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Feature flags

Assess

Techniques

A technique for turning features on or off without redeploying code.

Why it's here

Placed in Assess: 4 article(s) of evidence from 3 source(s), led by research-stage coverage, with 2 in the last 30 days. Confidence 57%.

Evidence (4)

  • 7GitHub Blog·9/25/2026framework_update
    GitHub improves performance by moving Primer to CSS Modules

    GitHub’s Primer design system moved from CSS-in-JS to CSS Modules to reduce client and server rendering overhead. The migration was done incrementally with feature flags and visual regression tests, and GitHub reports gains of 55% less server-side rendering time and 25% less component initialization time.

  • 5The New Stack·9/13/2026research
    AI Observability Gaps Expose Silent Failure in Production

    The article argues that traditional CI, evals, and traces are not enough to diagnose AI features that can return wrong answers while still appearing healthy. It uses Dynatrace’s 2026 SRE and Platform Engineering survey to show that observability is widely used but not fully integrated across deployments, and explains how runtime telemetry helps trace symptoms back to releases, tool calls, and retry behavior.

  • 4InfoQ·8/7/2026framework_update
    Rootly Ends Small PR Rule as AI Agent Code Generation Grows

    Rootly says its long-standing rule favoring small pull requests is no longer effective because AI agents now generate most of its code. The company is shifting review criteria toward blast radius, using feature flags and rollback readiness to judge risk instead of line count.

  • 4The New Stack·7/16/2026research
    The Real Bottleneck in Independent Deployments Is Validation

    The article argues that many platform teams already have strong deployment tooling, but still ship changes in batches because they cannot validate each change independently with enough confidence. It says shared environments and release trains were practical responses to scarce validation capacity, and that coding agents may worsen the problem by increasing change volume and batch size.