Dedicated writing on artificial intelligence

Clear, honest blogs on new AI, the tech behind it, and AI for science.

No hype, no jargon walls — just carefully written explainers and essays on how AI actually works, where it helps, and where the story began.

Actively updated · latest post Aug 7, 2026

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Everything an LLM can see at once — tokens, what fills the window, why it's limited, how 2026 models compare, and when bigger context is a bad idea.

Dhanji Aug 7, 2026

The full essays behind the field — seventy years of AI, from the 1956 Dartmouth conference to the models of today.

John McCarthy John McCarthy (1927–2011): the scientist who coined 'artificial intelligence,' organized the 1956 Dartmouth conference, and created LISP.
Evolution & Timeline A decade-by-decade history of AI: 1950s foundations, symbolic AI, expert systems, machine learning, deep learning, and modern LLMs.
Benefits Where AI has produced measurable value: automation, medical imaging, large-scale data analysis, adaptive education, and scientific research.
Harms and Risks Documented harms of AI: job displacement, dataset bias, lack of interpretability, surveillance misuse, deepfakes, and overdependence.
Philosophy The philosophy of AI: symbolic versus data-driven intelligence, the limits of today's systems, and whether machines can truly understand.
Conclusion What the record shows: AI has evolved over decades through incremental progress, and its genuine benefits and genuine risks must be weighed together.
AI Topics glossary Plain-language explainers for 100+ of the most-searched AI terms — ChatGPT, LLMs, transformers, AGI, and more.
Blog Dedicated blogs on AI tech, science, and the ideas behind the field.
About this archive The purpose of the archive and the authentic sources it draws on.