Systems | Development | Analytics | API | Testing

CORS errors can be a frustrating roadblock during API testing - but they’re fixable. These errors happen when your web app tries to access resources from another domain without proper permissions. Here’s how to tackle them step by step.

In less than three years, gen AI has become a staple technology in the business world. In November of 2022, OpenAI launched ChatGPT, with explosive growth of over 1 million users in just five days, galvanizing the widespread use of gen AI. Over the course of 2023 enterprises entered the experimentation stage and kicked off POCs with API services and open models including Llama 2, Mistral, NVIDIA and others.

Doing things manually on GitLab is okay, but it can get repetitive and time-consuming. That’s where the GitLab Python API comes to the rescue! With it, you can automate tasks like creating projects, branches, or merge requests using just a few lines of Python code. It’s incredibly handy for DevOps, scripting, or personal projects! In this blog, we’ll dive into setting up and using the GitLab Python API step by step.

This benchmark report provides a side-by-side comparison of Google’s Gemini 2.5 Pro and OpenAI’s o1 models in AI-driven software testing. Across both unit test generation (UTG) and API test generation (ATG), Gemini 2.5 Pro demonstrated clear superiority in key areas. In summary, Gemini 2.5 Pro outperformed OpenAI o1 by: OpenAI’s o1 model had strengths in smaller-scale applications, but overall it generated shallower tests and struggled to match Gemini on complex projects.

If you need a tool that can extract, transform, and load data, then Hadoop isn’t the right option for you. That doesn’t mean that you shouldn’t consider making Hadoop a part of your ETL process, though. Adding Apache Hadoop to your process could help increase speeds and ensure that you get accurate results.

This article originally appeared on SD Times on May 30, 2025. We’re sharing it here for our audience who may have missed it. The headlines are seductive: AI will replace developers. Coding is dead. Ship 10x faster with half the team. It’s the kind of hype that grabs attention and fuels confusion.

With an increasingly competitive market, ensuring software quality is essential for your business’s success. QA governance and strategy are crucial to ensure that processes are efficient, consistent, and aligned with business objectives. Managing these factors can be challenging, especially when teams deal with multiple tools, a lack of visibility about test progress, and maintaining compliance.

When searching for ETL pipeline best practices, you will find some common themes: ensuring data quality, establishing consistent processes, and automating out repetitive tasks. There’s a reason these are recommended over and over: they help establish reliable, efficient, and scalable workflows. But one thing that isn’t often emphasized is the importance of implementing consistent, scalable compliance efforts — specifically by using data masking.

Using Business Intelligence (BI) tools can be overwhelming—especially for company executives, shopkeepers, or factory workers who may only engage with them occasionally. This challenge is one of the key reasons we set out to rethink how most users interact with their data. Rather than relying solely on interactive dashboards or static PDF reports, we're exploring more intuitive and user-guided approaches.

ERP systems are the backbone of modern business operations. They support everything from finance and procurement to supply chain and compliance. But when it comes to reporting, even the most powerful ERP systems often fall short. Reports are slow to build, difficult to update, and often require IT support just to answer basic questions. That’s why optimizing your ERP reporting process is no longer optional. It’s essential.