Systems | Development | Analytics | API | Testing

In March 2024, Microsoft announced it would retire App Center in March 2025. For a lot of mobile teams, the first question was simple: what now? I was on the Release Management team at the time, and we thought CodePush was worth pursuing. We wrote up the case, got it onto the roadmap, and then waited. Nobody could say yet whether it would become a valuable product, and a team our size has to pick its bets carefully.

What are we spending? Who’s using AI? Is it improving productivity? Here’s how we use Fivetran’s own AI connectors to turn fragmented AI consumption data into answers we can act on.

AI agents that forget their context are just expensive chatbots. The difference between intelligent agents and simple assistants comes down to memory architecture, specifically how well your system tracks relationships, maintains temporal context, and reasons over evolving information. Graph databases have moved from niche infrastructure to essential AI components, with GraphRAG implementations showing 80% improvements in agent truthfulness and significant accuracy gains over vector-only approaches.

If you’ve ever written JavaScript, you’ve never had to write code that says “delete this object, I’m done with it.” That’s because there’s a process called garbage collection running in the background to save you the hassle. Most of the time this process works smoothly and invisibly. But it can also cause an app to suddenly start stuttering for no obvious reason if you don’t manage it effectively. This post will give you the tools to do just that.

LLM observability is the practice of collecting and analyzing data about how a language model application or AI agent behaves, including prompts, responses, tool calls, cost, speed, and errors, so teams can find problems, measure quality, and improve results. AI agents can now plan tasks, call tools, and work across business systems. That makes them powerful, and it also means a single request can involve many steps that no one sees directly.

Agentic AI security is the set of controls that keep AI agents from accessing, changing, or sharing more than they should. It covers who an agent acts for, which data and tools it can use, which actions need approval, and how its activity is recorded and reviewed. AI agents are moving from pilots into daily operations. They read invoices, update records, answer customers, and trigger workflows across business systems.