Systems | Development | Analytics | API | Testing

Foundation First: Why Model-Agnostic Data Platforms Win

In 2024, two of the largest data platform companies, each with billions in revenue and dedicated AI research teams, invested in building their own foundation models. One spent roughly $10 million training a 132-billion parameter model on 3,072 NVIDIA H100 GPUs. The other released a 480-billion parameter model optimized for enterprise tasks like SQL generation and code. Both achieved strong results within their compute class.

ThoughtSpot June Release: Customize Your Agent

Check out what’s new in ThoughtSpot’s latest release! SpotterModel gets smarter: Build complex data models with AI formula suggestions and instant version rollbacks if you make a mistake. No stress, no lost work. Spotter Instructions: Fully customize Spotter’s persona, formatting rules, and strict guardrails. It says exactly what you want it to say—and nothing it shouldn't. Ad Hoc Analysis: Drop local files directly into Spotter for instant answers, or blend them safely with your governed enterprise data.

Stop testing everything: How Tricentis SeaLights tells you exactly what to test

Most teams don't know which parts of their code have actually been tested, and which haven't. That gap is where defects escape. Tricentis SeaLights is a quality intelligence platform that gives engineering and QA teams real-time visibility into test coverage. It maps every code change to the tests that cover it, surfaces exactly what's tested and what isn't, and recommends which tests to run and which to skip across unit, regression, integration, manual, and end-to-end tests.

Introducing AI Test Prioritization and New AI Capabilities for Smarter Testing in Jira

With the release of Xray Cloud 15.0.0, Xray expands its latest AI capabilities with the introduction of AI Test Prioritization, joining two recently released features: Xray's Rovo Test Plan Summarizer and AI-generated Manual Scripts for Test Case Designer, introduced in Xray Cloud 14.0.0. Testing is rarely just about executing test cases. Teams need to understand where risk exists, how testing is progressing, and whether a release is ready to move forward.

Safeguarding Multi-Brand E-Commerce: Architectural Quality Engineering for Enterprise Scale

When you operate a digital commerce ecosystem across multiple international borders, processing thousands of concurrent checkout events for over 70 global brands, the standard concept of "QA" completely breaks down. Most corporate discussions treat software validation as a simple pre-release checklist, a final mechanical hurdle before a deployment goes live.