Browsing: Dev

OpenAI’s Function Calling feature allows for the transformation of unstructured data into structured formats, addressing a significant need in data processing. This article explores five powerful, yet often overlooked, applications of GPT functions, demonstrating their potential for data extraction, scoring, categorization, option-selection, and filtering.

Great API design goes beyond initial readability and correctness, focusing instead on how the code using the API will evolve and adapt to future changes and requirements.

Discover the surprising insights gained from attempting to simplify HTML and CSS concepts for a five-year-old, and how this experience re-emphasized the fundamental magic of web development.

This article distills survey findings, outlines action items for leadership, and explores recommendations for agentic AI in the enterprise. Ultimately, data quality is paramount.

React needs to differentiate between class and function components for rendering. This article explores the JavaScript mechanics, including the `new` operator and prototypes, that inform React’s approach, ultimately revealing its simple flag-based solution for component identification.

So long and thanks for all the bits!

The text-decoration-inset CSS property offers a solution to the long-standing issue of text decorations extending beyond character boundaries, leading to visual misalignment. This property allows for precise trimming and native animation of underlines and other text decorations, providing greater control over their appearance and behavior.

Engineers can significantly accelerate their professional development by strategically utilizing one-on-one meetings with their managers. These sessions are crucial for discussing career goals, seeking feedback, and addressing challenges.

Maintaining a minimum of 80% code coverage affects code decisions and not always for the better.

Optimizing complex AI models and systems with numerous configurations presents a significant challenge, especially when evaluating each setup is resource-intensive. Adaptive experimentation addresses this by proposing new configurations based on prior results, enhancing efficiency. Ax 1.0, an open-source platform, automates this process using machine learning and Bayesian optimization, helping researchers and developers find optimal configurations for their systems.