availability](), [security]() ### AI overview Apollo introduces the Supergraph Architecture Framework (SAF), an assessment based on the Apollo Solutions Architecture team's experience with enterprise organizations. It covers operational excellence, security, reliability, performance, and developer efficiency to help teams evaluate and improve GraphQL supergraphs in production. ### Source excerpt Building a scalable and effective enterprise supergraph is challenging, and often feels like there are countless numbers of ways to approach the challenges that arise without evidence of efficacy. Companies searching the GraphQL community for recommendations leads to a lack of consistency across the industry and doing things that may prove to be ineffective, thus requiring changes later down the line. ## How Nuon rebuilt its API with Temporal Workflows DevFeed: [How Nuon rebuilt its API with Temporal Workflows]() Original publisher: [Read original article]() Author: Justin Pirie Published: 2023-11-22T05:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [developer velocity](), [API](), [distributed-systems](), [reliability](), [data-architecture]() Tags: [api](), [data-management](), [developer](), [developer-velocity](), [how-to](), [reliability](), [workflows]() ### AI overview A webinar recap describes how Nuon rebuilt its API with Temporal Workflows. The company replicated its existing API in one sprint and put it into production in a second sprint, reporting improved developer velocity, reliability, and operational insights. ### Source excerpt We were joined by Jon Morehouse and Jordan Acosta from Nuon for a webinar last week to share how they completely rebuilt their API using Temporal, the best practices for long-lived workflows, and how Temporal is helpful for startups. ## New report shows disconnect between developers and security teams on software supply chain security priorities and responsibilities DevFeed: [New report shows disconnect between developers and security teams on software supply chain security priorities and responsibilities]() Original publisher: [Read original article]() Published: 2023-11-08T00:00:00Z Content type: release Language: en Sources: [Chainguard: Unchained]() Topics: [supply-chain-security](), [chainguard](), [Security](), [developer velocity](), [Open Source]() Tags: [chainguard](), [ciso](), [container-security](), [developer-velocity](), [developers](), [open-source](), [report](), [security](), [software-developer](), [software-supply-chain](), [software-supply-chain-security](), [software-supply-chain-security-research](), [survey]() ### AI overview Chainguard and The Harris Poll released a 2023 survey of CISOs and developers about software supply chain security. The report found that both groups consider it important but differ in their views of security awareness, responsibilities, tooling, and associated risks. ### Source excerpt Chainguard's new report reveals a crucial gap between developers and security teams on software supply chain priorities. ## GraphQL Summit Takeaways on Adoption, Architecture, and Caching DevFeed: [GraphQL Summit Takeaways on Adoption, Architecture, and Caching]() Original publisher: [Read original article]() Author: Khalil Stemmler Published: 2019-11-14T18:39:44Z Content type: article Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [API](), [apollo-client](), [Caching](), [migration](), [React Hooks](), [Developer experience](), [developer velocity](), [React Native](), [Redux]() Tags: [apollo](), [apollo-client](), [caching](), [can](), [code](), [community](), [developer-experience](), [developer-velocity](), [development](), [graphql](), [migration](), [react](), [react-hooks](), [redux](), [rest](), [server](), [summit]() ### AI overview The article summarizes themes from GraphQL Summit, including GraphQL adoption and incremental migration from REST and Redux, architectural benefits of the data graph, the Apollo and React Hooks stack, deployment challenges, federation, schema change validation, and caching. ### Source excerpt This year, I flew out to San Francisco to attend (and help host!) my very first GraphQL Summit. As a Canadian flying to San Francisco for the first time, I was delighted to escape the Toronto cold to explore a new city, meet my awesome new Apollo colleagues, hang out with puppies (oh yeah--we had a puppy booth 🐶), chat with devs from around the world, and of course learn everything I could about the present and future of GraphQL.