FeatBit

We’ve released FeatBit Aspire v0.0.3, making it easy for .NET developers to deploy FeatBit to Azure Container Apps using a language they already know. https://lnkd.in/g5XnSE9y FeatBit Aspire v0.0.3 supports FeatBit v6.0.0.

A sneak peek at FeatBit v6.0.0 👀

You can now deploy the latest FeatBit (v5.4.4) to Azure Container Apps with Terraform 🚀 It supports both Standalone and Standard deployments. We hope this makes self-hosting FeatBit on Azure a little simpler—and saves you a few setup headaches along the way. https://lnkd.in/gQytk56J #FeatBit #Azure #Terraform #FeatureFlags

We're excited to announce the launch of the FeatBit Partner Network. If you're a consulting company, system integrator, DevOps agency, or technology partner helping engineering teams build and deliver software, we'd love to work together. FeatBit is an open-source, enterprise-ready feature management platform built for self-hosted deployments, progressive delivery, and AI-native release engineering. Learn more: https://lnkd.in/gwHVWn-3

When people ask whether gradual rollout/canary deployment should happen in the gateway, or app level (using feature flag), my answer is usually: It depends. But before discussing implementation details, it's worth asking whether Feature Flags are the right tool for your team. In my experience, Feature Flags make sense when teams need to: - Ship multiple times a day. - Release multiple features in the same release train. - Quickly isolate and disable a problematic feature in production without rolling back the entire release. - Precisely identify which feature is causing production issues. - Validate user experience, product assumptions, or business outcomes before exposing a feature to everyone. For teams with these needs, Feature Flags are usually more important than the specific place where the rollout logic runs. The question is often not: "Should rollout happen in the gateway or the app?" The question is: "Do we need the ability to release, control, and troubleshoot features independently?" Some useful reading: - https://lnkd.in/g4QRf9yG - https://lnkd.in/gkun_GYz #DevOps #FeatureFlag OpenFeature #AICoding #Agile #SoftwareDevelopment #SoftwareProductivity

The best product teams do not win because they have more ideas. They win because they turn ideas into evidence faster. One metric captures this well: "Experiments per unit of time." The more meaningful experiments a team can run, the faster it learns. But meaningful experimentation is still surprisingly hard. To run a good experiment, you need to know: - What hypothesis is worth testing. - Which users should be included. - What metrics actually matter. - How long the experiment should run. - How to avoid false confidence. - How to interpret the result and decide what to do next. That usually requires a mix of product growth experience, statistical intuition, engineering discipline, and data science support. So in many companies, experimentation becomes centralized: - A few experts design the tests. - Everyone else waits. That slows down learning. At FeatBit, we are building an 【agentic experimentation platform】to make high-quality experimentation accessible to more people across the organization. PMs, engineers, analysts, and business teams should be able to turn an idea into a well-designed experiment — without needing to become experimentation experts first. Our belief is simple: The next leap in product velocity will not come only from shipping faster. It will come from helping every team learn faster. More meaningful experiments. Faster iteration. Compounding product knowledge. That is the future we are building at FeatBit. #Experimentation #ProductGrowth #AI #ProductManagement #FeatureManagement #FeatBit

AI coding is no longer a demo. It's in the daily workflow. OpenAI Codex, GitHub Copilot, Claude Code and others now write, refactor, explain, and review code at a speed teams have never had before. That speed is real. And it's useful. But it quietly moves the bottleneck downstream. When code generation gets faster, the pressure doesn't disappear — it shifts: - more pull requests - more reviews - more integration risk - more edge cases - more production uncertainty - more rollback responsibility So for CTOs and engineering leaders, the question is changing. Not just: "How do we help developers write code faster?" But: "How do we help teams release AI-assisted code safely?" That requires a stronger delivery control loop: - context before coding - repo rules before generation - feature flags before exposure - canary rollout before full release - metrics before confidence - rollback before panic - learning after production And here's the detail most people miss. When AI agents start generating feature-flagged code across your repos, the SDK layer suddenly matters. You probably don't want agents hard-wiring vendor-specific flag code everywhere. That's where OpenFeature earns its place. Not as the release strategy itself — but as a vendor-neutral standard that keeps flag usage portable, consistent, and less locked into any single provider. The takeaway: 🚀 AI makes coding faster. 🛡️ Feature flags keep exposure safe. 🔓 OpenFeature keeps that control layer portable. The teams that win won't just generate code faster. They'll absorb AI speed safely — without wiring themselves too deeply into one vendor's SDK. Genuinely curious how others are thinking about this: When an AI agent generates feature-flagged code, should it target a vendor SDK directly — or a standard like OpenFeature? #AIEngineering #GitHubCopilot #OpenAI #Codex #OpenFeature #FeatureFlags #EngineeringLeadership #PlatformEngineering #DevTools #CodingAgent #AIAgent

🚀 FeatBit (feature flag & experimentation infra) is now officially upgraded to .NET 10! Starting from FeatBit v5.3.6, FeatBit backend services — including APIs and evaluation services — are now running on the latest .NET 10 stack. Excited about the performance improvements, ecosystem evolution, and the future of modern .NET development 🙌 Huge respect to the .NET team and community for continuing to push the platform forward. #dotnet #dotnet10 #csharp #opensource #featureflags #devtools #softwareengineering #microsoft .NET Developers

Are AI code review better than human reviewers? We published a new research-backed blog looking at current evidence from AI review tools, academic benchmarks, and real-world engineering workflows. The short answer: not overall. AI review is already strong as a fast first-pass layer: - quick feedback - broad PR coverage - repetitive quality checks - security and test-gap reminders But human reviewers still lead where context matters: - product intent - architecture - risk judgment - mentoring - final acceptance The practical model is not AI vs. humans. It is AI for coverage, humans for judgment, and release control for changes that can pass review but still fail in production. Full blog in the first comment. 👇 #AICoding #SoftwareEngineering #DeveloperProductivity #DevTools #SoftwareDelivery