Systems | Development | Analytics | API | Testing

Introducing Advanced AI Observability: See the Whole Agent Journey

Traditional observability was built around requests. A request comes in. A service processes it. A response goes out. You trace what happened in between. That model works well for APIs. It breaks down for AI. With agents, a single user request can trigger multiple model calls. An agent might invoke a tool, call another agent, query an MCP server, retry a model, hit a guardrail, and call another tool before finally producing a response. And all of this happens non-determinstically.

12-Factor Apps: How to Containerize PHP Today

What makes modern web applications easier to deploy, scale, and move between environments? In this highlight reel from the Zend webinar "Think Big, Start Small: A Phased Approach to 12-Factor Web Apps," Matthew Weier O'Phinney explores the principles behind 12-factor applications and why they remain a practical framework for modern application delivery.

Devise for Ruby on Rails: Setup, Modules & Rails 8 Auth

Devise is a full-featured authentication gem for Ruby on Rails, built on Warden. It provides registration, login, password recovery, session timeout, and account locking via 10 composable modules. Install it with bundle add devise, then run rails g devise:install and rails g devise User. It works with Rails 7 and 8; Rails 8's built-in generator covers only simpler needs.

Your Guide to Perforce Autonomous Testing

Software testing is struggling to keep pace with modern release cycles. More code, faster deployments, fragmented tools, and increasing quality demands are creating bottlenecks for QA, engineering, and DevOps teams. Discover how Perforce Autonomous Testing transforms the way teams validate software by bringing functional, performance, web, mobile, and desktop testing together through a unified AI-driven experience. Using natural language, teams can define testing intent, automate execution, orchestrate complex workflows, and gain actionable insights faster than ever before.

How to Update Node.js Versions on Windows

Updating Node.js on Windows can be as simple as installing a new version—but a safe Node.js upgrade is more than changing the number returned by node -v. Prefer to watch the tutorial? Watch the full video on YouTube → A new major version can change the V8 engine, npm, OpenSSL, runtime APIs, native addon compatibility, dependency requirements, and application behavior.

From Excel to Real Estate Investment Dashboards: How to Build a Single View of Your Portfolio

Quick answer: A single portfolio view depends on the data and reporting infrastructure underneath it. Real estate investment dashboards work best when they draw from a governed data layer rather than a stack of monthly Excel exports.

How to Use ChatGPT Codex in Terminal to Build Your First Tool #aicoding #iosdev #aidevelopment

You don’t need a big app idea to try Codex. A messy Downloads folder is enough. In this beginner walkthrough, you’ll ask ChatGPT to write a prompt in plain language, then paste it into Codex in Terminal to build a simple file organizer. You’ll see how to: The file names and folder contents shown are fictional examples. Your first version doesn’t have to be perfect; you can improve it with follow-up prompts.

Introducing Kong Operator 2.3: AI Gateway & More

Today we're announcing Kong Operator 2.3. Building on 2.2's expansion into supporting Event Gateway and Dev Portal, this release significantly broadens our feature set. While 2.2 introduced foundational event-driven capabilities, 2.3 brings Kong **AI Gateway** fully into the Kubernetes-native fold, enabling teams to manage LLM routing and policies through familiar GitOps workflows. This update also rounds out Gateway API coverage with `TCPRoute`, `UDPRoute`, and `GRPCRoute`.

Can AI Agents Safely Debug Production Node.js?

AI agents are getting remarkably good at fixing code. Give an agent a well-defined performance problem, enough runtime data, and access to the relevant codebase, and it can often trace the evidence back to a function, suggest an optimization, validate the change, and produce a useful patch. That workflow is no longer particularly difficult to imagine. In some cases, it is already practical. Production debugging, however, has never been only about fixing the code directly in front of you.