# feature flags A software development technique for dynamically controlling application functionality, including turning features on or off without deploying new code. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Introducing SwiftTweaks DevFeed: [Introducing SwiftTweaks]() Original publisher: [Read original article]() Author: Khan Academy Published: 2016-05-09T22:00:00Z Content type: release Language: en Sources: [Khan Academy]() Topics: [Swift](), [iOS](), [Xcode](), [Objective-C](), [feature flags]() Tags: [code](), [engineering](), [feature](), [feature-flags](), [interface](), [ios](), [mobile](), [news](), [objective-c](), [swift](), [xcode]() ### AI overview Khan Academy introduces SwiftTweaks, a Swift library for adjusting parameters in iOS apps on a device without recompiling. The article explains how to create tweak libraries, use tweak values and bindings, adjust animation settings, and access the runtime interface. ### Source excerpt By Bryan Clark Today, we're releasing SwiftTweaks, a way to adjust your Swift-based iOS app without needing to ... Read more ## How Delphi ships 100 times a day with its Python backend on Vercel DevFeed: [How Delphi ships 100 times a day with its Python backend on Vercel]() Original publisher: [Read original article]() Author: Kevin Sundstrom Published: 2026-09-15T04:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [Python](), [Vercel](), [Back end](), [feature flags](), [Amazon Web Services](), [Docker](), [airflow]() Tags: [aws](), [backend](), [docker](), [feature-flags](), [python](), [vercel](), [workflow](), [workflows]() ### AI overview A Vercel case study describes how Delphi rebuilt its Python backend on Vercel after previously using AWS, ECS, Docker Desktop, and local databases. The move gave the team a shared deployment workflow, faster onboarding, and more than 100 production deploys per day, while Vercel Workflows and Queues support long-running work and background jobs. ### Source excerpt Delphi on Vercel 10 engineers with no dedicated infrastructure role Everyone ships code, including product and design 100+ production deploys a day behind feature flags Delphi builds digital minds. They capture what someone has written, recorded, and taught, so anyone can tap that expertise on demand. Delphi is "a destination where you can find and interact with interesting minds," says founding engineer Spencer Schoeben. Nobody has built a digital mind network before, so Delphi figures out what to build by shipping features and watching how people use them. That only works if the product is fast to change. "If we are going to win, we need to have an excellent developer and agentic experience," Spencer says. "Otherwise, we could not adapt to how fast our user's demands change." Delphi's frontend had run on Vercel since day one. "The frontend has always been easy," Spencer says, "but the backend was a nightmare." Six months ago, the team rebuilt its Python backend on Vercel as well. Today the whole team ships to production 100+ times a day. Building a Python backend that's easy to work onThe backend slowed every change Delphi's backend previously ran on AWS, with ECS, Docker Desktop, and local databases. In the earliest days, the setup worked because the team was small and much of the context lived in people's heads. As Delphi grew, that made onboarding slower than the team wanted: getting a new engineer to their first deploy took a full day of environment setup. Delphi could have fixed that without leaving AWS. But fixing it properly would have meant designing and owning the infrastructure themselves, and the team didn't want to own infrastructure at all. "That is not our current goal as a company," Spencer says. After moving the backend to Vercel, new engineers could get to production much faster, with the same deploy workflow the rest of the team used every day. Workflows and Queues made the move possible When Delphi was founded, Vercel Workflows and Vercel Queues ## How to Implement Feature Flags for Safe and Gradual Rollouts DevFeed: [How to Implement Feature Flags for Safe and Gradual Rollouts]() Original publisher: [Read original article]() Author: Abisoye Alli-Balogun Published: 2026-09-14T14:58:55Z Content type: article Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [feature flags](), [CI/CD](), [Deployment](), [Development](), [Tech Debt](), [Testing]() Tags: [architecture](), [best-practices](), [ci-cd](), [deployment](), [development](), [experimentation](), [feature-flags](), [how-to](), [tech-debt](), [testing]() ### AI overview This article explains how to implement feature flags for safe, gradual rollouts. It covers separating deployment from release, boolean toggles, percentage-based rollouts, user segmentation, experimentation, kill switches, lifecycle management, and common anti-patterns. ### Source excerpt Feature flags are one of the most powerful tools in a team's deployment arsenal. They decouple deployment from release, meaning your CI/CD pipeline can push code to production servers on every merge, ## Introducing Vercel for Slack DevFeed: [Introducing Vercel for Slack]() Original publisher: [Read original article]() Author: Kevin Corbett Published: 2026-08-19T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Slack](), [Vercel](), [Artificial Intelligence](), [Pull Request](), [ci](), [feature flags](), [configuration]() Tags: [agent](), [ai](), [collaboration](), [enterprise](), [feature-flags](), [features](), [logs](), [metrics](), [pull-request](), [reviews](), [slack](), [vercel](), [work](), [workflow]() ### AI overview Vercel introduces Vercel for Slack, a Public Beta integration for Pro and Enterprise teams. Vercel Agent can join channels, threads, and direct messages, use context from deployments, logs, metrics, code reviews, and pull requests, answer questions, investigate incidents, and propose fixes. Approved plans can also update code or configuration, roll back deployments, manage feature flags, and create tested pull requests without leaving Slack. ### Source excerpt Vercel Agent now works in Slack. Mention it the way you'd pull a teammate into a thread, and it reads the discussion, answers with the context of the platform running your app, and turns the team's decisions into changes you approve. Vercel for Slack is available today in Public Beta for Pro and Enterprise teams. Work starts in conversation. An alert gets noticed in a channel, a fix gets agreed on in a thread, and then someone leaves Slack to do the work. With Vercel Agent, you can tag it in for answers about your infrastructure or hand it the work, all without leaving the conversation. What is Vercel Agent? When we introduced Vercel Agent, we called it an AI teammate for your development workflow. Today it's the first responder for your production apps. When something changes, it investigates your logs, metrics, and deployments, finds the root cause, and proposes a fix, often before anyone's opened a laptop. Give it a PR and it flags the regressions and risky changes that a passing CI run won't show. In Slack, that agent joins the conversation itself. It is a core part of Vercel, so your deployments, build statuses, logs, metrics, code reviews, and PRs are in front of it before you've typed anything. Mention @Vercel in a channel, a thread, or a DM, and it answers like a teammate with every tab already open. And answering is only half of it, because once you know what's wrong, the agent can go fix it: Diagnose issues: Investigate incidents, trace errors to the deploy that caused them, explain a cost spike, and answer questions about your codebase. Code alongside your team: Fix failing builds and CI, review PRs with knowledge of how the code runs in production, and turn a thread's decision into a tested pull request. Operate your projects: Roll back deployments, update configuration, manage feature flags, and resolve unusual usage before it becomes a bill. The agent is read-only by default, and anything that changes code or configuration goes through a plan that you ## LaunchDarkly is now available on the Vercel Marketplace DevFeed: [LaunchDarkly is now available on the Vercel Marketplace]() Original publisher: [Read original article]() Author: Sam Halstead Published: 2026-08-11T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [feature flags](), [Vercel](), [experiments](), [SDKs]() Tags: [agent](), [experiments](), [feature-flags](), [sdk](), [vercel]() ### AI overview LaunchDarkly is now available through the Vercel Marketplace, providing feature flags with local evaluation, release targeting, experiments, metrics, rollback controls, and Vercel Toolbar management. ### Source excerpt LaunchDarkly is now available on the Vercel Marketplace, allowing you to quickly get started with feature flags without additional setup. You can: Sync flags into Global Config and evaluate them locally Target releases by user, attribute, or segment Run experiments with metrics, and roll back with a kill switch View and override flags from the Vercel Toolbar with the Flags Explorer To get started, run vercel install launchdarkly, add the @flags-sdk/launchdarkly adapter, and declare a flag with the Flags SDK: Using a coding agent? Hand it this prompt: Add LaunchDarkly from the Vercel Marketplace, or read the adapter docs. Read more ## How to Build an A/B Testing Framework for Modern CI/CD Pipelines DevFeed: [How to Build an A/B Testing Framework for Modern CI/CD Pipelines]() Original publisher: [Read original article]() Author: Aaron Newcomb Published: 2026-08-10T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [A/B Testing](), [CI/CD](), [CI/CD Pipeline](), [feature flags](), [progressive delivery](), [Monitoring]() Tags: [a-b-testing](), [ci-cd](), [feature-flags](), [how-to](), [monitoring](), [progressive-delivery](), [rollback](), [verification]() ### AI overview This tutorial explains how to integrate A/B testing into CI/CD pipelines using feature flags, progressive delivery, real-time metrics, automated verification, and rollback controls. It argues that deployment should be decoupled from release so teams can validate changes before full rollout. ### Source excerpt Learn how to build an A/B testing framework in your CI/CD pipeline. Accelerate software delivery, reduce risk, and boost innovation. Start now! | Blog ## ITIL vs SRE: why the big clouds went their own way DevFeed: [ITIL vs SRE: why the big clouds went their own way]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-07-26T00:00:00Z Content type: opinion Language: en Sources: [Sridhar Rajarao]() Topics: [SRE](), [site-reliability-engineering](), [Development](), [Terraform](), [pulumi](), [Availability](), [feature flags](), [incident]() Tags: [automated](), [aws](), [azure](), [cloud](), [feature-flags](), [gcp](), [human-review](), [hyperscaler](), [incident](), [incident-management](), [infrastructure-as-code](), [itil](), [on-call](), [postmortems](), [pulumi](), [release](), [reliability](), [service-catalog](), [sre](), [terraform]() ### AI overview This opinion article compares ITIL practices with SRE operations at hyperscaler scale. It argues that human change boards, single production instances, developer-to-operations handoffs, documentation-first configuration management, and weekly release windows do not fit environments serving millions of external customers. It describes automated approvals, gradual deployments, service-team ownership, infrastructure as code, continuous release, error budgets, SLOs, and blameless postmortems as alternatives. ### Source excerpt The big clouds don't run ITIL. Five assumptions ITIL makes that break at hyperscaler scale, and what AWS, Azure, GCP, and OCI use instead. ## Safe Database Rollback Starts Before Deployment DevFeed: [Safe Database Rollback Starts Before Deployment]() Original publisher: [Read original article]() Author: Raul Junco Published: 2026-07-25T12:00:54Z Content type: tutorial Language: en Sources: [System Design Classroom]() Topics: [Database](), [Deployment](), [migration](), [feature flags]() Tags: [database](), [deployment](), [feature-flags](), [migrations](), [production](), [recovery](), [rollback](), [schema]() ### AI overview This article explains why rolling back application code does not automatically roll back database schema or production data. It presents backward-compatible schema changes, staged migrations, feature flags, and targeted recovery as ways to prepare for deployment failures and reduce the risk of data loss. ### Source excerpt Learn why safe database rollback starts before deployment, and how backward-compatible schema changes, staged migrations, feature flags, and targeted recovery keep production failures from turning into data loss. ## Building self-healing feature releases with Harness FME metric alerts and Event Relay DevFeed: [Building self-healing feature releases with Harness FME metric alerts and Event Relay]() Original publisher: [Read original article]() Author: Joshua Klein Published: 2026-07-24T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [releases](), [feature flags](), [Automation](), [experiments](), [Resilience](), [Monitoring]() Tags: [automation](), [event](), [feature](), [feature-flags](), [metrics](), [monitoring](), [payload](), [pipelines](), [resilience](), [self-healing](), [webhooks]() ### AI overview This tutorial explains how to connect Harness FME metric alerts to Event Relay triggers so a pipeline can automatically mitigate risky feature releases through actions such as killing a feature flag. The pattern separates signal emission, webhook handling, and controlled remediation. ### Source excerpt Learn how to build self-healing feature releases with Harness Feature Management & Experimentation. Connect metric-alert webhooks to Event Relay triggers and au | Blog ## A Step-by-Step Guide to Feature Flag Implementation in CI/CD DevFeed: [A Step-by-Step Guide to Feature Flag Implementation in CI/CD]() Original publisher: [Read original article]() Author: Aaron Newcomb Published: 2026-07-23T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [CI/CD](), [feature flags](), [GitOps](), [Automation](), [Microservices](), [Deployment]() Tags: [automation](), [ci-cd](), [deployment](), [feature-flags](), [gitops](), [governance](), [guide](), [microservices](), [pipelines](), [policy-as-code](), [rollback]() ### AI overview A step-by-step tutorial on implementing feature flags in enterprise CI/CD pipelines. It explains how governance, policy as code, GitOps workflows, automation, verification, gradual rollouts, and rollback capabilities can help teams manage releases across many services while maintaining control and compliance. ### Source excerpt Discover how to implement Feature Flags in CI/CD pipelines using governance, automation, and AI-driven delivery. Speed up your releases while keeping them safe. | Blog ## Feature Flag Security in your CI/CD Pipeline DevFeed: [Feature Flag Security in your CI/CD Pipeline]() Original publisher: [Read original article]() Author: Lena Sano Published: 2026-07-22T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [feature flags](), [Security](), [CI/CD](), [AI Agent]() Tags: [ai-agent](), [ci-cd](), [cleanup](), [feature](), [feature-flagging](), [feature-flags](), [governance](), [lifecycle](), [local](), [pii](), [privacy](), [remote](), [security]() ### AI overview This article explains security practices for Harness Feature Management and Experimentation. It covers local versus remote feature-flag evaluation, privacy considerations for sensitive user data, governance, automated cleanup, and an AI agent for feature-flag lifecycle management. ### Source excerpt Secure feature flags with Harness FME using local or remote evaluation, automated cleanup, governance, and AI-powered lifecycle management. | Blog ## Harness AI Configs for Runtime Controls of AI Behavior DevFeed: [Harness AI Configs for Runtime Controls of AI Behavior]() Original publisher: [Read original article]() Author: Nico Zelaya Published: 2026-07-21T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Artificial Intelligence](), [configuration](), [feature flags](), [experiments](), [Model Routing](), [Inference]() Tags: [ai](), [blog](), [config](), [experimentation](), [inference](), [policy](), [routing]() ### AI overview Harness AI Config Management provides a governed runtime configuration layer for changing prompts, models, routing, inference parameters, and other AI behavior without redeploying code. It supports targeting, experimentation, approvals, policy controls, versioning, and audit trails. ### Source excerpt Harness AI Config Management helps teams change prompts, models, and AI behavior at runtime with targeting, experimentation, approvals, policy, and audit trails | Blog ## Software Release Management: A Practical Guide in 2026 DevFeed: [Software Release Management: A Practical Guide in 2026]() Original publisher: [Read original article]() Author: Eric Minick Published: 2026-07-20T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [Software](), [releases](), [Development](), [Deployment](), [Testing](), [CI/CD](), [feature flags]() Tags: [best-practices](), [ci-cd](), [deployment](), [feature-flags](), [guide](), [pipelines](), [release](), [release-management](), [rollback](), [software]() ### AI overview A practical guide to software release management, covering how code moves through testing, approval, deployment, monitoring, verification, and rollback. It explains the distinction between deployment and release and discusses feature flags, automated safety gates, and progressive delivery. ### Source excerpt Master software release management best practices. Learn how to reduce risk, speed up release pipelines, and ship faster with confidence in 2026. | Blog ## You don't have a deployment problem. You have a validation problem. DevFeed: [You don't have a deployment problem. You have a validation problem.]() Original publisher: [Read original article]() Author: Arjun Iyer Published: 2026-07-16T17:00:00Z Content type: opinion Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [Deployment](), [Microservice](), [Architecture & Design](), [feature flags]() Tags: [ai-agents](), [coding-agents](), [deployment](), [feature-flags](), [kubernetes](), [microservices](), [pipelines](), [platform-engineering](), [post-contributed](), [production](), [signadot](), [sponsor-signadot](), [sponsored-post-contributed](), [strategy]() ### AI overview The article argues that organizations often have mature deployment tooling but still release changes in batches because they lack confidence in validating individual service changes against the live versions of their dependencies. It presents validation, rather than deployment mechanics, as the main constraint on independent releases and says coding agents are increasing the volume and opacity of batch changes. ### Source excerpt Ask a platform team about their deployment capabilities, and you will usually hear a genuinely impressive story. Progressive rollouts that The post You don't have a deployment problem. You have a validation problem. appeared first on The New Stack. ## AI Is Writing More Code. Releases Haven't Kept Up DevFeed: [AI Is Writing More Code. Releases Haven't Kept Up]() Original publisher: [Read original article]() Author: Aaron Newcomb Published: 2026-07-14T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [releases](), [AI-assisted coding](), [feature flags](), [Deployment]() Tags: [ai](), [ai-coding](), [deployment](), [feature-flags](), [releases]() ### AI overview The article examines findings from the State of AI-Driven Software Releases 2026 report, arguing that AI-generated code is increasing production faster than release processes can handle. It highlights manual code review as a bottleneck, a gap in AI-specific guardrails, and feature flags as a way to separate deployment from release. ### Source excerpt AI is accelerating code creation, but releases are struggling to keep up. Explore key findings from the State of AI-Driven Software Releases 2026 report and how | Blog ## Flags SDK now evaluates flags 10x faster DevFeed: [Flags SDK now evaluates flags 10x faster]() Original publisher: [Read original article]() Author: Dominik Ferber Published: 2026-07-08T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [feature flags](), [SDKs](), [Vercel]() Tags: [documentation](), [feature](), [feature-flags](), [sdk](), [upgrade](), [vercel]() ### AI overview The Flags SDK and Vercel Flags now evaluate multiple feature flags in bulk around 10 times faster. The improvement comes from reducing microtask queue overhead and creating fewer promises, and it increases with the number of flags. ### Source excerpt Flags SDK and Vercel Flags now evaluate multiple feature flags in bulk around 10x faster. Flag evaluation time improved by reducing microtask queue overhead and creating fewer promises. The improvement scales with the number of flags. Use await evaluate([flagA, flagB]) instead of Promise.all([flagA(), flagB()]) to benefit from these optimizations: You can also pass an object to evaluate flags with named keys precompute() automatically benefits from these improvements as well. Upgrade flags and @flags-sdk/vercel to the latest versions to get started. Learn more in the documentation. Read more ## Vercel Flags: Platform-native feature flags DevFeed: [Vercel Flags: Platform-native feature flags]() Original publisher: [Read original article]() Author: Dominik Ferber Published: 2026-06-22T08:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [feature flags](), [Vercel](), [SDKs](), [Next.js](), [OpenFeature](), [React](), [Frameworks](), [browser]() Tags: [code](), [feature](), [feature-flags](), [features](), [integration](), [next-js](), [open-source](), [performance](), [platform](), [server](), [vercel]() ### AI overview Vercel Flags is a platform-native feature flagging system for creating targeting rules, progressive rollouts, and production kill switches. It evaluates flags server-side, integrates with frameworks such as Next.js and SvelteKit, and provides an open-source Flags SDK with OpenFeature support. ### Source excerpt At Vercel, feature flags are how we ship. From new features to model updates in v0, and even infrastructure changes like a production database migration where a flag was the cutover. The v0 team alone runs hundreds at any given moment. Merging code sends a build to production, but the feature flags control whether users can see what changed. Flags let you ship on your own schedule, release to segments when you're ready, and roll back immediately by toggling a flag, without touching source files or redeploying. Vercel Flags is platform-native: server-side by default, zero impact on page performance, and directly integrated with the frameworks you already use. What is Vercel Flags Vercel Flags lets you create feature flags, define targeting rules by user attributes, segments, or environment, run progressive rollouts, and flip kill switches if something breaks in production. From your code, you read flags through Flags SDK, an open-source, provider-agnostic library we maintain with first-class adapters for Next.js and SvelteKit. If you are using another framework, you can consume Vercel Flags using the built-in OpenFeature provider. The Vercel Flags dashboard sits alongside your project and deployments, where you can create and manage flags. But what makes Vercel Flags different from other flag services is the framework integration. Why framework-native matters Other flag providers give you a generic SDK to wire through your framework yourself, and a separate dashboard to manage flags in. Vercel Flags is built into the Vercel platform, so you manage flags in the same dashboard as your deployments, and your code reads them through the framework-native Flags SDK. Server-side evaluation When a flag is evaluated on the client, users see a loader, a flicker, or a layout shift. The browser can't render the correct view until the flag value comes back. The Flags SDK evaluates on the server instead. With Next.js React Server Components, you read the flag with await during rend ## When Feature Flags Weren't Enough DevFeed: [When Feature Flags Weren't Enough]() Original publisher: [Read original article]() Author: Global Technology Published: 2026-06-09T13:33:43Z Content type: article Language: en Sources: [McDonald's Technical Blog - Medium]() Topics: [configuration](), [feature flags](), [Back end](), [version-control](), [pull-requests](), [ci](), [real-time](), [Tooling]() Tags: [backend](), [ci](), [configuration](), [configuration-management](), [devops](), [distributed-systems](), [feature-flags](), [infrastructure](), [pull-requests](), [real-time](), [software](), [software-architecture](), [tooling](), [version-control]() ### AI overview McDonald's engineers describe replacing a third-party feature-flag platform with a remote configuration system for structured, hierarchical restaurant settings. The system treats configuration like code through version control, pull requests, CI validation, peer review, and auditable live delivery to running backend services without redeployment. ### Source excerpt We rebuilt remote configuration because tooling must match the problem and working like code with review, validation, and live delivery without redeploys. By: Mikhail Baranov, Software Engineer III, & Erick Lopez, Software Engineer III Quick Bytes: Feature flags worked for tracking changes, but they fell short when we needed richer configuration across thousands of restaurants We rebuilt remote configuration to work like code -- version-controlled, validated in CI, and delivered live to running services without redeploys Teams now manage configuration by exception, ship changes safely in real time, and operate with far more confidence and control Runtime configuration carries real weight in a restaurant technology environment. Thresholds get tuned. Features roll out region by region. One location needs a special rule; another needs a noisy signal dialed down. These aren't hypotheticals -- they're everyday decisions that shape how our backend fleet behaves across thousands of restaurants. For a long time, we managed these decisions through a third-party feature-flag platform. It gave us auditability and a clear record of who changed what. But over time, an uncomfortable truth emerged: the tool wasn't built for the shape of our problem. We didn't need toggles and targeting rules. We needed rich, structured configuration -- hierarchical defaults, per-restaurant overrides, clear environment promotion, and real-time delivery to distributed backend services. Forcing that model into a flags-first product created friction: awkward workarounds, unnecessary cost, and extra operational steps for changes that should have felt routine. We wanted configuration changes to feel as safe and controlled as code changes, while still reaching running services immediately. So, we set out to build exactly that. Treating configuration like a managed product Our solution treats configuration with the same rigor as production code. Structured configuration lives in version control. Changes move ## Azure Deployment Strategies & CI/CD Best Practices DevFeed: [Azure Deployment Strategies & CI/CD Best Practices]() Original publisher: [Read original article]() Author: Chinmay Gaikwad Published: 2026-06-09T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [Azure](), [Deployment](), [CI/CD](), [feature flags](), [GitOps](), [Infrastructure as code](), [observability]() Tags: [aks](), [azure](), [best-practices](), [ci](), [ci-cd](), [deployment](), [deployment-strategies](), [feature-flags](), [gitops](), [harness](), [iac](), [observability](), [progressive-delivery]() ### AI overview This guide explains Azure deployment strategies and CI/CD practices for safer software delivery. It covers canary, blue-green, and rolling deployments, feature flags, GitOps, infrastructure as code, pipeline controls, and observability-driven rollback, including examples involving AKS and other Azure workloads. ### Source excerpt Master Azure deployment with CI/CD, canary releases, feature flags, GitOps, and IaC. Learn how progressive delivery and Harness help teams ship faster, safer, and with fewer incidents. | Blog ## With AI, The Proof Is in Production DevFeed: [With AI, The Proof Is in Production]() Original publisher: [Read original article]() Author: Joshua Klein Published: 2026-06-09T00:00:00Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [AI-assisted coding](), [feature flags](), [Software Engineering](), [Code](), [releases]() Tags: [ai](), [ai-code-review](), [code-review](), [feature-flags](), [metrics](), [progressive-delivery](), [safety](), [software]() ### AI overview The article argues that human review, AI code review, staging, and QA cannot guarantee that software changes will behave safely in production. Because AI-assisted development increases the volume and uncertainty of changes, it recommends relying on feature flags, progressive delivery, and production metrics to manage releases when review is imperfect. ### Source excerpt Human and AI code review can't guarantee production safety. Learn why feature flags, progressive delivery, and metrics-driven releases are essential in the AI software era. | Blog ## Vercel Flags is now generally available DevFeed: [Vercel Flags is now generally available]() Original publisher: [Read original article]() Author: Chris Widmaier Published: 2026-04-16T08:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [feature flags](), [Vercel](), [SDKs](), [Next.js](), [OpenFeature](), [backends]() Tags: [feature-flags](), [frameworks](), [next-js](), [platform](), [sdk](), [vercel]() ### AI overview Vercel Flags is now generally available as a feature flag provider built into the Vercel platform. It supports targeting rules, user segments, environment controls, and framework-native use through the Flags SDK in Next.js and SvelteKit applications. An OpenFeature adapter supports other frameworks and custom backends. ### Source excerpt Vercel Flags is now generally available. Vercel Flags is a feature flag provider built into the Vercel platform. Create and manage feature flags with targeting rules, user segments, and environment controls directly in the Vercel Dashboard. The Flags SDK provides a framework-native way to define and use these flags within Next.js and SvelteKit applications, integrating directly with your existing codebase: Once you define a flag, you can use them within your application in a few lines of code: For teams using other frameworks or custom backends, the Vercel Flags adapter supports the OpenFeature standard, allowing you to plug Vercel Flags into their provider agnostic SDK. Try it out or learn more about Vercel Flags. Read more ## Copy-to-Prompt instructions now available for Flags DevFeed: [Copy-to-Prompt instructions now available for Flags]() Original publisher: [Read original article]() Author: Vincent Derks Published: 2026-04-13T16:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [feature flags](), [Vercel](), [SDKs](), [Command-line interface](), [Code](), [configuration]() Tags: [cli](), [code](), [configuration](), [feature-flags](), [sdk](), [vercel]() ### AI overview Vercel Flags now provides copy-to-prompt instructions on the feature flag details page. Developers can use the instructions to install the Flags SDK, link a project with the Vercel CLI, and add flag definitions to their code, while teams can also copy the definitions for manual configuration. ### Source excerpt The feature flags details page now includes copy-to-prompt instructions in the instructions pane. You or your agent can install the Flags SDK, link the project using the Vercel CLI, and add the required flag definitions to the code base. Teams that prefer manual configuration can still copy the exact flag definitions directly from the instructions pane. Learn more about Vercel Flags. Read more ## Dynamic configuration in Java with Server-Side Firebase Remote Config DevFeed: [Dynamic configuration in Java with Server-Side Firebase Remote Config]() Original publisher: [Read original article]() Author: Athira M Published: 2025-10-24T06:33:20Z Content type: tutorial Language: en Sources: [Firebase Developers - Medium]() Topics: [Firebase](), [Java](), [Back end](), [SDKs](), [feature flags](), [Firebase console]() Tags: [backend](), [feature-flags](), [firebase](), [firebase-console](), [firebase-remote-config](), [java](), [maven](), [remote-config](), [sdks]() ### AI overview This tutorial explains how to use server-side Firebase Remote Config with Java to externalize backend configuration and feature flags. It describes how the Java server fetches the full configuration template, evaluates conditions locally through the SDK, initializes the Firebase Admin SDK, defines safe defaults, and evaluates parameters using runtime context. ### Source excerpt Dynamic Configuration in JavaUsing Server-Side Firebase Remote Config Firebase Remote Config is a powerful, cloud-based service that lets you dynamically change your app's behavior and appearance without requiring users to update your app. It's been a game-changer for feature flags and dynamic theming on mobile and web platforms. But what about your backend, or your critical API or Java micro-service? The great news is that Firebase has expanded its support, bringing this powerful tool to the server side with SDKs for Node.js, Python, Go, and Java. This allows you to control server behavior and externalize configuration directly from the Firebase console. The key difference: where evaluation happens If you're familiar with client-side Remote Config, you know the client makes a request, and the Firebase service returns the final, evaluated configuration. The server-side model is fundamentally different: The Java server fetches the entire configuration template (parameters, values, and conditions) from the Remote Config service. The Java server then performs the condition evaluation locally, using the SDK. This shift means your server is in control, making local decisions based on attributes like user ID, location, or subscription tier. This is efficient and perfect for dynamic server logic. 🛠 Setting up Remote Config in Java Let's walk through how to integrate this into your backend, using a Payment Gateway Feature Flag as our running example. Step 1: Add the Firebase Admin SDK First, add the Firebase Admin SDK dependency to your project's configuration file (e.g., pom.xml for Maven). com.google.firebase firebase-admin 9.7.0 Step 2: Initialize the Admin SDK Your server needs secure access to Firebase. We'll use a Service Account to initialize the Admin SDK. import com.google.auth.oauth2.GoogleCredentials; import com.google.firebase.FirebaseApp; import com.google.firebase.Firebas ## Diff Feature Flags Before Running Tests DevFeed: [Diff Feature Flags Before Running Tests]() Original publisher: [Read original article]() Author: Gleb Bahmutov Published: 2025-09-23T04:00:00Z Content type: tutorial Language: en Sources: [Gleb Bahmutov]() Topics: [feature flags](), [Testing](), [Cypress](), [GitHub Actions](), [JSON](), [Command-line interface]() Tags: [advice](), [cli](), [cypress](), [e2e](), [feature-flags](), [github-actions](), [json](), [process](), [testing](), [tests]() ### AI overview This tutorial explains how to detect unexpected feature-flag changes before end-to-end tests run. It shows how to save LaunchDarkly flags as JSON, compare current flags with the saved version in GitHub Actions, and update the saved configuration when defaults change. ### Source excerpt As I explained in my previous blog post on feature flags and testing, you need [Next page]()