Systems | Development | Analytics | API | Testing

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.

To generate a random number in Ruby, call rand: rand(10) returns an integer from 0–9, rand(1..10) an integer from 1–10, and rand alone a float between 0.0 and 1.0. For security-sensitive values like tokens and passwords, use SecureRandom — rand is predictable. Computers cannot produce truly random numbers by computation alone. A deterministic machine can only produce pseudorandom numbers: a stream that looks random but comes from an algorithm fed with a starting value, the seed.

Every software team knows the pain of the 4:00 PM build failure. The application itself is perfectly fine, but a tiny layout update threw off an automated script, triggering a false alarm across the deployment pipeline. The failure stems not from broken software, but from a brittle script losing its way. By tying validation to volatile implementation details, the QA industry has cornered itself.

Here's the idea I want to leave you with. Your manual test library isn't documentation. It's automation that has been waiting for something that can read it. And you shouldn't have to move it, rewrite it or migrate it to set it free. The clearest version of this came up in a customer conversation a few weeks ago. The head of quality at a fast-growing fintech told me his company pays an outside firm to run its manual regression, and the firm bills per test executed.

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.

Two days at RAI Amsterdam (17–18 September), nearly a hundred sessions, and energy throughout the hallways. AGNT Con + MCPCon Europe is the Agentic AI Foundation's flagship event under the Linux Foundation, and this year it consolidated the European MCP content that previously lived at MCP Dev Summit Europe. If you care about MCP, this was the place to be.

An API is a defined way for one piece of software to request data or actions from another. The Model Context Protocol (MCP) is an open standard that gives AI agents a consistent way to discover and use tools and data sources, so they don't need a custom integration for each one. The short version: MCP doesn't replace APIs. Most MCP servers call APIs behind the scenes. What MCP changes is who the interface is designed for. APIs are designed for developers who read documentation and write code.

Watch an AI agent run a regression test sometime. Really watch it. It lands on a login page. It reads the entire page structure: every element, every attribute, every nested div someone added in 2019 that nobody has dared to remove since. Then it reasons. It weighs its options. It explains to itself, at some length, that the button labeled "Sign in" is probably the one that signs you in. And then, finally, it clicks. Now multiply that by every step, in every test, in every regression run, every night.

Quick summary: SAP Enterprise Performance Testing (EPT) by Tricentis now integrates with SAP Cloud ALM, so teams can create, run, and track performance tests in the same hub as the rest of their S/4HANA transformation. For teams managing SAP transformations, most major workstreams are visible in Cloud ALM, but performance testing has long been an exception. It has lived in a separate tool, forcing teams to treat it as a separate workflow that’s often left to the end of a project.