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Modern Software Development For Today's Systems & Applications
Best 5 Tools for Monitoring AI-Generated Code in Production Environments
AI-generated code is no longer experimental. It is actively running in production environments across SaaS platforms, fintech systems, marketplaces, internal tools, and customer-facing applications. From AI copilots assisting developers to autonomous agents opening pull requests, the volume of machine-generated code entering production has increased dramatically. This shift has created a new operational challenge: how do you reliably monitor AI-generated code once it is live?
Confluent for Startups AI Accelerator Cohort-2 (November 2025)
Busie Agentic Driver and Vehicle Scheduling: Intelligent assignment of drivers and vehicles to trips using real-time data on driver location and availability, vehicle utilization, and customer sentiment to optimize efficiency, service quality, and outcomes.
Connect Databox MCP to ChatGPT: Chat With Your Data
Imagine asking ChatGPT "How are sales right now?" and getting the actual live number from your business instead of a generic answer. We're going to show you how to install the Databox MCP using ChatGPT's Developer Mode, so your GPT stops guessing and starts tracking your real-time KPIs.
Building the Foundation for Responsible Autonomy: Preparing for the Agentic Era of AI
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.
From Qlik to Quick: How to Transform Qlik Dashboard Analysis With Hidden Insights AI
The gap between having data and getting actionable insights has always been a challenge in business intelligence. Users face dashboards filled with information but struggle to answer critical questions without exporting to Excel, waiting on developers, or missing key trends hidden in their filtered data. But with technological advancements like natural language AI agents, users can access insights and patterns they might otherwise miss.
Hybrid by Design: The New AI Mandate
For the better part of a decade, the enterprise technology mandate was simple: “cloud first,” or more pointedly “cloud only.” Modernizing meant moving to the public cloud, and on-premises architecture was viewed as legacy infrastructure to be maintained until it could eventually be migrated. Fast forward to today, that narrative has shifted dramatically, with AI as the major catalyst.
This Is Bad AI Onboarding (w/ Rodrigo Fernandez)
Asking a few questions and dumping data at the end isn’t AI onboarding. It’s lazy. If AI doesn’t actively personalize the product and get users to value faster, it actually hurts trust. Your users notice and they expect more.
Why AI can't debug your API integrations (yet)
The next generation of debugging doesn’t depend exclusively on the quality of AI models, but it’s heavily dependent on feeding AI tools the context they need to be useful. AI coding assistants have transformed how we write code. For example, GitHub Copilot, Cursor, and ChatGPT can generate Stripe integration boilerplate in seconds. They'll scaffold your payment flow, suggest error handling patterns, and even write unit tests.