ADRs and decision logs are essential for documenting architectural choices in AI-generated systems. Learn how to use them effectively, how AI is transforming the process, and why skipping documentation risks team chaos.
AI coding is growing fast, but its energy use is hidden. Learn how AI-generated code can waste power, why sustainable coding matters, and what developers can do today to cut emissions without sacrificing performance.
In 2025, LLM governance rules demand strict data tracking, safety testing, and compliance. Federal and state policies now require transparency, bias checks, and human oversight to prevent harm while enabling AI efficiency.
Traffic shaping and A/B testing are essential for safely releasing large language models. Learn how to control user exposure, measure real-world performance, and avoid costly deployment failures with proven LLMOps practices.
Vibe coding accelerates development but struggles with complex distributed systems. Learn when AI-assisted coding helps, when it fails, and how to use it safely without risking production systems.
AI-generated code is leaking secrets at alarming rates. Learn how modern secrets scanning tools detect and block hardcoded credentials in AI-assisted repos before they cause breaches.
Large language models may appear fair but often hide deep implicit biases that standard tests miss. Learn how to detect hidden bias in LLMs using real-world methods and why bigger models aren’t always fairer.
California's AI Transparency Act (AB 853) requires major platforms to label AI-generated media and offer free detection tools. Learn how it works, what it covers, and why accuracy remains a challenge.
Mixed-precision training with FP16 and BF16 cuts LLM training time by up to 70% and reduces memory use by half. Learn how it works, why BF16 beats FP16, and how to implement it today.