Systems | Development | Analytics | API | Testing

A Head of Product at a major sportswear retailer has a brilliant idea: let’s build an app that sales staff on the shop floor can have on their tablets, and ask their questions there and then, where they serve customers. They set about building. In order for the app to answer questions, it needs to have the information from the 2026 Spring/Summer Catalogue, a mammoth manual, let’s say 300k tokens.

This fall, Tosca’s suite of AI capabilities will be available for on-premises deployments — giving teams the tools to meet the pressure of the AI era head-on. Customers will gain access to leading AI features, including Agentic Test Automation, AI-powered test creation, MCP connectivity, and AI Workspace, no matter their deployment type or existing infrastructure. AI’s unprecedented development means testing practices that were standard a year ago could already be outdated.

For humans and agents alike to use data effectively for future workloads, they need unified context, reduced friction, and flexibility.

The N|Solid Extension and open-source N|Solid Plugin bring real Node.js runtime context into code editors and AI coding agents, helping developers investigate production issues, improve performance, and validate changes without breaking their workflow.

Services firms run on a specific financial rhythm that traditional business dashboards were not built for. Revenue is lumpy because invoices land when clients pay them, not when work is delivered. Cash flow depends on which clients are current, which are 45 days overdue, and which have quietly stopped billing altogether.

Xcode debugging tools integrate smoothly with the rest of the Xcode ecosystem and offer myriad benefits to developers, including powerful breakpoints and source-level visibility. This guide will help you unlock them. We’ll equip you with the knowledge to: If you’ve come for a specific piece of knowledge, here’s the full list of contents so you can go straight there.

From identifying targets with proprietary market data to closing on time and separating cleanly in a divestiture — having all of your data in one place can make or break your M&A.

Enterprise QA teams are discovering that deploying machine learning models breaks their existing validation pipelines. Legacy testing environments rely on a simple truth: fixed inputs must produce predictable outputs. Because intelligent architectures operate on probabilistic distributions, deterministic testing alone can no longer guarantee reliability. When conducting a code review or architectural risk assessment, treating an active model as a standard black-box API leaves critical flaws unaddressed.

MCP servers have quietly become production infrastructure. If you have shipped a Model Context Protocol server so that Claude, an agent, or a connector can call your tools, that endpoint is now on the critical path. When it goes down, or starts returning errors, your users feel it the same way they would feel any API outage. The difference is that most teams are not watching it yet.

You build a data pipeline from natural language by describing the source, destination, and required transformations in plain English to a platform with a prompt-to-pipeline feature, which then generates a draft pipeline with inferred field mappings, transformations, and a schedule for you to review and adjust. This guide is for operations teams, data analysts, and junior team members who understand the desired outcome of a migration but don't write SQL or Python.