Systems | Development | Analytics | API | Testing

Ask one question with a time range, a metric, a comparison, and a goal. The AI analyst does the gathering. You keep the judgment. To answer any performance question in minutes, ask Databox’s AI Analyst, Genie, one well-built question that includes: a time range, a metric, a comparison, and a goal. Genie queries the data sources you’ve connected, runs the calculation, and returns the answer with a recommendation attached.

Ten reporting platforms compared on current pricing, integrations, white-label options, and AI capabilities, with a clear recommendation for every agency type.

Two dashboards, same portfolio, different occupancy numbers. This is the moment most reporting modernization projects start, and it is usually read as a dashboard problem. The tool gets blamed, a replacement gets scoped, and the divergence survives the migration intact. It survives because it never lived in the dashboard. When occupancy reads 91% on one screen and 94% on another, both tools are usually working correctly. They are faithfully rendering two different calculations of the same concept.

Quick answer: Agentic AI in banking refers to AI systems that don’t just generate text or answer questions – they take a goal, break it into steps, use tools and data sources, make decisions, and complete multi-step workflows (like investigating a fraud case or processing a KYC file) with minimal human intervention, looping in a person only for genuine judgment calls. Walk into any banking technology conference in 2026, and you’ll notice the conversation has quietly shifted.

Cross-platform builds promise rapid delivery through a shared codebase, yet QA teams encounter stubborn regression roadblocks across Android and iOS. React Native connects JavaScript threads to native host views via an asynchronous message channel. When test runners dispatch actions faster than native components finish rendering, automated assertions trigger false alarms without underlying code failures.

Automated document processing, also known as intelligent document processing (IDP), uses artificial intelligence and machine learning to reduce the manual effort required to extract data from paper or digital documents. Why is that vital? As organizations continue to modernize in their workflows, enterprises still need to grapple with enormous volumes of documents, such as invoices, receipts, and contracts. Manual data entry and rekeying of document data creates an operational bottleneck.

Handwritten mocks are cheap one at a time. This series built enough of them to show how quickly that stops being true. Nine posts took one package notifier from a function returning "delayed" to a captured response from a real carrier. Along the way, we hand-authored canned successes, failure cases, a spy, a stateful fake, an HTTP server, response fixtures, and contract-drift tests in four languages.

August was a month of giving teams more control over their own workspace, more room to trust what they automate, and a quieter but real security and reliability pass underneath it. Test cases get a real version history instead of silent overwrites. Project admins get to clean up their own taxonomy instead of filing a ticket. Playwright finally reaches the private networks the rest of the platform already could, and a critical vulnerability in the Katalon Agent gets patched along the way.

It’s back-to-school season, which means millions of Chromebooks are logging back on. ChromiumOS browser testing, now available on Sauce Labs, helps you make sure your app is ready for them.

New BigQuery identity columns let you define columns that automatically generate sequential 64-bit integer values to manage’ unique identifiers.