Explore why Large Language Models generate plausible-looking but incorrect answers. This post delves into the mechanisms behind LLM "lies" and offers insights into how to best interact with these powerful text generators.
Explore why modern .NET, often overlooked due to historical perceptions, offers powerful "batteries-included" features for startups. Learn how its open-source, cross-platform nature and robust ecosystem address rapid development needs while debunking common myths.
Many teams are finding their 'AI experts' lack fundamental knowledge of how models work or where they run. Explore the reasons behind this competence gap, the risks of misrepresentation, and how to navigate the current AI landscape.
Explore the critical distinction between the Golden Rule and mere reciprocity. Learn how to apply ethical principles effectively, moving beyond an 'eye for an eye' to foster positive conduct.
Discover why curved quotation marks (“ ”) are not a reliable 'tell' for AI-generated content. Learn about their history in typography and why the real issue is often company policy, not the tool itself.