Systems | Development | Analytics | API | Testing

M5 Pro runners for GitHub Actions, coming to Bitrise Build Hub and Bitrise CI

Apple announced the M6 and M5 Pro on August 25, 2026, both inside the redesigned Mac mini. Bitrise Build Hub will be the first GitHub Actions runner alternative to add them to the fleet. That means your builds run on the newest Apple silicon before any other GitHub Actions runner can offer it. That's not a one-off. We've shipped new runners fast on every Apple silicon generation since M1 all the way up to M4 Pro, and M5 Pro and M6 are next.

Agentic QA demo: Stop fixing broken test scripts

See agentic testing in action with SmartBear BearQ. BearQ uses autonomous agents to explore your application, uncover defects, create regression tests, and adapt testing as your product changes – with human oversight built into the process. Broken, flaky test scripts eat up hours every sprint – and still leave gaps in coverage. SmartBear BearQ replaces constant script maintenance with agentic QA, so testing can move beyond predefined paths without giving up human oversight.

Migrating a LoadRunner Script to OctoPerf With an AI Agent

A VuGen script is C. Some scripts are a recorded journey with helpers around it, others are a framework that happens to send a few requests. OctoPerf 17 ships a LoadRunner migration playbook for AI agents whose first job is to tell you which one you have. Real prompts, real output, script downloadable. Target: JPetStore, our public MyBatis demo shop. Every step is reproducible. One Action.c, HTTP recording level, eight transactions walking a purchase. Around them: It replays green in VuGen.

The AI Code Verification Crisis: Meet AURA, the Platform Built to Solve It

AI didn't remove the release bottleneck, it moved it downstream. Code volume exploded, verification didn't. The old QA model isn't broken, it's outgrown: 80% of engineering teams have already traced a production incident to AI-generated code. AURA is Sauce Labs' answer, the only full-lifecycle release assurance platform built to close the gap. It continuously verifies every release against business intent, authoring, running, and regenerating tests in an autonomous learning loop, with humans in control.

Customer Service AI Orchestration: Smart Intake Isn't Enough

Customer service AI orchestration connects AI-driven intake to the backend systems and people who actually resolve a request, not just the chatbot that receives it. Every request should trigger an end-to-end resolution, not stop at an automated response. The real challenge with AI in customer service isn’t adoption; it’s fragmentation.

Automating eLearning with AgentSpot: A 7-Agent Pipeline

Seven AgentSpot agents now augment every step of our content development pipeline, shifting our team’s focus from manual operational tasks to high-value content development and strategy. Our eLearning development team at ThoughtSpot manages around 50 courses and hundreds of videos across six learning paths on ThoughtSpot University. We build enablement content for external customers: business users, business analysts, data experts, and administrators.

Migrating a NeoLoad Project to OctoPerf With an AI Agent

Migrating a load testing project is rarely hard. It is long, and easy to abandon halfway. OctoPerf 17 ships a NeoLoad migration playbook for AI agents. Real prompts, real output, project downloadable. Target: JPetStore, our public MyBatis demo shop. Every step is reproducible. One User Path, split Init / Actions / End, six containers walking a purchase. Around them: Download it to follow along.

Guardrails, not gatekeepers: unlock Kafka self-service for developers

The standard approach for Kafka in enterprise environments is to lock it down and have engineers file tickets. Platform teams do that because traditional engineering tools (CLIs, UIs and native ACLs) have no enterprise governance, so you cannot grant access you can scope, revoke or prove later. That’s the Kafka ticket desk: the week is access requests instead of platform work, engineers wait days to ship and Kafka adoption scales only as fast as the ticket queue moves.