When AI Chatbots Fail: What Testing Really Reveals
AI chatbots now sit in customer journeys, product workflows, help centers, and decision paths. They represent brands, influence user trust, and increasingly, they make autonomous judgments.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Stop Subsidizing Innovation, Start Monetizing It
The ‘AI Credit’ Economy: GitHub’s Pricing Shift Is the Beginning, Not the Exception *GitHub just sent waves of budget panic across its developer base. Seat-based Copilot pricing is out. Consumption-based credits are in. And if you're building an AI-driven product today on flat-rate pricing? You're building a problem into your roadmap.* Seats aren't going away, but they now fund a shared pool of AI credits (one credit = one cent) instead of unlocking uncapped use.
Introducing Kafka Skills for AI Engineering Agents
If you've written a line of code in the last 18 months, you already know this. Tools like Claude, Codex, Bob, Kiro and Cursor have made agentic software engineering the default. Most developers today are writing prompts as much as they are writing code. That shift changes what ‘developer experience’ means. Clean UIs, useful tools and good docs are still the foundation but the focus has shifted to ensuring a coding agent actually knows what it is doing, in the hands of a developer.
Lenses MCP Server with OAuth 2.1
You can now drive Lenses from Cursor, VS Code, IBM Bob or Claude Code without running any extra piece of infrastructure locally. Lenses MCP offers secure tools across topics, schemas, Kafka Connect, SQL processors, consumer groups, datasets and pod logs: everything an engineer would normally click through in the Lenses UI, now reachable from any MCP-compatible client over HTTP.
Rust Demo: Rust and C, C++ Mixed-Language Support in Perforce QAC and Klocwork
In this demo, see how Perforce Static Analysis quickly and efficiently scans Rust code to detect security issues beyond memory safety. We also show you how the cross-language capabilities in QAC and Klocwork enable the analysis of mixed Rust, C, and C++ codebases.
3 Reasons React Native Teams Use OTA Updates
Hotfixes. Continuous iteration. Controlled rollouts. You only need one of these to change how you ship.
Why Your AI Strategy is Breaking: The Power of AI Anywhere with Cloudera
Hey, did you know AI can’t be confined to just one environment? AI is moving faster than ever, but it cannot be confined to a single environment. From the public cloud to on-prem data centers and out to the edge, AI is everywhere. However, when these environments remain siloed, your data strategy breaks—leading to inconsistent governance and scaling roadblocks. In this video, discover the vision of AI Anywhere. To unlock real business value, AI must operate exactly where your data lives, maintaining the same level of control, trust, and security across every platform.
Why Simplified Test Script Creation Is the Future of Load Testing Efficiency in 2026
For many QA teams, the real challenge in load testing isn’t infrastructure – it’s the complexity of legacy, code-heavy test scripts. Over time, the drive to add more scripting features has created a tangle of logic that slows teams down and limits what can be tested efficiently. While advanced scripting offers flexibility, it often comes at the expense of time spent on setup, fragile scripts, and mounting technical debt.
You're not doing AI transformation. You're doing AI decoration.
Every enterprise AI story right now follows the same plot. You pick a system — Salesforce, Workday, SAP, NetSuite — and you bolt an AI agent on top of it. The agent can summarize deals. It can write follow-up emails. It can pull a report without you clicking through five dashboards. It is genuinely useful. And it is not transformation. What you have built is a smarter interface on top of a system designed for humans.
AI in Banking: Use Cases, Architecture & Implementation - The Complete Guide for Financial Institutions (2026)
AI is already embedded in banking systems. The question is whether it’s delivering measurable outcomes or just adding another layer of complexity. Across the industry, investment is not the constraint. Banks spent over $73 billion on AI in 2025, yet most initiatives haven’t translated into production-scale impact. Nearly 95% of generative AI programs remain in pilot mode, and only a small fraction of institutions report clear ROI. The pattern is consistent.