Systems | Development | Analytics | API | Testing

With 34% of software failing to meet quality standards, and 77% of AI failures significant enough to damage user trust and product integrity, new research highlights why global brands must pair AI orchestration with expert human insight.

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.

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.

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.

Learn how to manage column-level access controls in BigQuery using IAM data governance tags, ensuring secure, scalable data classification and protection.

Seamlessly chain Python and SQL in Jupyter notebooks using BigQuery DataFrames and the %%bqsql cell magic. Learn how in this step-by-step guide.

The world of banking is shifting. For a long time, cool consumer banking apps made all the headlines. But today, the biggest change is happening in the business world. Recent financial reports from firms like McKinsey and Boston Consulting Group show that small and medium enterprises (SMEs) spend over $850 billion a year on banking fees and loans. Because of this, specialized neobanking for SMEs has quietly grown into a massive $100 billion market opportunity in 2026.

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.

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.

As enterprises race to adopt AI, weak data foundations are preventing more than half (58%) of organizations in the United States and Canada from realizing value and contributing to an estimated $108 billion in wasted global AI investment each year, according to a report from Hitachi Vantara. The reason is rarely bad models or lack of ambition.