Systems | Development | Analytics | API | Testing

When Everyone Builds Everything, the Moat Moves to the Data Layer

Copying a PropTech feature set used to take real time and money. It doesn’t anymore, and that changes what makes a product defensible. Property management systems and point solutions are converging on the same capabilities, and the overlap between EliseAI and Funnel earlier this year showed how quickly two platforms in separate lanes can end up competing head-on.

10 Best API Monitoring Tools in 2026 (Free and Paid)

Is Your Infrastructure Ready for Global Traffic Spikes? Unexpected load surges can disrupt your services. With LoadFocus’s cutting-edge Load Testing solutions, simulate real-world traffic from multiple global locations in a single test. Our advanced engine dynamically upscales and downscales virtual users in real time, delivering comprehensive reports that empower you to identify and resolve performance bottlenecks before they affect your users.

Your Multi-Agent System Is Only as Reliable as Its Context Layer

You've mapped the architecture. You know your agents need to retrieve context from external tools, coordinate with other agents, and propagate mutations through your systems. The model logic is solid. What you haven't fully solved is what sits between those agents and everything they're trying to reach. That's the gap Kong was built to close. Multi-agent workflows live and die on context.

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.

Confluent Cloud for Apache Flink: Engine for Mission-Critical, Real-Time Operational Systems and dbt/SQL-Native Home for Data Science and AI

Organizations today are under immense pressure to deliver on two critical fronts: building mission-critical, real-time operational systems and powering the next generation data science and artificial intelligence (AI) workflows with analytics-ready data. Historically, achieving both meant navigating a divided, complex architecture.

New in Confluent Intelligence and AI Tools: Making Agents Native to the Stream, Expanded Model Support, New Agent Skills, and Copilot

A customer writes in asking where their order is. The AI support agent checks the account, sees the order marked shipped, and sends a reply. The order was cancelled forty minutes ago. The agent wasn't wrong about anything it could see. It was reasoning over stale data that refreshes every six hours. This is why AI projects stall. Not because the models aren't capable, but because they lack AI-ready data and a reliable view of the current state of the business.

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.