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Glyphs That Remember the Past: Teaching AI to Read History Without Being Told It

Symbols are easy to digitize and surprisingly hard to respect. A business team sees two product names, two supplier records, two compliance clauses, or two scanned forms that look related. The lazy engineering answer is: “label the matches, label the non-matches, train a contrastive model.” That answer often works. It is also how many embedding systems quietly turn uncertainty into false certainty, then call the result “semantic similarity.” Very tidy. Very confident. Occasionally very wrong. ...

March 10, 2026 · 15 min · Zelina
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Who Was Where When? AI Tries to Remember History

Archive work has a very simple-looking question at its center: who was where, and when? That question looks harmless until a machine has to answer it from a century-old newspaper, after OCR has mangled the spelling, the place names have shifted, the language is not always English, and the text only implies the answer through an event, job title, or institutional affiliation. At that point, “extracting information” becomes less like copying a fact from a sentence and more like making a legally cautious inference from a witness who speaks in fragments. ...

February 20, 2026 · 13 min · Zelina
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Artism, or How AI Learned to Critique Itself

Art is very good at inventing new labels for old habits. A canvas becomes a critique of perception. A broken object becomes an ontology of absence. A projected loop becomes a meditation on archive, memory, and technological mediation. Sometimes this is intellectually serious. Sometimes it is a well-dressed remix. The uncomfortable part is that outsiders are not always bad at telling the difference. Insiders are not always good at it either. ...

December 18, 2025 · 14 min · Zelina