Systems | Development | Analytics | API | Testing

Agriculture has long been the backbone of human survival, proof of our deep connection with and dependence on nature. But as the world evolves, so do the challenges in farming. From shrinking arable land to unpredictable weather patterns, how can farmers keep up? How do we ensure there's enough food for future generations? There’s no doubt that farming has come a long way, but let’s be honest, it’s getting harder every year.

When you think of artificial intelligence in transportation, what’s the first image that comes to mind? Is it self-driving cars smoothly cruising city streets, or maybe delivery drones zipping through the skies? While the dream of fully autonomous vehicles might still be on the horizon, the reality is that AI is already reshaping how we move, every single day. Look how our Co-founder is enjoying the view in a self-driving car in the USA! ‍ Yes, you saw that right, it’s fully autonomous.

How does GDPR impact AI innovation, and what affects might AI have on regulations like GDPR? According to McKinsey, 78% of companies now use AI in at least one area of their business as of July 2024. But this quick adoption brings challenges for organisations handling data from the European Union and the UK. The main challenge for InfoSec and other enterprise leaders is clear. Using AI effectively means being able to develop faster.

The demand for AI-driven applications is surging, and enterprises have reached an inflection point where they can no longer afford fragmented, siloed development. Traditionally, AI development is done by data scientists or machine learning experts with deep expertise in multiple tools and frameworks.

Every day, finance teams using Oracle ERPs find themselves bogged down by the inefficiencies of manual processes—relying on spreadsheets, juggling data extracts, and waiting on IT for access to the information they need. Even with Excel as the go-to tool, managing large datasets and producing accurate reports becomes a time-consuming ordeal, pulling focus away from strategic analysis and decision-making. Enter Spreadsheet Server from insightsoftware.

Financial institutions and other heavily regulated industries can use generative AI in their testing strategy today. Read on to see the use cases.

Many people don’t test their WordPress websites, and it’s a problem. While the core of WordPress is fairly well-tested by its creators, users, and the open-source community, the same cannot be said for every plugin and theme. There’s an infinite combination of versions, hosting environments, plugins, themes, and configurations. You can’t trust the community to catch every bug that could affect your website.

AI is everywhere. Depending on who you ask, it’s either making developers obsolete, or it’s just hype. But for those of us who’ve actually used AI tools in real-world engineering workflows, especially in complex distributed systems, the truth lies somewhere in between. At Multiplayer, we’ve spent the past few years exploring how AI can—and can’t—help solve two of the most persistent challenges in distributed systems: documentation and debugging.