Researchers have identified significant security vulnerabilities within the ChatGPT ecosystem that potentially allow attackers to access users' accounts on third-party websites, such as GitHub, without their consent. These vulnerabilities were found both in the core ChatGPT platform and its plugins. They expose sensitive data and enable account takeovers through exploitation of the OAuth authentication process and other flaws in plugin frameworks.

This article walks through how an email marketer uses ChatGPT to build an email sequence. First, she uses a prompt to create an outline of key topics, value-adds, and CTAs to focus on in each step of the sequence, along with recommendations for time delays between emails. Then, she has ChatGPT provide potential email copy to use as a base. Lastly, she manually writes and infuses her brand voice into the copy to reflect human empathy when finalizing the email sequence.

This guide explores the role of pricing as a direct lever for revenue in SaaS. It highlights strategies like targeting high gross margins and establishing clear price positioning. The guide examines pricing models from ChatGPT's $20/month flat-rate plan to Stripe's transaction-based pricing and covers tactics such as tiered pricing and the Van Westendorp Price Sensitivity Meter to determine optimal price points. It also recommends continuous experimentation and iteration, sharing examples of how companies like Hubspot adjust pricing based on customer value and willingness to pay.

JavaScript bundles on modern websites are much bigger than they need to be, even for simple functionalities. Popular websites, like ChatGPT and YouTube, have a high amount of client-side JavaScript bloat that doesn't always match the functionality the page offers. Certain websites, like adult websites, prioritize fast loading speeds and have small JavaScript bundles.

The launch of GPT-4o sent ChatGPT's revenue soaring - it pulled in $28 million in net revenue from the App Store and Google Play in July. The app grossed $39.9 million before paying in-app purchase fees. ChatGPT saw an estimated 2 million new paying customers last month. Growth is expected to continue in the months to come.

Designing for AI-first products represents a significant shift in the approach to product development, moving beyond traditional methods to embrace the unique capabilities and challenges posed by artificial intelligence. An AI-first product is fundamentally built around AI technology, meaning that its core functionality relies entirely on AI. Examples include platforms like ChatGPT and Amazon Alexa, where the absence of AI would render the product ineffective. The distinction between AI-first product design and traditional product design is crucial. Traditional design often focuses on enhancing existing products with AI features, while AI-first design starts with the premise that AI is the solution to specific user problems. This shift in perspective necessitates a more collaborative approach, involving not just designers and developers but also data scientists and ethicists to address the complexities of AI technology. AI-first product design is inherently data-dependent, requiring continuous data collection and analysis to adapt and improve user experiences. This contrasts with traditional design, which is typically data-driven but does not rely on real-time data to the same extent. Additionally, the user journey in AI-first products is more complex and dynamic, as these products can adapt to individual user interactions, creating a personalized experience that traditional static designs cannot offer. However, designing for AI-first products comes with its own set of challenges. User trust is paramount, as concerns about privacy, data protection, and ethical implications can hinder adoption. Designers must also grapple with the inherent biases in AI systems, ensuring that their products do not perpetuate harmful stereotypes or make biased decisions. Scalability is another concern, as AI products must maintain performance and usability as they evolve. To navigate these challenges, several guiding principles can be employed. First, a human-centric approach to problem-solving should remain at the forefront, ensuring that the design process focuses on delivering real value to users. Designers should also prioritize user control, balancing the efficiency of AI with the need for users to feel in command of their interactions. Transparency is essential for building trust; users should be informed about how AI operates and how their data is used. Ethical considerations must be integrated into every design decision, with proactive measures taken to identify and mitigate biases. Finally, fostering cross-functional collaboration is vital, as successful AI-first product design requires input from a diverse range of experts. Looking ahead, the integration of AI in UX design is expected to grow, leading to more AI-first products and a redefined design process. While AI will not replace human designers, it will transform their roles and the nature of the products they create. As the field evolves, continuous learning and adaptation will be essential for designers to keep pace with these changes.