Software Engineering

Explore the nuanced reasons behind the public's complex relationship with artificial intelligence, from concerns over corporate overreach to the annoyance of low-quality automated content.

Are AI agents driving real value or just creating expensive busywork? We explore the growing concern over 'token inflation' and offer strategies to ensure LLM usage leads to tangible outcomes rather than just process.

As enterprise AI costs soar, developers are increasingly turning to locally hosted open-source models for coding tasks. Discover the pros, cons, and hardware considerations for running your own LLM architecture.

A software engineer struggling with the UK job market receives advice on refining their technical focus, adopting AI-assisted workflows, and leveraging networking over cold applications. The discussion highlights the shifting expectations for mid-level roles, emphasizing architectural design and team leadership over basic syntax knowledge.

Explore effective strategies for ensuring engineering quality and code ownership in the age of AI, balancing automated productivity with the necessity for human understanding and accountability.

Facing massive AI tool bills, companies are scaling back access. Learn how to manage the transition from an AI-heavy workflow to a leaner, more sustainable development process.

Explore the growing industry skepticism toward AI, how to distinguish meaningful technology from hype, and the importance of maintaining human-centric skills in an increasingly automated world.

Explore the security implications of using AI-powered coding assistants and practical strategies for protecting sensitive codebase data.

Exploring the loss of passion in computing and practical strategies to rediscover the joy of building, tinkering, and maintaining technical agency.

Discover why human review of AI-generated code remains essential for preventing over-engineering, ensuring correctness, and maintaining system health, even with advanced LLMs.