Articles

In this podcast, Sumit Saha shares his insights on staying curious as a developer in the AI hype era. He emphasizes the need to understand concepts rather than relying on shortcuts and provides tips for expanding developer skills.

In this podcast, Luke Ciciliano shares his expertise on building a software consultancy and landing freelance clients. He discusses how AI tools can create more potential small business customers and provides tips on engaging with clients and closing deals.

Chris Coyier shares his thoughts on front-end development tools, the web vs. mobile, and how high-skilled devs can adapt to AI disruption. He also recommends various resources, including freeCodeCamp courses and tutorials.

Quincy Larson interviews Carl Brown, a seasoned developer with over 37 years of experience, about the importance of integration testing, the pros and cons of LLMs and agent code generation tools, and the challenges of ageism in tech.

Robby Russell shares his insights on managing a codebase, reducing dependency on individual developers, and leveraging LLM tools to speed up workflows. This discussion is aimed at advanced developers and engineering managers.

Early-stage businesses often struggle to translate innovative ideas into successful ventures. Digital executives can provide the necessary expertise to drive growth and efficiency.

When AI answers come from your data, testing must ensure they come from the right data — and nothing else.

A PostgreSQL database wraparound incident can be devastating. Discover the importance of having a runbook in place to prevent data loss and ensure business continuity.

AI-citable documentation is essential for large language models like ChatGPT, Perplexity, and Claude to understand and cite developer docs. This article shows you how to build it.

A PostgreSQL authentication error in a Node.js API using Docker Compose led to a surprising discovery: a native PostgreSQL installation on Windows was causing the issue due to a port collision.

AI models can learn from incorrect labels, leading to biased and inaccurate results. Noisy training data can have a significant impact on the performance and reliability of AI systems, making it essential to address these issues.