Claude 5 Fable, the first public Mythos-class model, blew past every AI I’ve tried. In side-by-side tests it outperformed other public models by a wide margin and stuck with multi-page specs for hours. It whipped up a sophisticated social-science paper from a single prompt and one piece of feedback, then turned around a ten-page, all-“s” epic rhyming poem. For a more practical demo, I had it spin up several games using only code—no images—like a coin-flip strategy game, a self-aware snake romp, and a descent-into-darkness adventure. Each one began with a vague instruction, followed by light “make it better” tweaks, yet emerged fully playable. The real kicker arrived when I asked Fable to build an isochrone map showing how far you can travel from a city in a set time. Previous models floundered at the research and judgment calls. Fable launched dozens of Claude Sonnet agents to scour schedules—2,200 flights, TGV and Shinkansen timetables, road-speed studies—and then started coding. When I flagged gaps (remote spots like Greenland), it spun up adversarial research agents to nail down ship routes to Pitcairn Island and flight connections to Grise Fjord. In under a few hours it delivered a polished, 1881-style map with customizable cities, visual tweaks, full data sources and methods listed. The most ambitious test was a nine-and-a-half-hour run to build “Concord,” a tool for calibrating human and AI judgments on messy research data. Fable wrote a 19-page design spec, generated production-ready code, and handled dataset integration, calibration algorithms and analytical reporting. I identified a handful of expert-level tweaks, asked for fixes, and it updated the system. The result is a codebase researchers can fork or refine—something the community long needed but never funded. At each step, I played a minimal role: issue a prompt, give light feedback, and the model carried out hundreds of micro-decisions I neither saw nor controlled.