Systems | Development | Analytics | API | Testing

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.

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Whether it’s using a variable that was never declared, making up functions that aren’t part of a class, using code from outdated packages, or misdiagnosing an issue, code generation models can create problems.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 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.

Operationalizing Agentic AI with Hitachi iQ Studio and NVIDIA Nemotron 3

NVIDIA just announced NVIDIA Nemotron 3, a new family of open models, datasets, and libraries designed to support long-context reasoning and multi-step AI workflows. With the ability to work across enterprise ecosystems, this family of models empowers enterprises to build and deploy reliable multi-agent systems at scale, offering an important set of technologies at a pivotal moment in AI evolution.

7 RAG Evaluation Tools You Must Know

RAG evaluation measures how effectively a system retrieves relevant context and uses it to generate grounded answers. These evaluations detect hallucinations, measure retrieval precision and reveal where pipelines degrade after model updates or knowledge-base changes. Engineers rely on these tools to maintain output quality, prevent regressions, validate prompt and architecture choices and ensure that production answers stay aligned with trusted sources.

Replit vs Cursor : Which AI Coding Platform Should Developers Choose?

In an age where software developers are speeding up their code development to meet the demand of rapid application deployment, there are new tools being developed based on Artificial Intelligence (AI) technology. Replit and Cursor have received a lot of excitement for both of these platforms due to their use of artificial intelligence in assisting developers with coding.

Meet the New BI A-Team

Talk to anyone who works with data, and you’ll hear a familiar story: Data engineers are still bogged down cleaning, prepping, and untangling semantic models. Analysts are churning out dashboard after dashboard, with little time left for real analysis. Developers are hand-coding embedded analytics, turning every new feature into a months-long project. And business users are stuck in line, waiting for answers.