Systems | Development | Analytics | API | Testing

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.

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.

We’re lowering our prices and introducing key product enhancements to power your data pipelines.

Performance budgets deliver value when tied to user-focused metrics such as Time to Interactive and First Contentful Paint. For most mobile experiences, aiming for a Time to Interactive under five seconds helps reduce bounce rates and keeps users engaged.

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.

For a real estate platform, a new market can introduce different source systems, property attributes, identifiers, business rules, geographic structures, and reporting requirements, each of which enters the platform somewhere. The engineering question is where those differences land in the existing platform, and whether the current data model, integrations, search, workflows, and analytics can absorb them without a second architecture forming alongside the first.

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.

Google Research’s TimesFM 3.0 is a 330-million parameter foundation model that unlocks native multivariate time-series forecasting in BigQuery SQL.