Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
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.
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.
The Age of AI Connectivity
Kong was born to connect. The world is shifting from connecting cloud services with apps to connecting LLMs through agents. API calls and tokens are moving in tandem; a new unit of intelligence is forming. As AI traffic explodes into hypervolumes, speed is all that matters. The same principles of performance, security, and reliability behind Kong are essential in an agentic world. A new connectivity layer for AI is born.
Move More Agentic Workloads to Production with AI Gateway 3.13
Kong AI Gateway 3.13 moves enterprises from AI experimentation to shipping production-grade agents by unlocking new capabilities focused on agentic security, developer productivity, and resilience, including MCP tool-level access control, expanded provider support, and smarter load balancing.
2026 Data & AI Predictions: What Trends Will Shape the Future?
We recently released our 2026 Confluent Predictions Report, outlining bold ideas and trends that are shaping the future of data, AI, and real-time systems. And stay tuned for an upcoming episode of the Life Is But a Stream web show that will air early in the new year. Join the conversation as host Joseph Morais sits down with Sanjeev Mohan, independent analyst at SanjMo, for an exciting roundtable discussion breaking down those predictions. Are they forecasts? Are they trends? And which ones will matter most as we move forward into 2026?
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.
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.
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.
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.