Systems | Development | Analytics | API | Testing

The AI Code Verification Crisis: Meet AURA, the platform built to solve it

AI didn't remove the release bottleneck, it moved it downstream. Code volume exploded, verification didn't. The old QA model isn't broken, it's outgrown: 80% of engineering teams have already traced a production incident to AI-generated code. AURA is Sauce Labs' answer, the only full-lifecycle release assurance platform built to close the gap. It continuously verifies every release against business intent, authoring, running, and regenerating tests in an autonomous learning loop, with humans in control.

How Does Tier 2 SOC Automation Work?

Tier 2 SOC work picks up evidence gathering across consoles, containment decisions, sandbox detonation and verdicting, sweeping new indicators through historical data, and the case documentation and handoff that follow. Tier 1 work is linear enough to enumerate, so a playbook can list the steps. Tier 2 investigations branch, since each answer changes the next question, and no engineer can pre-write every path and that is why SOAR does not do well in Tier 2 even in teams where it works well at tier 1.

Build Custom, AI-Ready API Endpoints Without Writing Backend Code

Auto-generated APIs changed how fast teams ship. Point DreamFactory at a database and you get a complete REST API in seconds: every table, full CRUD, live documentation, role-based security. For thousands of teams, that is the whole job. But auto-generated APIs mirror your schema. Your applications, and increasingly your AI agents, want something more deliberate: clean paths, shaped responses, and endpoints that match how the consumer thinks rather than how the database is laid out.

Where AI Delivers Real ROI in Brokerage and Listing Platforms

Two AI features can cost the same to build and land on opposite sides of the P&L. Add a chatbot to a listing page, and you get a support-deflection number that plateaus in a quarter. Rework search ranking so a buyer who types “quiet street, near a school, room for an office” gets the right ten homes instead of 400 filtered results, and you move search-to-contact conversion, which sits at the top of every revenue metric downstream.

SmartBear MCP for Zephyr: Connect your testing system of record to your AI tools

Your SmartBear Zephyr test data holds the answers you need before you ship: what’s covered, what passed, where the risk sits. That data has always lived one context switch away, behind the Jira UI. The SmartBear MCP Server changes that. It brings your Zephyr test data into any MCP-compatible AI client, so quality keeps pace with how fast your team builds. This guide covers where testing sits in the AI age, what MCP is, and how it unifies data visibility within your Zephyr workflow.

Top Challenges of Interoperability in Healthcare and How AI Is Helping Solve Them

Healthcare interoperability enables clinical and administrative systems to exchange usable patient information. However, connectivity alone does not ensure accurate interpretation or workflow compatibility. Many of the challenges with interoperability in healthcare have less to do with moving data and more to do with whether the receiving system understands what that data means. FHIR standardizes healthcare data exchange through structured resources and implementation frameworks.

Real-Time Fraud Detection with Edge-to-AI | Cloudera Data in Motion Demo

Learn how to build an end-to-end, real-time edge-to-AI data pipeline to tackle critical enterprise challenges like credit card fraud detection. In this demo, Diby Malakar (Product Lead for Data in Motion) demonstrates how to process an average of 5,000 transactions per second in low hundreds of milliseconds to detect fraud instantly. Discover how Cloudera’s Data in Motion suite enables application developers to ingest, govern, and enrich streaming edge data to power instant AI model inference and live analytics.