Built-in functions, UDFs, materialized results, and integrations with ML and AI models make streaming SQL a compelling choice ...

Your trading bot crashes at 3 AM because the forex feed went silent. Real-time currency data really shouldn't mean spe ...

A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...

AI local packs and ads are shrinking organic space. New data shows why calls drop even when 3-pack positions hold — and what ...

X is once again reforming its fee structure for the developer interface. While the entry barrier is decreasing, the costs are ...

For years, the industry standard for data ops has been a ticket-based service bureau. A product manager wants a new dashboard? Ticket. A data scientist needs a new feature pipeline? Ticket. A ...

Agent experience (AX) is the new competitive advantage in the era of AI. Learn how B2Ai commerce is transforming marketing ...

Q4 2025 Earnings Call February 4, 2026 4:30 PM ESTCompany ParticipantsJames Friedland - Senior Director of Investor ...

At this time, I would like to welcome everyone to Reddit's Fourth Quarter 2025 Earnings Call. [Operator Instructions]. I would now like to turn the conference over to Jesse Rose, Head of Investor ...

The first dimension is the most fundamental: statistical fidelity. It is not enough for synthetic data to look random. It must behave like real data. This means your distributions, cardinalities, and ...

Imagine launching a website that works perfectly in testing, only to watch it struggle or crash the moment real users arrive.

Organizations embracing agents often fail to estimate the costs of testing their output, with the non-deterministic nature of results often leading to complex and expensive evals.