Systems | Development | Analytics | API | Testing

If you've been following the agentic AI wave, you've probably noticed that the conversation tends to center on the agents themselves: which LLM to use, which orchestration framework to pick, which use cases to tackle first. But a growing body of analyst research is pointing to a different bottleneck, one that's hiding in plain sight: integration. Forrester's David Mooter argues that integration must sit at the center of your AI strategy — not as plumbing, but as a strategic capability.

When a PropTech product struggles, the issue usually isn’t the code. A skilled team can build the wrong thing well: a listing platform on a data model that buckles when the second MLS feed arrives, a CRM that ships to spec while agents quietly stop using it, an MVP that investors, agents, and end users each expected to do something different. That gap between what the business assumes, what users need, and what the architecture can support is what we mean by PropTech product misalignment.

Until recently, we extensively used HashiCorp's CDK for Terraform. Then they announced the end of its development. We didn't want to spend months migrating to a new ecosystem, but we needed a replacement. In the Infrastructure Team at Ably, we like Go. I wondered if there was an opportunity for something simpler.

Debugging Android apps can feel daunting given the sheer size of the ecosystem. However the suite of Android Studio debugging tools allows us to find, fix and prevent problems across our entire user base. Wherever they are, whichever devices they’re using. This guide will give you a practical workflow to maximize the functionality of Android Studio, and empower you to debug Android apps from first breakpoint to production issues.

FastAPI has become one of the most popular web frameworks among Python developers. It's so popular that it often overshadows the technologies it's built on. FastAPI is built on Starlette and Pydantic. Starlette handles the HTTP layer (routing, middleware, WebSockets, the ASGI plumbing) and Pydantic handles data validation. FastAPI is the layer on top that ties them together with type-driven parameter parsing, dependency injection, and automatic OpenAPI documentation.

Shared state is a hard problem in the unglamorous sense: the concepts are well understood, but someone still has to build the conflict resolution, the reconnection handling, and the state recovery before anyone can ship the feature that depends on it. That's what LiveObjects is for, and the path-based model that reshaped the API in JavaScript is now available to Java developers too.

If you've ever updated a dependency, scanned your project for vulnerabilities, or opened a GitHub Security Advisory, you've probably seen something like CVE-2025-13465. For many developers, it's just another identifier attached to a security warning. Easy to ignore, especially when your priority is simply getting your application shipped. But that small reference represents something much bigger: it's the common language the software industry uses to identify and communicate security vulnerabilities.

As APIs grow across teams, keeping them consistent becomes difficult. Some APIs follow naming conventions and include clear descriptions, while others don’t. Over time, these differences make APIs harder to understand, review, and maintain. That is where API linting helps. That made it possible to apply custom Spectral rules as part of local development, Git workflows, or CI checks.. Teams can now upload and manage custom Spectral rulesets directly from the Insomnia UI.

Production AI no longer generates one kind of traffic. It generates four patterns, and most teams govern only one. **AI traffic management** starts with a single decision: **proxy AI traffic** through one control point instead of letting it flow straight from application code to model providers. Skip that step and security teams have no policy chokepoint, token spend climbs with no meter, and every new provider adds an integration nobody owns.