Systems | Development | Analytics | API | Testing

Why we ditched frontier AI agents and built our own

At Bitrise, our goal is to help developers speed up their builds and automate tedious processes, from first code commit all the way to production release. To advance this mission, we began exploring how AI can improve developer workflows. Over recent months, I joined a small, fast-moving tiger team focused on cutting through hype to identify valuable AI capabilities. Our goal: to design, develop, and maintain production-grade AI features that truly help developers.

API Gateway vs. AI Gateway: The Definitive Guide to Modern AI Infrastructure

Traditional API Gateways: Excellent for routing, auth, and microservice traffic; poor at AI workloads. Limitations: Can't track tokens, manage streaming responses, enforce content-level security, or use semantic caching. AI Gateways Purpose-built for LLMs with: Architecture Recommendation: Layered approach: Benefits: Lower costs (20--40%), better performance, centralized governance, future-proof AI infrastructure. Market Context.

The AI-Powered Telecommunications Enterprise: Zain KSA's Strategy

The AI-Powered Enterprise: A Zain KSA Blueprint In the competitive telecommunications sector, how does a leading communications service provider move its AI strategy into action? This interview with Zain KSA’s Analytics General Manager, Hamzeh Saud Aldraaee, reveals their powerful blueprint for success.

Kafka Service Bundle: Managed Apache Kafka Without Lock-In

Apache Kafka delivers unmatched performance for real-time data streaming — but managing it in-house requires deep expertise. That’s where OpenLogic’s Kafka Service Bundle comes in. This managed Apache Kafka solution helps enterprises simplify operations, control costs, and maintain full ownership of their data — without the vendor lock-in of commercial clouds. Key benefits: With OpenLogic, your business gets the freedom and flexibility of open source Kafka — supported by the expertise and reliability enterprises depend on.

Performance Testing and Artificial Intelligence (2/2)

If you recall part one of this blog post, we were going to use ChatGPT in parallel with how we would work to cover these aspects of performance testing. We left the first part of this blog post at the point at which we had compared Requirements Gathering and Risk Assessment, we will pick this post up by looking at Script Creation before concluding with Results Analysis. Our performance testing tool of choice will be JMeter.

Performance Testing and Artificial Intelligence (1/2)

If you believe many articles online you would believe that automation in testing will soon be defined, managed and executed by Artificial Intelligence (AI). AI is embedded in many organisations technology landscape and to think that this model will change is shortsighted. AI is here to stay undoubtedly in one form or another, but should it be responsible for the automated testing of your applications under test?