Systems | Development | Analytics | API | Testing

A customer opens a banking app at 9:02 AM to check a failed payment. The balance looks wrong. Support says, “It’s a system delay.” The transaction finally reflects several hours later. That’s not a UX problem. It’s an architecture problem. Traditional banks still run on tightly coupled, monolithic systems designed for batch processing, not real-time expectations. But customers today compare banking experiences to Google Pay or Apple Pay, not legacy core systems.

Picture a developer pair-programming with an AI assistant. The model returns a function that almost works. The developer asks it to try again. The second attempt is worse. They want the first one back. In a linear chat, that history is gone, or it's a third bubble in the thread that pollutes context for every future turn.

Most real estate and brokerage platforms no longer suffer from a lack of data. They have the opposite problem – too much of it. CRM systems, MLS feeds, listings data, transaction records, marketing performance, and third‑party real estate datasets are all available and constantly growing. But having access to data is no longer the challenge.

Data masking comes in different forms: dynamic vs. static masking. Each has its own characteristics, use cases, and methods for data protection. But when it comes to comprehensive, consistent protection, static data masking rises above. In this blog, we’ll break down where dynamic data masking works, how it fails, and which use cases you need to use static masking for.

Take any AI agent demo from the last six months. It works. Now ship it to real users on real networks, real devices, real attention spans. A meaningful share of those users will never finish their first conversation cleanly. Not because the model gave a bad answer. Because the connection dropped, the tab refreshed, the phone took over from the laptop, or the spinner kept spinning forever.

UIKit is Apple’s primary framework for building user interfaces on iPhone and iPad. If you’ve read that it’s about to be deprecated, don’t believe the reports. In 2026 UIKit remains as integral to production apps as it’s ever been. In this guide we’ll focus on how UIKit actually works. The lifecycle timing, the navigation structure, the memory management and (our favorite) the production debugging. You’ll find it useful if you’re.

At Ably we recently shipped AI Transport, our drop-in transport layer for streaming LLM output over Ably channels, with all the resumability, multi-device continuity, and handover guarantees that implies.

API managers are often used to expose REST APIs to an internal or external group of developers. An internal group of enterprise developers might use a catalog of API services as a starting point for software development projects. An external group of partners might use the APIs for integration with their backend systems. In some cases, partners might use the APIs to purchase goods, services, or information.

Python is a robust and powerful programming language. In addition to machine learning, Python can be used for tasks such as web scraping, image processing, scientific computing, and much more. A framework such as Django, which is built on top of Python, enables you to build beautiful web applications—top websites such as Dropbox, Instagram, and YouTube use Django.