React feature flags secure rollbacks by polling backend APIs for sanitized toggles.

·1 views

A media platform uses React feature flags to control a new search view during nightly pipeline updates. To safely roll back changes, a backend service fetches a sanitized toggle value and passes only a public Boolean to the frontend. This prevents the browser from accessing administrative keys or sensitive flag data. The frontend polls the backend for updates, with a controlled interval and jitter to manage load and propagation delay during incidents. If the flag response is absent or invalid, the system defaults to disabling the new feature to fail safely.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

How to diagnose Windows access violation crash code 0xC0000005

The Windows crash code 0xC0000005, or an access violation, indicates a program attempted to access invalid memory. The most direct way to find the culprit is to check the Windows Event Viewer under Application logs for event ID 1000, focusing on the 'Module' field. If Event Viewer is unavailable, a PowerShell command using Get-WinEvent can retrieve the same crucial faulting module data. For deeper analysis, administrators can configure the system to save full crash dumps and analyze them with Windows debugging tools.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer releases open-source React Native library for sticky tab interfaces

Developer Jade Zhou has open-sourced a React Native library called StickyTabView. The library creates interfaces with a collapsible header, sticky tabs, and synchronized scrolling across tabbed sections. It was built to address the complex gesture coordination required for such layouts, which existing solutions often handle differently. The library offers features like allowing scrolling to start from the header area and customizable sticky positioning for the tab bar.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

AREX-2 AI research develops LLM agents that learn from mistakes over time

A new research paper titled 'AREX-2' introduces a method to improve autonomous AI agents. It addresses a common limitation where large language models fail at long, complex tasks requiring sustained reasoning and self-correction. The system trains agents using verifiable improvement trajectories from domains like machine learning and programming. These domains provide clear feedback, such as test results, to label genuine improvement. The approach aims to decouple the skill of iterative self-improvement from raw knowledge, allowing performance to scale with more reflection time.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

AI bug detection study finds format influences results more than model power

An independent developer tested a small AI model's ability to detect a specific software bug in August 2026. The model identified the bug in 9 out of 10 attempts when the code was presented as a 'diff', but failed in all 10 attempts when analyzing raw source code. The researcher noted the test involved only one specific, seeded bug and a single small local model, limiting broader conclusions. They also emphasized that the AI's findings were never formally accepted by the review tool's own validation step.

0 comments Read more at DEV Community