Systems | Development | Analytics | API | Testing

Federal agencies must perform a high-stakes balancing act: Modernize legacy systems, break down data silos, and deliver real-time citizen services—all while operating under strict security and compliance requirements with constrained budgets and staff. Today, we're announcing that Confluent Cloud for Government (CCG) is now available on the FedRAMP Marketplace, with FedRAMP Moderate authorization achieved through the competitive FedRAMP 20x Pilot program.

In the burgeoning intelligence economy, AI tokens are a metered utility, but enterprise profitability now hinges on a critical distinction: output tokens can cost up to 10x more than inputs, creating a new, invisible risk for cost overruns, particularly with Agentic AI. Learn how Kong AI Gateway and Konnect Metering & Billing provide the essential financial control plane to enforce directional guardrails, protect margins, and turn token consumption into realized revenue.

Your WireMock stubs are lying to you. They were accurate when someone wrote them six months ago, but the payment API added a metadata field in January, the inventory service switched from REST to gRPC in February, and nobody updated the stubs because the tests still pass. Meanwhile, production is breaking in ways your mocks will never catch. This is not a WireMock problem. It is a hand-written mock problem.

Whether you’re building your first iOS app or improving user engagement, understanding push notifications is essential. They’re how apps stay connected with users, delivering updates, reminders and alerts even when the app itself isn’t running. In this guide, you’ll learn how to implement iOS push notifications from setup to debugging, using Swift and Apple Push Notification Service (APNs) in a real app.

Data infrastructure growth has a direct, measurable relationship with energy consumption. As organizations ingest more events, retain more data, and deploy more always-on services, infrastructure energy use increases—often faster than business value. For streaming systems, this effect can be amplified by long-running clusters, peak-based sizing, and duplicated pipelines. Sustainability in this context is not about environmental reporting or corporate commitments.

Astera Centerprise is already one of the most AI-forward data platforms available. Its built-in agentic AI creates data models, builds ETL/ELT pipelines, generates source-to-target mappings, orchestrates workflows, prepares data, and deploys schemas to production, all through natural language. You describe what you need; the AI uses real Centerprise tools to build it.

AI has completely reshaped the boundary between human imagination and what’s possible. Along the way, AI use in business has become mainstream, with software delivery among its top adoption areas. In 2026, leading global technology companies are now using AI to generate the majority of their code, with some development teams reporting that they haven’t written code manually in months.

Building AI agents that work reliably in production requires solving problems that have nothing to do with AI. While teams focus on prompt engineering, model selection, and agent orchestration, a different class of challenges emerges at deployment. These have little to do with LLMs and everything to do with keeping agents and clients synchronized in realtime. Over the past few months, we've spoken with engineers at over 40 companies building AI assistants, copilots, and agentic workflows.

Today marks a critical step forward for enterprise Node.js. In partnership with the OpenJS Foundation, NodeSource is launching a Node.js LTS Upgrade & Modernization program to provide companies with a secure and streamlined path to migrate business-critical applications off legacy and End-of-Life (EOL) Node.js versions and onto the latest Long-Term Support (LTS) releases.

The AI coding and Agent Harness approach is no longer experimental. This is likely the most impactful agentic AI use case in production today, and Claude Code is one of the solutions really leading the charge. But as engineering teams race to adopt Claude Code across their organizations, a critical question emerges: who's governing all that LLM traffic?