Systems | Development | Analytics | API | Testing

Copilot vs Cursor: A Complete AI Coding Assistant Comparison

Coding with artificial intelligence is not just a nice-to-have; AI applications in computer programming are becoming integral to modern computer programming workflows. Presently, two primary applications dominate the discussions in this area: GitHub Copilot and Cursor AI. While both applications provide faster coding times and fewer bugs, fewer bugs, and smarter code, they offer such features in extremely different ways.

The Hidden Cost of 30% AI-Generated Code #speedscale #aicoding #devops #technews #ai

AI now writes 30% of Big Tech’s code, but the resulting surge in defects is crashing platforms like AWS and GitHub. Manual testing can no longer keep up with this velocity; it's time to deploy AI Quality Agents to save our systems. Is AI speed worth the decline in code quality, or are we headed for a breaking point? Let me know if you’ve noticed more bugs in your workflow lately. Video collab with @ScottMooreConsultingLLC.

Spotter 3: Your Smartest Analytical Partner Yet

Spotter 3 is our smartest agent yet. It acts as a true analytical partner that thinks, reasons, and validates its work—all automatically. It blends structured and unstructured data to go beyond traditional data sources, providing a complete picture of the business. With new skills, like Python coding and forecasting, Spotter 3 acts as your AI data scientist. Spotter 3 ensures every question leads naturally to confident, data-backed action.

Frank O''Dowd

AI is reshaping how sales teams find prospects, build relationships, and close deals. Frank O’Dowd, Cloudera’s Chief Revenue Officer, joins to discuss Cloudera’s approach to AI in the sales function. Frank details his philosophy, which is that rather than replacing the human touch, AI is helping sales professionals work smarter, offering insights, personalization, and efficiency at scale. It’s a complementary tool that can help sales teams make themselves relevant to their target audience. As Frank says in the episode, “The person with the most information always wins.”

Best Practices for AI in CI/CD QA Pipelines

AI transforms CI/CD testing from reactive bug detection into proactive quality assurance that accelerates release cycles while improving software reliability. Start embedding AI into your testing workflows now because teams that wait will struggle to match the velocity of competitors who already have. Continuous integration and continuous deployment pipelines have become the backbone of modern software delivery.

Chat with Your Data: The Official Databox MCP

Your AI is brilliant, but it’s blind. Until now. We are thrilled to launch the official Databox MCP (Model Context Protocol). This open standard server bridges the gap between your business data and your favorite AI tools, turning general-purpose LLMs into specialized data analysts that know your business data. Stop manually exporting CSVs or taking screenshots of dashboards. With Databox MCP, you can connect 130+ data sources (Google Analytics, HubSpot, Salesforce, Stripe, and more) directly to tools like Claude, ChatGPT, Cursor, and n8n.

What Is MCP? Connecting AI Across the Software Delivery Lifecycle

AI promises speed and automation — but most teams are still stuck jumping between disconnected tools across development, testing, and operations. In this video, we introduce the Model Context Protocol (MCP) and how it enables AI assistants to securely access tools, systems, and real-time context across the software delivery lifecycle. MCP is the foundation of Perforce Intelligence, allowing AI to: The result: less friction, faster feedback, and AI that works with your existing systems — not around them.

Identity Passthrough for AI: Why Your LLM Needs to Know Who's Asking

When a user asks your AI assistant a question, who actually runs the database query? In most enterprise AI deployments, the answer is troubling: a shared service account with broad access to everything. The user's identity evaporates the moment their request enters the AI system. This architectural pattern creates security gaps, compliance failures, and data leakage risks that undermine enterprise AI adoption.