UK bosses can't make decisions without AI
New Confluent research reveals that millions of UK bosses have handed off key decision-making responsibilities to AI.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
How to make APIs AI-ready | Automating reviews with Swagger Studio & Spectral
As AI agents increasingly interact with APIs, design clarity and structured metadata matter more than ever. In this focused demo, Senior Solutions Engineer Mairtín Conneely take us through how to use Spectral rulesets in Swagger Studio to automatically enforce AI-ready API design standards across your OpenAPI definitions. This video covers:What “AI-ready” API design meansCreating custom Spectral rulesImporting governance rules into Swagger StudioRunning automated AI-readiness checksScaling API quality with governance automation.
Ep 63 | Open Lakehouse Architecture: How to Scale AI to Production
Open lakehouse architecture is becoming the foundation for production AI and enterprise AI at scale. In this episode of The AI Forecast, Dipankar Mazumdar, Director of Developer Relations at Cloudera and co-author of the book “Engineering Lakehouse with Open Table Formats,” joins Paul Muller to explain why open lakehouse architecture is critical for moving from AI pilot to production AI.
Kong Insomnia Named in Gartner's Market Guide for API and MCP Testing Tools
We’re proud to share that Kong Insomnia has been recognized as a vendor in the Gartner Market Guide for API and MCP Testing Tools in February, 2026. In a rapidly evolving landscape where AI-driven integration and MCP servers are reshaping how APIs are built, tested, and consumed, being recognized by Gartner validates what our community and customers already know: Insomnia is a serious, enterprise-ready platform for modern API development and testing.
Is Prompt Engineering Dead? Here is the Reality... #promptengineering #generativeai #llm #ai
Remember the ‘Prompt Engineer’ hype? It’s died down, but prompts are still the heart of AI. They shape an LLM’s personality and focus by guiding its vast knowledge. Curious how these system prompts fit into complex, multi-agent enterprise systems? Listen to the complete breakdown in *EP 19: Demystifying Agents*
With AI coding, the delivery pipeline is the new bottleneck - and we already solve it
For fifty years, the hardest part of software was writing it. That's no longer true. In 2025, AI coding assistants went mainstream — 90% of developers now use them (DORA 2025). Then came background agents: autonomous systems that take a ticket, write the code, run the tests, and open a pull request while the engineer sleeps. Stripe merges over 1,000 AI-written PRs per week. Ramp reached 30% AI-authored PRs within two months. Spotify has merged 1,500+ agent-generated PRs into production.
How ClearML Helps Optimize Resource Allocation Across AI Workloads
Author: Adam Wolf Efficient resource allocation is a foundational requirement for scaling AI workloads, particularly as organizations move from isolated experiments to shared infrastructure supporting multiple teams, models, and environments. GPUs, CPUs, and high-performance storage are costly and finite, and without coordination, utilization often degrades as usage grows.
How to Calculate Measurable Returns from AI Spend?
AI isn’t just some side project anymore. These days, it’s a real budget line for big companies, something boards talk about all the time. Global investment in AI is about to break $300 billion a year. McKinsey says AI could add up to $4.4 trillion to the economy every year. That’s huge. But even with all this promise, a lot of businesses still have trouble figuring out if their AI projects are actually paying off. That’s the spot most CXOs are stuck in now.
7 things engineering teams get wrong about AI-powered QA
We’ve all been there. When engineering teams evaluate AI-powered QA tools, the same questions come up again and again. Some are rooted in genuine technical curiosity. Others stem from experiences with earlier-generation tools that earned a healthy dose of skepticism. After hundreds of these conversations, I’ve identified the seven most common misconceptions. Contents Toggle.
Agentic Payments: Redefining the Future of Payments for Enterprises
Enterprise payment systems are at a breaking point: rising volumes, tighter margins, and ever-more sophisticated fraud are pushing traditional automation to its limits. The AI-enabled payments market was valued at $38.36 billion in 2024 and is projected to grow over the next decade. As firms seek smarter, real-time decisioning and risk control, highlighting how indispensable AI has become in payment stacks today. -