Outsourcing software as a service (SaaS) development is a strategic approach adopted by businesses aiming to enhance growth, reduce operational costs, and streamline product delivery. In an era where swift market entry and technological efficiency are pivotal, outsourcing offers companies the leverage to focus on core competencies while delegating the complex task of SaaS development to specialized service providers. The process involves partnering with an external organization that possesses the technical expertise, experience, and resources to build and maintain SaaS solutions, effectively bridging gaps in a company's in-house capabilities.

Before the Raygun API limited release last year, we'd been consistently receiving requests for a public API for a long time, to provide a way for our customers to access their Raygun data programmatically. We're now proud to say we're providing a public API with a range of endpoints, but it took us a lot of planning and development to get here! In this post, I'd like to take you back to the beginning of development on our big API project. Specifically, I want to walk through the pivotal decisions we made around testing when we started development on the project, and how (and why) these have paid off.

Large Language Models (LLMs) like GPT-3 have revolutionized the field of artificial intelligence, offering unprecedented capabilities in natural language processing. Fine-tuning these models to specific tasks or datasets can enhance their performance. However, this process presents unique challenges and limitations that must be addressed. This article explores the intricacies of LLM fine-tuning, shedding light on the obstacles and constraints faced in this advanced AI domain.