Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Agentic AI: The Shift to Autonomous Software Testing
The landscape of software development is undergoing a profound transformation. We are witnessing a collision between unprecedented development speed and spiraling architectural complexity. According to the 2024 Global DevSecOps Report by GitLab, 69% of Global CxOs report that their organizations are shipping software at least twice as fast as they did a year ago.
Why Most AI Content Gets Ignored by ChatGPT
Using AI to rewrite generic content won’t help you show up in AI search. In this clip, Rick Kranz explains why LLMs ignore repetitive content and what they actually look for when choosing sources to cite.
AI Prediction for 2026
Every technology cycle comes with hype, backlash, and eventually… utility. AI is shaping up to be no different. As we head into 2026, the conversation is already shifting from “AI will replace everything” to “why isn’t this paying off yet?” This shift is heavily influenced by evolving market trends, as businesses and technologists respond to changes in customer behavior, operational patterns, and broader market conditions that shape expectations around AI.
How to Engage AI for Calculating Credit Scoring?
Across the globe, 1.5 billion people remain unbanked, without access to even the most basic financial services. For the rest, fewer than 50% of the banked population qualify for formal credit, limiting both financial inclusion and lending growth. In an era where traditional credit models struggle to assess evolving financial behaviors, AI credit scoring is emerging as a strategic differentiator for banks and fintechs alike.
What Is an MCP Gateway? Key Features and Benefits
API protocols evolve every few years. We have moved from SOAP to REST, then to GraphQL, gRPC, and AsyncAPI for event-driven systems. Now with the rise of large language models (LLMs) and AI agents, organizations need a new class of interfaces that allow agents to take action across real systems, not just generate text. LLMs are powerful reasoning engines, but they lack context. They cannot perform actions by themselves, see real-time data, private information, or internal systems.
SpotterModel: From Manual Wrangling to AI-Powered Architecture
Stop manual data wrangling and start architecting. Meet SpotterModel: your new modeling agent built to deliver instant, AI-ready data. It’s time to move beyond the "join-loop" and let AI handle the heavy lifting. Meet your new modeling agent today.
Why Apache Iceberg & Open Lakehouse is the Foundation for Data & AI Workloads
In this discussion, Dipankar - Cloudera’s Director of Developer Relations sits with Navita - Director of Product Marketing to unpack why Apache Iceberg has emerged as the foundation of the open lakehouse - and why it’s increasingly essential for modern Data & AI workloads. Dipankar & Navita walks through how Iceberg became the de facto standard among open table formats, what it's design enables (interoperability, engine-agnostic access, reliable metadata), and why openness matters as organizations move toward multi-engine, hybrid data architectures.
Why You Should Run AI-Generated Code in a Sandbox
At their best, code generation LLMs reduce cognitive load, accelerate iteration, and serve as a great pair programmer for well-scoped tasks. That said, they also introduce a level of risk. Whether it’s using a variable that was never declared, making up functions that aren’t part of a class, using code from outdated packages, or misdiagnosing an issue, code generation models can create problems.
How SpotterCode Supercharges ThoughtSpot Embeds for Developers
Developers, ditch the documentation dive! SpotterCode is your 10x coding partner for embedded intelligence. ThoughtSpot's Nicolas Rentz shows how SpotterCode leverages the ThoughtSpot SDK docs and code examples to auto-generate production-ready code directly in your IDE. Accelerate your path to market with clean, seamless integration.