Systems | Development | Analytics | API | Testing

Confluent has been instrumental in enabling customers from various industries to develop real-time stream processing solutions using Apache Kafka. While many of these use cases demand low-latency and real-time processing, stream processing is also increasingly being utilized for ingesting logging and telemetry data. This type of data typically features a high ingest rate, but allows for a higher tolerance for end-to-end processing time.

Concurrency is the ability of an app to perform multiple tasks at once, and it’s a crucial concept for apps that need to perform multiple tasks at once in an efficient, usable way. Thankfully Swift has made great strides with concurrency, and now provides simple tools for writing robust apps that are responsive and enjoyable to use. In this article we’ll explore two main ways of using threads for concurrency models.

RAG and Agentic AI shape how intelligent systems interact with data and users. RAG enhances LLMs by retrieving external information to improve accuracy and contextual relevance, while Agentic AI introduces autonomy, decision-making, and adaptability into AI-driven workflows. Agentic RAG combines the power of both, transforming RAG into a multi-step, autonomous, complex process that can self-improve.

In the rapidly evolving landscape of software development, QA teams never stop searching for means to optimize testing efficiency without losing precision. Playwright Model Context Protocol (MCP) has a new paradigm that is revolutionizing automated testing. Playwright MCP fills the gap between Large Language Models (LLMs) and test environments, naturalizing and simplifying QA automation. It is a paradigm shift in how testing is understood within the context of contemporary software development.

Model Context Protocol (MCP), introduced by Anthropic, is a new standard that simplifies artificial intelligence (AI) integrations by providing a secure, consistent way to connect AI agents with external tools and data sources. When we saw MCP’s potential, we immediately started exploring how we could bring real-time data streaming into the mix. With our long history of supporting open source and open standards, building an MCP server was a natural fit.

Notifications muted. Social media logged out. Phone flipped face down. Sounds familiar? Well, in a world buzzing with digital distractions, many of us have tried going off the grid by deactivating apps, turning on airplane mode, or even attempting a full-blown digital detox. But let’s be honest, is that truly sustainable? For years, smartphones have been labeled as the root cause of stress and reduced productivity.

At ThoughtSpot, our mission has always been to empower every decision-maker with instant insights that fuel smarter, faster decisions. Recently, we announced the launch of Spotter, our AI Analyst, which brings AI-powered insights to every user, on any question, and any dataset. This is ThoughtSpot's answer to a growing market of AI agents, and it’s our vision to make AI the new BI.