AI Error Fixes & How-To

Security is paramount for any digital platform, and the recent announcements from the WordPress Plugin Directory signal significant changes aimed at improving plugin security and adherence to best practices. With the implementation of mandatory Two-Factor Authentication (2FA) for all plugin owners and committers, coupled with the introduction of the Plugin Check tool, WordPress is taking […]

Google has recently unveiled Jules, an experimental AI-powered coding assistant aimed at assisting developers in resolving buggy code with greater efficiency. Using the capabilities of Google’s latest Gemini 2.0 model, Jules is able to generate multi-step plans, modify multiple files at once, and prepare pull requests for Python and JavaScript directly within GitHub workflows. This

In recent discussions surrounding artificial intelligence (AI), it has become increasingly evident that both businesses and technical professionals should approach the topic with a critical mindset. Kurt Kufeld, Vice President at Amazon Web Services (AWS), recently drew attention to the distinction between true AI and machine learning, stating that even the most complex models currently

Apple’s recent foray into artificial intelligence with the release of iOS 18.1 has uncovered substantial operational challenges, particularly concerning the accuracy of AI-generated notifications. This situation not only raises questions about the reliability of AI systems but also highlights the importance of quick and effective problem-solving in the rapidly evolving tech landscape. In recent weeks,

Implementing AI solutions can significantly enhance operational efficiency and decision-making within organizations. However, as many small and medium-sized businesses (SMBs) venture into the realm of artificial intelligence, they often encounter various data-related challenges that can hinder progress. Initial efforts to remedy these data issues are expected, but persistent and time-consuming repairs can indicate deeper, systemic

In the fast-evolving landscape of software development, automation has become a cornerstone for driving efficiency, quality, and speed. As companies increasingly rely on automated testing to identify and mitigate bugs, it is crucial to understand the common pitfalls associated with this process. Leveraging advancements in artificial intelligence, the sector promises significant improvements in quality assurance

In the rapidly evolving landscape of artificial intelligence, innovative companies face significant challenges in managing their network operations effectively. As organizations increasingly rely on AI workloads, their data center networks must provide not just speed and efficiency but also resilience against common pitfalls that arise from automation and integration. At FlowMind AI, our commitment is

As businesses increasingly adopt AI technologies, they often face challenges that can disrupt operations and impact the bottom line. A recent incident involving Google’s Gemini AI highlights the importance of understanding these challenges and implementing effective solutions. When Google prepared for a high-profile advertisement during Super Bowl LIX, a misleading statistic generated by its AI

Let’s face it, many organizations are struggling with outdated legacy systems that hamper innovation and efficiency. While there is a growing trend to layer artificial intelligence (AI) on top of these dated infrastructures, this approach often results in superficial improvements rather than substantive change. Just as you cannot simply attach a massive spoiler and a

Early, a pioneer in the realm of generative AI for code quality enhancement, recently announced a significant milestone with the acquisition of $5 million in Seed funding, led by Zeev Ventures. This funding, supported also by Dynamic Loop Capital, positions Early to further develop its AI-powered solution aimed at improving code quality through automated testing.