Systems | Development | Analytics | API | Testing

Understanding The Differences Between Windsurf And Cursorai

In 2025, AI-powered coding platforms have rapidly moved from "nice to have " to an important part of modern developers. The tools that caught everyone’s attention in these are Windsurf and Cursor. It is hard to choose between the Windsurf vs cursor – they are mostly similar. They are both IDEs (integrated development environments) which are mostly developed for the vibe coders, i.e. non non-coders who build apps for fun and work.

Blueprint for Enterprise GenAI: Governance, Gateways, and Guardrails

Generative AI is transforming how businesses operate, with 74% of enterprises already deploying it in production by 2025. The technology offers measurable benefits like a 1.7x ROI and cost reductions of 26–31% in key areas like supply chain and customer operations. But with rapid adoption comes serious risks - data breaches, AI bias, and compliance issues are top concerns.

AI Gateway Benchmark: Kong AI Gateway, Portkey, and LiteLLM

In February 2024, Kong became the first API platform to launch a dedicated AI gateway, designed to bring production-grade performance, observability, and policy enforcement to GenAI workloads. At its core, Kong’s AI Gateway provides a universal API to enable platform teams to centrally secure and govern traffic to LLMs, AI agents, and MCP servers. Additionally, as AI adoption in your organization begins to skyrocket, so do AI usage costs.

From AI to ROI: The Case For Insurers

Insurers are facing tighter margins, rising costs, and pressure to modernize, which are all challenges that traditional levers alone can no longer solve. Amidst the struggle, Generative AI offers a breakthrough. With the potential to add $2.6 to $4.4 trillion annually to the global economy, which is higher than the UK’s GDP, it can redefine how insurers create value.

Performance Under Pressure: Benchmarking DreamFactory's Gateway for RealTime AI

DreamFactory’s API Gateway is purpose-built for handling the demanding workloads of real-time AI applications. Unlike traditional API gateways, it delivers high-speed performance, robust security, and efficient data management tailored for AI-specific needs. Key results from benchmarking demonstrate its ability to handle thousands of requests per second, maintain sub-100ms response times, and ensure 99.9% uptime - even under heavy traffic.

Monitoring MCP Security and Agent Behavior with Moesif

The Model Context Protocol (MCP) has pioneered a new interface layer between AI agents and tools. It has become easier to enable seamless access to external services, APIs, workflows, and data with natural language. MCP servers are now powering the decentralization of AI intelligence and orchestrating the interplay among modern AI systems. In doing so, they also introduce a more open, fluid, and automation-driven attack surface. However, traditional API security models weren’t built for this.

AI in exploratory testing: from hype to practice

AI is more than just a buzzword now - it's becoming an integral part of various processes, including software testing. But how effective is it really, especially when applied to the dynamic nature of Exploratory Testing? In this webinar, Sérgio Freire stated that he recently experimented with leveraging AI in his own Exploratory Testing sessions and discovered both promising applications and significant limitations.

ChatGPT Made AI a Tool for Everyone - Now Data Infrastructure Needs to Catch Up

When ChatGPT entered the mainstream, it didn’t just change how people use artificial intelligence — it changed who gets to use it. By abstracting away the complexity and making the interface simple and intuitive, OpenAI opened the floodgates. Now, instead of AI being the exclusive domain of engineers and data scientists, it’s being actively explored by product managers, marketers, revenue operations leaders, and customer experience teams.

ETL for LLMs to Build Context-Rich Pipelines for Generative AI

Large Language Models (LLMs) like GPT-4, Claude, and LLaMA have reshaped the way businesses think about intelligence, automation, and human-computer interaction. But the performance of an LLM hinges entirely on what powers it: data. And that data must be systematically collected, cleaned, enriched, and delivered—a task owned by the ETL (Extract, Transform, Load) pipeline.