Local Code: 3,659 Proposals About Data, Design & The Nature of Cities A Book by Nicholas de Monchaux localco.de Local Code’s data-driven layout arranges drawings of 3,659 digitally tailored interventions for vacant public land in San Francisco, Los Angeles, New York City, and Venice, Italy. The natures of these found parcels is as particular as the cities that house them — land under billboards in Los Angeles, dead-end alleys in San Francisco, city-owned vacant lots in New York City, and abandoned islands in the Venetian lagoon — but have in common an unrecognized potential as a social and ecological resource. Names vs. The NothingLocal Code: The Constitution of a City at 42º N Latitude citiesurbanism
AI-driven "Design"? An Article by Jorge Arango jarango.com Like a programming language interpreter, GPT-3 translates the designer’s intent from a language they’re already familiar with (English) to one they need to learn (Figma’s information architecture, as manifested in its UI.) This can be easier for a new/busy designer, much like Python is easier and faster to work with than assembly language. But that’s not “designing” — at least not any more than compiling Python code is “programming.” In both cases, all the system does is translate human intent into a lower level of abstraction. Sure, the process saves time — but the key is getting the intent part right. I’ll be convinced the system is “designing” when it can produce a meaningful output to a directive like “change the product page’s layout to increase conversions.” aidesignintentabstraction