Systems | Development | Analytics | API | Testing

Artificial Intelligence (AI) is no longer a futuristic ambition — it’s the strategic reality of today’s most competitive Independent Software Vendors (ISVs). In our recent Seamless Intelligence: Real-World AI Success Stories from Innovative ISVs webinar, I broke down how AI is transforming the ISV landscape, offering a blueprint for how organizations can move beyond dashboards, and into the age of predictive and generative intelligence.

There are few things that can derail developer productivity and undermine your pipeline like a flaky test. Testing is the backbone of a good development process, ensuring that your code is as accurate and usable as possible. When these tests point towards faulty development, the impacts can be significant. This information is predicated on an assumption, however – the assumption that what the test says is accurate.

Learn the latest best practices for knowing which mobile devices to test on, plus our recommended devices to test on by region and business size.

Just as some problems are too big for one person to solve, some tasks are too complex for a single artificial intelligence (AI) agent to handle. Instead, the best approach is to decompose problems into smaller, specialized units so that multiple agents can work together as a team. This is the foundation of a multi-agent system—networks of agents, each with a specific role, collaborating to solve larger problems. When building a multi-agent system, you need a way to coordinate how agents interact.

JSON (JavaScript Object Notation) is a fundamental component of modern web development. Its simplicity and readability have made it a universal data interchange format, used across a wide range of industries and applications. The straightforward structure of JSON, which is both human-readable and machine-parseable, has contributed to its widespread adoption.

Data masking protects sensitive information by replacing real data with realistic, fictitious values. It’s an essential technique for securing data, reducing breach risks, and meeting compliance standards. Let’s explore the fundamentals of data masking, its importance, common methods, and some real-world examples of how it’s used.

Most engineering teams have infrastructure monitoring nailed down—they are tracking uptime, latency, and error rates, and have set up alerting in places. But API issues don’t always start there. Infrastructure metrics don’t tell you how your API users experience your API. A critical integration may have been repeatedly facing failures due to invalid authentication tokens. A new version you have deployed might have introduced a subtle schema change that breaks older clients.

BigQuery ML adds TimesFM, LLM-based structured output generation, row-wise inference functions, expanded model choice, and Contribution Analysis GA.

Emerging technologies like generative AI (GenAI) and agentic AI are poised to significantly enhance IT operations. These advancements offer new, truly transformative ways to manage, optimize and automate IT environments, and are certain to improve efficiency and foster innovation. GenAI’s ability to process vast amounts of unstructured data and agentic AI’s autonomous decision-making capabilities span predictive analytics to automated problem-solving.

Anyshift provides AI context for your infrastructure, powered by Annie—an AI infrastructure assistant trained on your environment. From answering complex infrastructure questions to suggesting Terraform code and catching hidden issues, Annie helps teams manage, monitor, and optimize their infrastructure with ease and precision. Unlike generic AI copilots, Anyshift provides context-aware insights based on your actual infrastructure and codebase—not just LLM guesses.