Systems | Development | Analytics | API | Testing

Over the past two years, generative AI has transformed how we create, learn, and interact. But a more profound shift is already underway—one that changes not just how we work but who (or what) does the work itself. We are entering the era of agentic AI, where systems don’t merely answer questions—they reason, decide, and act on our behalf.

The hardest part about remote work is building real connection and purpose when everyone is not in the same room. At Confluent, we know flexibility is essential, but we also know that great work and a sense of belonging don’t just happen; they take effort. That’s why we’re intentional about how we bring people together, starting from day one.

DreamFactory is a secure, self-hosted enterprise data access platform that provides governed API access to any data source, connecting enterprise applications and on-prem LLMs with role-based access and identity passthrough. During the last 10 years, microservices-based applications have benefited global enterprises by providing them with massive scalability, greater agility, more highly available systems, and improved operational efficiency.

The recently released OWASP, “A Practical Guide for Securely Using Third-Party MCP Servers,” highlights a fundamental challenge in modern AI deployments: how do we govern, secure, and audit systems that are inherently non-deterministic? Unlike traditional, static software, AI agents dynamically adapt their execution paths, tool selection, and decisions based on context and real-time resources, allowing the same agent to achieve identical goals through entirely different approaches.

AI test case generation tools are transforming how QA teams create, maintain, and execute tests by automating repetitive work and improving coverage. Teams that adopt AI for QA now will reduce manual test creation time while expanding their test coverage. Software testing has always been a balancing act between thoroughness and speed. You want comprehensive coverage, but you also want to ship features before your competitors do.

Gherkin syntax transforms software requirements into executable, human-readable test scenarios that both technical and business teams understand. Start with clear, behavior-focused scenarios and your test suite becomes a communication tool that actually gets used. Software teams waste countless hours translating business requirements into something developers can actually build. Miscommunication between stakeholders and technical teams leads to rework, missed deadlines, and features nobody asked for.

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Modern teams need to move sensitive data from on-prem SQL Server into Salesforce safely and predictably. This guide explains how to design, implement, and operate a secure ETL that balances performance with controls. It is written for data engineers, platform owners, and security leads who support regulated workflows. You will learn core components, common pitfalls, architecture patterns, and a phased implementation plan with code examples.

If you’ve recently upgraded to Debian 13 (“Trixie”) or a newer version of Ubuntu and suddenly started seeing security warnings when running apt update (or apt update --audit), don’t worry. You didn’t do anything wrong. This is a side effect of a broader security change across modern Linux distributions. SHA-1 signatures are being deprecated, and repositories that still rely on them may now trigger warnings or audits.

AI now sits inside most software, whether teams planned for it or not. A recent report from Stanford HAI found that AI adoption jumped from 55 percent in 2023 to 78 percent in 2024.