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 the challenges of creating licenses to prohibit AI training on code, delving into the nuances of open source definitions and the ongoing legal debate around fair use. Understand the practical implications and alternative strategies for protecting your projects.
Explore why AI models frequently use em dashes and how this trend is ironically prompting human writers to abandon a classic punctuation mark to avoid being mistaken for AI. Discover the historical context and modern typing methods for em dashes.
Explore the contrasting ethical and practical perceptions of Generative AI in creative arts versus software development, examining arguments around copyright, job displacement, and the nature of output. Uncover why AI art faces intense moral opposition while AI coding assistance sparks different, though equally valid, concerns.
Explore why artificial intelligence predominantly uses static neuron activations instead of more biologically accurate dynamic neurons. This analysis delves into the computational challenges, training instability, and practical trade-offs driving AI's architectural choices.
Discover the strategic reasons ReCAPTCHA doesn't show ads. Learn how its true value lies in AI training data, user fingerprinting, and maintaining conversion rates, rather than direct advertising revenue.
Will Grok win the AI race by training on data from Optimus androids? An analysis of the arguments for unique data versus the overwhelming advantage of massive computational power and logistics.
An analysis of why experienced developers change their minds on foundational debates like static vs. dynamic typing, Rust vs. Go, and tabs vs. spaces, moving from dogma to pragmatism.
The rise of LLMs is forcing a reckoning in the open source community. Explore the divisive impact on developer contributions, licensing debates, and the future of collaborative software development.
Discover why AI models tend to be conservative, from their training data mirroring our world to the deliberate safety and commercial controls placed upon them. Learn how you can even make a local AI more unpredictable.