Systems | Development | Analytics | API | Testing

Wherever your config lives: Repo-stored and modular bitrise.yml

Most CI tooling makes you pick a side. Visual editing or code. Git as your source of truth or a decent place to edit. A config small enough for one file, or a modular structure your tooling flattens the moment you touch it. The Bitrise Workflow Editor does not ask you to choose. It meets your config where it lives. One file or forty, stored on Bitrise or in your repo. This post covers both, in that order.

How to Automatically Fail Over Between LLM Providers | Kong AI Gateway

Learn how to automatically switch between LLM providers when your primary provider becomes unavailable. In this AI Gateway Discovery video, we configure automatic failover using Kong AI Gateway’s Priority load balancing algorithm. You’ll learn how to assign models to priority groups and fall back to a backup group when all targets in the preferred group are unavailable. Follow along to set up automatic LLM failover and make your AI applications more resilient to provider outages.

What Is Bugfender? Find and Fix Bugs on Real User Devices

Bugs don’t always cause a crash, and they can be difficult to reproduce on your own device. Bugfender helps development teams investigate app issues with remote logs, error monitoring, crash reports, and user feedback in one dashboard. Add the Bugfender SDK to supported mobile and frontend apps to collect logs from real devices, including while a device is offline. Review device context and investigate issues that traditional crash reporting may miss.

Meet Bugfender: Debug Apps With Real User Context #aicoding #iosdev #aidevelopment #aidebugging

Trusted by more than 35,000 apps, Bugfender helps developers understand bugs on real users’ devices, even without a crash. Collect logs, errors, device details and feedback with the SDK, then connect an AI agent through MCP to investigate the evidence.

Best AI Video Tools for Software Demo and Team Marketing Workflows

Sponsored by Magic Hour. This article was prepared with AI assistance. It compares documented product capabilities and practical workflow considerations; it does not report hands-on tests, measured performance, or a universal ranking. Choosing an AI video tool is partly a creative decision and partly a systems decision. A clip must look appropriate, but a marketing team also needs to know where its inputs came from, how the result will be reviewed, and whether another colleague can reproduce the process. An impressive demo answers only one of those questions.

AI Gave Developers More Capacity. Testers Are Next.

Talk to any developer who has been using an AI coding assistant for a few months, and you'll hear some version of the same thing: "I'm getting through a lot more than I used to." Same person. Same hours. More output. Now ask a QA manager how they're keeping up with all that extra output. You'll usually get a tired laugh, followed by: I'm not. And I can't hire. That's the gap I want to talk about. Development is finding ways to grow its capacity without growing the team.

Production Debugging: A Complete Guide to Fixing Live Bugs

We’ve all had this problem as developers. An app works perfectly fine in our own local environment, but as soon as it hits the real world it starts throwing glitches all over town. Why does this happen? Because the real world is messy and hard to predict. Some bugs are only triggered by real users, real devices, and real data. This makes them harder to diagnose – but not impossible.