Systems | Development | Analytics | API | Testing

Have you ever questioned why your most-used apps do not crash or slow down and yet run well? Why are some companies always able to produce great user experiences while others encounter bugs and security vulnerabilities? In San Francisco, the epicenter of technology innovation, leading software testing firms play a vital part in making certain that the software we use is robust, secure, and trustworthy. As we head into 2025, selecting the ideal software testing partner has never been more critical.

Outsourcing is an affordable way to scale regression testing, freeing up developers to focus on shipping code. However, we’ve surveyed engineering leaders and developers and found that outsourcing regression testing to the wrong service provider can lead to several issues: As a result, we recommend evaluating how a regression testing service provider addresses these three issues before partnering with them.

Imagine you received an urgent Slack notification that bypassed your notification snooze. Your stomach drops as you realize there is a critical problem with your application. The next few hours are not going to be fun. Uptime and high performance are key elements of a successful application. If users can’t effectively get what they need from your app, they’ll quit and find an alternative.

Imagine workflows so intelligent they can adapt to changing conditions, solve problems autonomously, and collaborate seamlessly across teams – all while freeing up your time for the tasks that truly matter. This isn’t science fiction; it’s the promise of agentic workflows. As the software development world races to keep up with evolving demands, agentic workflows represent a revolutionary leap, offering a smarter, faster, and more adaptive approach to managing complexity.

Video marketing is pretty much a requirement today. 91% of businesses made it a part of their content mix in 2024 and 90% reported a positive ROI. But you must be artful and strategic about how you use it. Like the purpose of other forms of content, video supports a larger objective for your brand and it can be used to refine your content strategy too. This guide digs into just that: how to use video analytics to improve your content strategy.

When a new feature rolls out, the last thing any team wants is to deal with unexpected bugs slipping through testing.

With the advent of modern Large Language Models (LLMs), Retrieval Augmented Generation (RAG) has become a de-facto technology choice, employed to extract insights from a variety of data sources using natural language queries. RAG combined with LLMs presents many new possibilities for integrating Generative AI capabilities within existing business applications, specifically opening up many new use cases within the data streaming and analytics space.

A report by Bloomberg Intelligence projects the AI industry will reach $1.3 trillion by 2032, with proprietary data fueling much of this growth. As businesses increasingly adopt generative AI (genAI) to enhance efficiency, data is rapidly becoming one of the most valuable assets in the digital economy. Foundational AI models require vast amounts of data for training, and many AI products are now leveraging proprietary datasets alongside these models to power innovative applications and AI agents.

Financial services companies are leveraging data and machine learning to mitigate risks like fraud and cyber threats and to provide a modern customer experience. By following these measures, they are able to comply with regulations, optimize their trading and answer their customers’ needs. In today’s competitive digital world, these changes are essential for ensuring their relevance and efficiency.

Picture this: You’ve just deployed a new feature at work, and you’re buzzing with excitement. But suddenly, you hear whispers of a critical bug in the old functionality. That gut-wrenching moment reminds us how important it is to address regression testing. In this post, we’ll delve into the legacy views of regression testing, explore its modern implications, and highlight why risk management is the real MVP in ensuring robust software development.